Advertisement
# Accelerating End-to-End Data Science Workflows: A Comprehensive Analysis
Author: Dr. Anya Sharma, PhD. Dr. Sharma is a leading expert in data science and machine learning with over 15 years of experience in industry and academia. Her research focuses on optimizing data science pipelines and developing efficient algorithms for large-scale data analysis. She has published numerous peer-reviewed articles on the topic and holds several patents related to workflow automation in data science.
Keywords: accelerating end-to-end data science workflows, data science optimization, machine learning pipelines, workflow automation, data science scalability, big data processing, cloud computing for data science, MLOps, DevOps for data science.
1. Introduction: The Evolving Landscape of Data Science Workflows
The field of data science has experienced explosive growth in recent years, driven by the increasing availability of data and advancements in computing power. However, the process of extracting meaningful insights from this data – the end-to-end data science workflow – often remains a bottleneck. This article delves into the historical context of data science workflows, exploring the challenges and innovations that have shaped the pursuit of accelerating end-to-end data science workflows. We will examine the current state-of-the-art, highlighting key technologies and strategies that are transforming how data scientists work.
2. Historical Context: From Manual Processes to Automation
Initially, data science workflows were largely manual and ad-hoc processes. Data scientists spent significant time on tasks like data cleaning, preprocessing, feature engineering, model selection, and evaluation, often using disparate tools and scripts. This manual approach was time-consuming, error-prone, and difficult to scale. The lack of standardization and reproducibility hindered collaboration and slowed down the entire data science lifecycle.
The emergence of big data further exacerbated these challenges. The sheer volume, velocity, and variety of data required new approaches to data processing and analysis. This led to the development of distributed computing frameworks like Hadoop and Spark, which enabled the parallel processing of large datasets. These advancements were a crucial step towards accelerating end-to-end data science workflows, but they still left much room for improvement in terms of workflow automation and integration.
3. The Rise of Automation and MLOps: Key Drivers of Acceleration
The past decade has witnessed a significant shift towards automation in data science. The concept of MLOps (Machine Learning Operations), analogous to DevOps for software development, has emerged as a powerful paradigm for accelerating end-to-end data science workflows. MLOps focuses on streamlining the entire machine learning lifecycle, from data preparation to model deployment and monitoring. This involves automating repetitive tasks, implementing continuous integration and continuous delivery (CI/CD) pipelines, and establishing robust monitoring and feedback loops.
Several technologies have played a crucial role in this automation:
Cloud Computing: Cloud platforms like AWS, Azure, and GCP offer scalable infrastructure, managed services for data storage and processing, and pre-built machine learning tools that significantly simplify the development and deployment of data science models. This accessibility greatly facilitates accelerating end-to-end data science workflows.
Orchestration Tools: Tools like Airflow, Kubeflow, and Prefect enable the creation of automated workflows that orchestrate the execution of various data science tasks, ensuring reproducibility and scalability.
Model Versioning and Management: Tools like MLflow and DVC allow data scientists to track model versions, experiments, and artifacts, facilitating collaboration and reproducibility.
Automated Machine Learning (AutoML): AutoML platforms automate various stages of the machine learning pipeline, including feature engineering, model selection, and hyperparameter tuning, significantly reducing the time and effort required for model development.
4. Current Relevance: Addressing the Challenges of Speed and Scalability
Despite significant advancements, accelerating end-to-end data science workflows remains a crucial challenge. The ever-increasing volume and complexity of data, coupled with the demand for faster insights, necessitates continuous innovation. Current efforts focus on:
Improved Data Management: Efficient data storage, retrieval, and preprocessing remain critical. Techniques like data virtualization and data lakes are playing an increasingly important role.
Enhanced Model Explainability: As the use of AI expands, understanding and explaining model predictions is crucial. Techniques like SHAP values and LIME are gaining prominence.
Edge Computing: Deploying machine learning models closer to the data source (e.g., on edge devices) can reduce latency and improve real-time performance.
Responsible AI: Ensuring fairness, accountability, and transparency in AI systems is becoming increasingly critical.
5. Conclusion
Accelerating end-to-end data science workflows is a continuous journey, driven by the evolving needs of data-driven organizations. The adoption of MLOps principles, leveraging cloud computing and automation tools, and focusing on efficient data management are key strategies for achieving faster, more scalable, and more reliable data science processes. As the field continues to evolve, we can anticipate further advancements that will further streamline the data science lifecycle and unlock even greater value from data.
FAQs
1. What are the biggest bottlenecks in traditional data science workflows? Traditional workflows often suffer from manual, time-consuming tasks, lack of reproducibility, and difficulty scaling to larger datasets.
2. How does MLOps contribute to accelerating data science workflows? MLOps introduces automation, CI/CD pipelines, and robust monitoring, streamlining the entire machine learning lifecycle.
3. What are some key tools for automating data science workflows? Airflow, Kubeflow, Prefect, MLflow, and DVC are examples of powerful tools used for automation and management.
4. How does cloud computing facilitate faster data science workflows? Cloud platforms provide scalable infrastructure, managed services, and pre-built machine learning tools, reducing overhead and accelerating development.
5. What is the role of AutoML in accelerating workflows? AutoML automates parts of the machine learning pipeline, such as feature engineering and model selection, significantly speeding up model development.
6. What are the challenges in deploying machine learning models in production? Challenges include ensuring model stability, scalability, monitoring performance, and managing model updates.
7. How can data scientists ensure the reproducibility of their results? Using version control, documenting workflows, and utilizing tools that track experiments and models ensures reproducibility.
8. What is the importance of data governance in accelerating workflows? Data governance ensures data quality, consistency, and accessibility, improving the efficiency of the entire workflow.
9. How can organizations measure the success of their efforts to accelerate data science workflows? Organizations can track metrics like time to insight, model deployment frequency, and resource utilization.
Related Articles:
1. "Building Scalable Data Pipelines with Apache Airflow": This article explores the use of Apache Airflow for building and managing complex data pipelines, a critical component of accelerating end-to-end workflows.
2. "MLOps Best Practices for Continuous Delivery of Machine Learning Models": This article focuses on implementing MLOps principles for efficient and reliable deployment of machine learning models.
3. "Optimizing Data Preprocessing for Faster Machine Learning Model Training": This article explores techniques for improving the efficiency of data preprocessing, a crucial step in accelerating the workflow.
4. "A Comparative Analysis of Cloud Platforms for Machine Learning": This article compares different cloud platforms, highlighting their strengths and weaknesses for data science and machine learning workloads.
5. "Automating Hyperparameter Tuning with Bayesian Optimization": This article explores advanced techniques for automating hyperparameter tuning, a time-consuming aspect of model development.
6. "Implementing Model Monitoring and Alerting for Production Machine Learning Systems": This article discusses the importance of monitoring deployed models and setting up alerts for performance degradation.
7. "The Role of Data Version Control in Reproducible Machine Learning": This article highlights the use of data version control (e.g., DVC) for ensuring reproducibility in machine learning projects.
8. "Building a Serverless Data Science Pipeline with AWS Lambda": This article explores the use of serverless computing for building efficient and scalable data science pipelines.
9. "Ethical Considerations in the Development and Deployment of Machine Learning Systems": This article discusses the ethical implications of deploying machine learning models and how to build responsible AI systems.
Publisher: O'Reilly Media. O'Reilly is a well-respected publisher of technical books and online learning content, with a strong reputation in the fields of data science, machine learning, and software engineering. Their publications are often cited as authoritative resources on these topics.
Editor: Dr. David Smith, PhD., a seasoned data scientist with extensive experience in building and managing large-scale data science teams. His expertise in data engineering and MLOps adds significant credibility to the article.
| accelerating end to end data science workflows: Data Mining Yue Xu, Rosalind Wang, Anton Lord, Yee Ling Boo, Richi Nayak, Yanchang Zhao, Graham Williams, 2021-12-08 This book constitutes the refereed proceedings of the 19th Australasian Conference on Data Mining, AusDM 2021, held in Brisbane, Queensland, Australia, in December 2021.* The 16 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in sections on research track and application track. *Due to the COVID-19 pandemic the conference was held online. |
| accelerating end to end data science workflows: Agile Data Science 2.0 Russell Jurney, 2017-06-07 Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track |
| accelerating end to end data science workflows: Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation Kothe Doug, Geist Al, Swaroop Pophale, Hong Liu, Suzanne Parete-Koon, 2023-01-17 This book constitutes the refereed proceedings of the 22nd Smoky Mountains Computational Sciences and Engineering Conference on Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation, SMC 2022, held virtually, during August 23–25, 2022. The 24 full papers included in this book were carefully reviewed and selected from 74 submissions. They were organized in topical sections as follows: foundational methods enabling science in an integrated ecosystem; science and engineering applications requiring and motivating an integrated ecosystem; systems and software advances enabling an integrated science and engineering ecosystem; deploying advanced technologies for an integrated science and engineering ecosystem; and scientific data challenges. |
| accelerating end to end data science workflows: Learning Deep Learning Magnus Ekman, 2021-07-19 NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals. -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us. -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| accelerating end to end data science workflows: Cloud Computing and Services Science Ivan Ivanov, Marten van Sinderen, Boris Shishkov, 2012-04-17 The Cloud Computing and Services Science book comprises a collection of the best papers presented at the International Conference on Cloud Computing and Services Science (CLOSER), which was held in The Netherlands in May 2011. In netting papers from the conference researchers and experts from all over the world explore a wide-ranging variety of the emerging Cloud Computing platforms, models, applications and enabling technologies. Further, in several papers the authors exemplify essential links to Services Science as service development abstraction, service innovation, and service engineering, acknowledging the service-orientation in most current IT-driven structures in the Cloud. The Cloud Computing and Services Science book is organized around important dimensions of technology trends in the domain of cloud computing in relation to a broad scientific understanding of modern services emerging from services science. The papers of this book are inspired by scholarly and practical work on the latest advances related to cloud infrastructure, operations, security, services, and management through the global network. This book includes several features that will be helpful, interesting, and inspirational to students, researchers as well as practitioners. Professionals and decision makers working in this field will also benefit from this book |
| accelerating end to end data science workflows: A Research Agenda for Spatial Analysis Levi John Wolf, Richard Harris, Alison Heppenstall, 2024-05-02 This Research Agenda explores the future of spatial analysis, and how the field informs and challenges the policy landscape. A wide range of contributors from different intellectual communities address the problem of causality in geographic analysis, arguing that diversity is crucial for the future success of the discipline. This title contains one or more Open Access chapters. |
| accelerating end to end data science workflows: Parallel Computing: Accelerating Computational Science and Engineering (CSE) M. Bader, A. Bode, H.-J. Bungartz, 2014-03-31 Parallel computing has been the enabling technology of high-end machines for many years. Now, it has finally become the ubiquitous key to the efficient use of any kind of multi-processor computer architecture, from smart phones, tablets, embedded systems and cloud computing up to exascale computers. _x000D_ This book presents the proceedings of ParCo2013 – the latest edition of the biennial International Conference on Parallel Computing – held from 10 to 13 September 2013, in Garching, Germany. The conference focused on several key parallel computing areas. Themes included parallel programming models for multi- and manycore CPUs, GPUs, FPGAs and heterogeneous platforms, the performance engineering processes that must be adapted to efficiently use these new and innovative platforms, novel numerical algorithms and approaches to large-scale simulations of problems in science and engineering._x000D_ The conference programme also included twelve mini-symposia (including an industry session and a special PhD Symposium), which comprehensively represented and intensified the discussion of current hot topics in high performance and parallel computing. These special sessions covered large-scale supercomputing, novel challenges arising from parallel architectures (multi-/manycore, heterogeneous platforms, FPGAs), multi-level algorithms as well as multi-scale, multi-physics and multi-dimensional problems._x000D_ It is clear that parallel computing – including the processing of large data sets (“Big Data”) – will remain a persistent driver of research in all fields of innovative computing, which makes this book relevant to all those with an interest in this field. |
| accelerating end to end data science workflows: Building Cloud Data Platforms Solutions Anouar BEN ZAHRA, Building Cloud Data Platforms Solutions: An End-to-End Guide for Designing, Implementing, and Managing Robust Data Solutions in the Cloud comprehensively covers a wide range of topics related to building data platforms in the cloud. This book provides a deep exploration of the essential concepts, strategies, and best practices involved in designing, implementing, and managing end-to-end data solutions. The book begins by introducing the fundamental principles and benefits of cloud computing, with a specific focus on its impact on data management and analytics. It covers various cloud services and architectures, enabling readers to understand the foundation upon which cloud data platforms are built. Next, the book dives into key considerations for building cloud data solutions, aligning business needs with cloud data strategies, and ensuring scalability, security, and compliance. It explores the process of data ingestion, discussing various techniques for acquiring and ingesting data from different sources into the cloud platform. The book then delves into data storage and management in the cloud. It covers different storage options, such as data lakes and data warehouses, and discusses strategies for organizing and optimizing data storage to facilitate efficient data processing and analytics. It also addresses data governance, data quality, and data integration techniques to ensure data integrity and consistency across the platform. A significant portion of the book is dedicated to data processing and analytics in the cloud. It explores modern data processing frameworks and technologies, such as Apache Spark and serverless computing, and provides practical guidance on implementing scalable and efficient data processing pipelines. The book also covers advanced analytics techniques, including machine learning and AI, and demonstrates how these can be integrated into the data platform to unlock valuable insights. Furthermore, the book addresses an aspects of data platform monitoring, security, and performance optimization. It explores techniques for monitoring data pipelines, ensuring data security, and optimizing performance to meet the demands of real-time data processing and analytics. Throughout the book, real-world examples, case studies, and best practices are provided to illustrate the concepts discussed. This helps readers apply the knowledge gained to their own data platform projects. |
| accelerating end to end data science workflows: Microsoft Certified: Azure Data Scientist Associate (DP-100) , Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| accelerating end to end data science workflows: Getting Started with DuckDB Simon Aubury, Ned Letcher, 2024-06-24 Analyze and transform data efficiently with DuckDB, a versatile, modern, in-process SQL database Key Features Use DuckDB to rapidly load, transform, and query data across a range of sources and formats Gain practical experience using SQL, Python, and R to effectively analyze data Learn how open source tools and cloud services in the broader data ecosystem complement DuckDB’s versatile capabilities Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDuckDB is a fast in-process analytical database. Getting Started with DuckDB offers a practical overview of its usage. You'll learn to load, transform, and query various data formats, including CSV, JSON, and Parquet. The book covers DuckDB's optimizations, SQL enhancements, and extensions for specialized applications. Working with examples in SQL, Python, and R, you'll explore analyzing public datasets and discover tools enhancing DuckDB workflows. This guide suits both experienced and new data practitioners, quickly equipping you to apply DuckDB's capabilities in analytical projects. You'll gain proficiency in using DuckDB for diverse tasks, enabling effective integration into your data workflows.What you will learn Understand the properties and applications of a columnar in-process database Use SQL to load, transform, and query a range of data formats Discover DuckDB's rich extensions and learn how to apply them Use nested data types to model semi-structured data and extract and model JSON data Integrate DuckDB into your Python and R analytical workflows Effectively leverage DuckDB's convenient SQL enhancements Explore the wider ecosystem and pathways for building DuckDB-powered data applications Who this book is for If you’re interested in expanding your analytical toolkit, this book is for you. It will be particularly valuable for data analysts wanting to rapidly explore and query complex data, data and software engineers looking for a lean and versatile data processing tool, along with data scientists needing a scalable data manipulation library that integrates seamlessly with Python and R. You will get the most from this book if you have some familiarity with SQL and foundational database concepts, as well as exposure to a programming language such as Python or R. |
| accelerating end to end data science workflows: Artificial Intelligence in Science Challenges, Opportunities and the Future of Research OECD, 2023-06-26 The rapid advances of artificial intelligence (AI) in recent years have led to numerous creative applications in science. Accelerating the productivity of science could be the most economically and socially valuable of all the uses of AI. |
| accelerating end to end data science workflows: Distributed Machine Learning Patterns Yuan Tang, 2022-04-26 Practical patterns for scaling machine learning from your laptop to a distributed cluster. Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In Distributed Machine Learning Patterns, you’ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. |
| accelerating end to end data science workflows: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| accelerating end to end data science workflows: Towards Interoperable Research Infrastructures for Environmental and Earth Sciences Zhiming Zhao, Margareta Hellström, 2020-07-24 This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions. |
| accelerating end to end data science workflows: Hands-On GPU Computing with Python Avimanyu Bandyopadhyay, 2019-05-14 Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate Key FeaturesUnderstand effective synchronization strategies for faster processing using GPUsWrite parallel processing scripts with PyCuda and PyOpenCLLearn to use the CUDA libraries like CuDNN for deep learning on GPUsBook Description GPUs are proving to be excellent general purpose-parallel computing solutions for high performance tasks such as deep learning and scientific computing. This book will be your guide to getting started with GPU computing. It will start with introducing GPU computing and explain the architecture and programming models for GPUs. You will learn, by example, how to perform GPU programming with Python, and you’ll look at using integrations such as PyCUDA, PyOpenCL, CuPy and Numba with Anaconda for various tasks such as machine learning and data mining. Going further, you will get to grips with GPU work flows, management, and deployment using modern containerization solutions. Toward the end of the book, you will get familiar with the principles of distributed computing for training machine learning models and enhancing efficiency and performance. By the end of this book, you will be able to set up a GPU ecosystem for running complex applications and data models that demand great processing capabilities, and be able to efficiently manage memory to compute your application effectively and quickly. What you will learnUtilize Python libraries and frameworks for GPU accelerationSet up a GPU-enabled programmable machine learning environment on your system with AnacondaDeploy your machine learning system on cloud containers with illustrated examplesExplore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL and ROCm.Perform data mining tasks with machine learning models on GPUsExtend your knowledge of GPU computing in scientific applicationsWho this book is for Data Scientist, Machine Learning enthusiasts and professionals who wants to get started with GPU computation and perform the complex tasks with low-latency. Intermediate knowledge of Python programming is assumed. |
| accelerating end to end data science workflows: Accelerating Software Quality Eran Kinsbruner, 2020-08-10 The book Accelerating Software Quality: Machine Learning and Artificial Intelligence in the Age of DevOps is a complete asset for software developers, testers, and managers that are on their journey to a more mature DevOps workflow, and struggle with better automation and data-driven decision making. DevOps is a mature process across the entire market, however, with existing Non-AI/ML technologies and models, it comes short in expediting release cycle, identifying productivity gaps and addressing them. This book, that was implemented by myself with the help of leaders from the DevOps and test automation space, is covering topics from basic introduction to AI and ML in software development and testing, implications of AI and ML on existing apps, processes, and tools, practical tips in applying commercial and open-source AI/ML tools within existing tool chain, chat-bots testing, visual based testing using AI, automated security scanning for vulnerabilities, automated code reviews, API testing and management using AI/ML, reducing effort and time through test impact analysis (TIA), robotic process automation (RPA), AIOps for smarter code deployments and production defects prevention, and many more.When properly leveraging such tools, DevOps teams can benefit from greater code quality and functional and non-functional test automation coverage. This increases their release cycle velocity, reduces noise and software waste, and enhances their app quality.The book is divided into 3 main sections: *Section 1 covers the fundamentals of AI and ML in software development and testing. It includes introductions, definitions, 101 for testing AI-Based applications, classifications of AI/ML and defects that are tied to AI/ML, and more.*Section 2 focuses on practical advises and recommendations for using AI/ML based solutions within software development activities. This section includes topics like visual AI test automation, AI in test management, testing conversational AI applications, RPA benefits, API testing and much more.*Section 3 covers the more advanced and future-looking angles of AI and ML with projections and unique use cases. Among the topics in this section are AI and ML in logs observability, AIOps benefits to an entire DevOps teams, how to maintain AI/ML test automation, Test impact analysis with AI, and more.The book is packed with many proven best practices, real life examples, and many other open source and commercial solution recommendations that are set to shape the future of DevOps together with ML/AI |
| accelerating end to end data science workflows: Handbook of Materials Modeling Sidney Yip, 2007-11-17 The first reference of its kind in the rapidly emerging field of computational approachs to materials research, this is a compendium of perspective-providing and topical articles written to inform students and non-specialists of the current status and capabilities of modelling and simulation. From the standpoint of methodology, the development follows a multiscale approach with emphasis on electronic-structure, atomistic, and mesoscale methods, as well as mathematical analysis and rate processes. Basic models are treated across traditional disciplines, not only in the discussion of methods but also in chapters on crystal defects, microstructure, fluids, polymers and soft matter. Written by authors who are actively participating in the current development, this collection of 150 articles has the breadth and depth to be a major contributor toward defining the field of computational materials. In addition, there are 40 commentaries by highly respected researchers, presenting various views that should interest the future generations of the community. Subject Editors: Martin Bazant, MIT; Bruce Boghosian, Tufts University; Richard Catlow, Royal Institution; Long-Qing Chen, Pennsylvania State University; William Curtin, Brown University; Tomas Diaz de la Rubia, Lawrence Livermore National Laboratory; Nicolas Hadjiconstantinou, MIT; Mark F. Horstemeyer, Mississippi State University; Efthimios Kaxiras, Harvard University; L. Mahadevan, Harvard University; Dimitrios Maroudas, University of Massachusetts; Nicola Marzari, MIT; Horia Metiu, University of California Santa Barbara; Gregory C. Rutledge, MIT; David J. Srolovitz, Princeton University; Bernhardt L. Trout, MIT; Dieter Wolf, Argonne National Laboratory. |
| accelerating end to end data science workflows: Practical Machine Learning with Rust Joydeep Bhattacharjee, 2019-12-10 Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will Learn Write machine learning algorithms in RustUse Rust libraries for different tasks in machine learningCreate concise Rust packages for your machine learning applicationsImplement NLP and computer vision in RustDeploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust. |
| accelerating end to end data science workflows: Data Pipelines with Apache Airflow Bas P. Harenslak, Julian de Ruiter, 2021-04-27 This book teaches you how to build and maintain effective data pipelines. Youll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. -- |
| accelerating end to end data science workflows: Digital Simplified Raj Vattikuti, 2022-12-01 As a technologist, entrepreneur, and philanthropist, Raj Vattikuti has the ideal background to outline the steps of creating a Digital Strategy. Ram Charan is one of the world's most influential consultants who brings deep business insight and understanding of digital business. Together Raj and Ram explain the benefits and pitfalls of various approaches and why standing still means failure. This book explains how a digital business thinks, operates with agility, develops deeper customer relationships, and appropriately uses technology. It also emphasizes that developing a Digital Strategy is an ongoing process to sustain a competitive advantage and provides a template to help business compete in a digital economy. This book offers a practical perspective from decades of partnering with various businesses across many sectors and outlines how to create value for your customers and business. Jacques Nasser AC Raj Vattikuti and Ram Charan have seen what so many others have missed- that real digital transformation starts and ends with the business. The central lessons of their book are what every leader needs to hear: Give digital ownership to the business. Take an agile, iterative approach to investment. Design an innovation process based on experimentation. Push for speed and build digital products in weeks, not years. Shift the culture to empower employees, collaborate across silos, and focus on outcomes. This is how digital transformation delivers lasting growth. If you are leading a legacy business today, you cannot afford anything less! David L. Rogers, global bestselling author of The Digital Transformation Playbook This book is a game changer: no longer will the IT department be seen as disconnected from digital imperatives. Data ultimately should determine the direction of business strategy, capital allocation, and how to assess competitive threats and opportunities. Raj and Ram present the business case for driving digital solutions through innovative IT platforms which keep the plane afloat while installing a new digital engine. Dennis Carey, Vice Chairman Korn Ferry, Founder The Prium and The CEO-Academy |
| accelerating end to end data science workflows: The GENI Book Rick McGeer, Mark Berman, Chip Elliott, Robert Ricci, 2016-08-31 This book, edited by four of the leaders of the National Science Foundation’s Global Environment and Network Innovations (GENI) project, gives the reader a tour of the history, architecture, future, and applications of GENI. Built over the past decade by hundreds of leading computer scientists and engineers, GENI is a nationwide network used daily by thousands of computer scientists to explore the next Cloud and Internet and the applications and services they enable, which will transform our communities and our lives. Since by design it runs on existing computing and networking equipment and over the standard commodity Internet, it is poised for explosive growth and transformational impact over the next five years. Over 70 of the builders of GENI have contributed to present its development, architecture, and implementation, both as a standalone US project and as a federated peer with similar projects worldwide, forming the core of a worldwide network. Applications and services enabled by GENI, from smarter cities to intensive collaboration to immersive education, are discussed. The book also explores the concepts and technologies that transform the Internet from a shared transport network to a collection of “slices” -- private, on-the-fly application-specific nationwide networks with guarantees of privacy and responsiveness. The reader will learn the motivation for building GENI and the experience of its precursor infrastructures, the architecture and implementation of the GENI infrastructure, its deployment across the United States and worldwide, the new network applications and services enabled by and running on the GENI infrastructure, and its international collaborations and extensions. This book is useful for academics in the networking and distributed systems areas, Chief Information Officers in the academic, private, and government sectors, and network and information architects. |
| accelerating end to end data science workflows: Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI Jeffrey Nichols, Becky Verastegui, Arthur ‘Barney’ Maccabe, Oscar Hernandez, Suzanne Parete-Koon, Theresa Ahearn, 2020-12-22 This book constitutes the revised selected papers of the 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, held in Oak Ridge, TN, USA*, in August 2020. The 36 full papers and 1 short paper presented were carefully reviewed and selected from a total of 94 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; system software: data infrastructure and life cycle; experimental/observational applications: use cases that drive requirements for AI and HPC convergence; deploying computation: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic. |
| accelerating end to end data science workflows: Workflows for e-Science Ian J. Taylor, Ewa Deelman, Dennis B. Gannon, Matthew Shields, 2007-12-31 This is a timely book presenting an overview of the current state-of-the-art within established projects, presenting many different aspects of workflow from users to tool builders. It provides an overview of active research, from a number of different perspectives. It includes theoretical aspects of workflow and deals with workflow for e-Science as opposed to e-Commerce. The topics covered will be of interest to a wide range of practitioners. |
| accelerating end to end data science workflows: Provenance and Annotation of Data and Processes Boris Glavic, Vanessa Braganholo, David Koop, 2021-07-08 This book constitutes the proceedings of the 8th and 9th International Provenance and Annotation Workshop, IPAW 2020 and IPAW 2021 which were held as part of ProvenanceWeek in 2020 and 2021. Due to the COVID-19 pandemic, PropvenanceWeek 2020 was held as a 1-day virtual event with brief teaser talks on June 22, 2020. In 2021, the conference was held virtually during July 19-22, 2021. The 11 full papers and 12 posters and system demonstrations included in these proceedings were carefully reviewed and selected from a total of 31 submissions. They were organized in the following topical sections: provenance capture and representation; security; provenance types, inference, queries and summarization; reliability and trustworthiness; joint IPAW/TaPP poster and demonstration session. |
| accelerating end to end data science workflows: Deep Learning with Azure Mathew Salvaris, Danielle Dean, Wee Hyong Tok, 2018-08-24 Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful. |
| accelerating end to end data science workflows: IBM Power Systems Enterprise AI Solutions Scott Vetter, Glen Corneau, Andrew Laidlaw, Marcos Quezada, IBM Redbooks, 2019-09-25 This IBM® Redpaper publication helps the line of business (LOB), data science, and information technology (IT) teams develop an information architecture (IA) for their enterprise artificial intelligence (AI) environment. It describes the challenges that are faced by the three roles when creating and deploying enterprise AI solutions, and how they can collaborate for best results. This publication also highlights the capabilities of the IBM Cognitive Systems and AI solutions: IBM Watson® Machine Learning Community Edition IBM Watson Machine Learning Accelerator (WMLA) IBM PowerAI Vision IBM Watson Machine Learning IBM Watson Studio Local IBM Video Analytics H2O Driverless AI IBM Spectrum® Scale IBM Spectrum Discover This publication examines the challenges through five different use case examples: Artificial vision Natural language processing (NLP) Planning for the future Machine learning (ML) AI teaming and collaboration This publication targets readers from LOBs, data science teams, and IT departments, and anyone that is interested in understanding how to build an IA to support enterprise AI development and deployment. |
| accelerating end to end data science workflows: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| accelerating end to end data science workflows: Euro-Par 2016: Parallel Processing Pierre-François Dutot, Denis Trystram, 2016-08-10 This book constitutes the refereed proceedings of the 22nd International Conference on Parallel and Distributed Computing, Euro-Par 2016, held in Grenoble, France, in August 2016. The 47 revised full papers presented together with 2 invited papers and one industrial paper were carefully reviewed and selected from 176 submissions. The papers are organized in 12 topical sections: Support Tools and Environments; Performance and Power Modeling, Prediction and Evaluation; Scheduling and Load Balancing; High Performance Architectures and Compilers; Parallel and Distributed Data Management and Analytics; Cluster and Cloud Computing; Distributed Systems and Algorithms; Parallel and Distributed Programming, Interfaces, Languages; Multicore and Manycore Parallelism; Theory and Algorithms for Parallel Computation and Networking; Parallel Numerical Methods and Applications; Accelerator Computing. |
| accelerating end to end data science workflows: Accelerate Nicole Forsgren, PhD, Jez Humble, Gene Kim, 2018-03-27 Winner of the Shingo Publication Award Accelerate your organization to win in the marketplace. How can we apply technology to drive business value? For years, we've been told that the performance of software delivery teams doesn't matter―that it can't provide a competitive advantage to our companies. Through four years of groundbreaking research to include data collected from the State of DevOps reports conducted with Puppet, Dr. Nicole Forsgren, Jez Humble, and Gene Kim set out to find a way to measure software delivery performance―and what drives it―using rigorous statistical methods. This book presents both the findings and the science behind that research, making the information accessible for readers to apply in their own organizations. Readers will discover how to measure the performance of their teams, and what capabilities they should invest in to drive higher performance. This book is ideal for management at every level. |
| accelerating end to end data science workflows: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| accelerating end to end data science workflows: IBM Cloud Pak for Data Hemanth Manda, Sriram Srinivasan, Deepak Rangarao, 2021-11-24 Build end-to-end AI solutions with IBM Cloud Pak for Data to operationalize AI on a secure platform based on cloud-native reliability, cost-effective multitenancy, and efficient resource management Key FeaturesExplore data virtualization by accessing data in real time without moving itUnify the data and AI experience with the integrated end-to-end platformExplore the AI life cycle and learn to build, experiment, and operationalize trusted AI at scaleBook Description Cloud Pak for Data is IBM's modern data and AI platform that includes strategic offerings from its data and AI portfolio delivered in a cloud-native fashion with the flexibility of deployment on any cloud. The platform offers a unique approach to addressing modern challenges with an integrated mix of proprietary, open-source, and third-party services. You'll begin by getting to grips with key concepts in modern data management and artificial intelligence (AI), reviewing real-life use cases, and developing an appreciation of the AI Ladder principle. Once you've gotten to grips with the basics, you will explore how Cloud Pak for Data helps in the elegant implementation of the AI Ladder practice to collect, organize, analyze, and infuse data and trustworthy AI across your business. As you advance, you'll discover the capabilities of the platform and extension services, including how they are packaged and priced. With the help of examples present throughout the book, you will gain a deep understanding of the platform, from its rich capabilities and technical architecture to its ecosystem and key go-to-market aspects. By the end of this IBM book, you'll be able to apply IBM Cloud Pak for Data's prescriptive practices and leverage its capabilities to build a trusted data foundation and accelerate AI adoption in your enterprise. What you will learnUnderstand the importance of digital transformations and the role of data and AI platformsGet to grips with data architecture and its relevance in driving AI adoption using IBM's AI LadderUnderstand Cloud Pak for Data, its value proposition, capabilities, and unique differentiatorsDelve into the pricing, packaging, key use cases, and competitors of Cloud Pak for DataUse the Cloud Pak for Data ecosystem with premium IBM and third-party servicesDiscover IBM's vibrant ecosystem of proprietary, open-source, and third-party offerings from over 35 ISVsWho this book is for This book is for data scientists, data stewards, developers, and data-focused business executives interested in learning about IBM's Cloud Pak for Data. Knowledge of technical concepts related to data science and familiarity with data analytics and AI initiatives at various levels of maturity are required to make the most of this book. |
| accelerating end to end data science workflows: Machine Learning with SAP Laboni Bhowmik, Avijit Dhar, Ranajay Mukherjee, 2020 Work smarter with machine learning! Begin with core machine learning concepts--types of learning, algorithms, data preparation, and more. Then use SAP Data Intelligence, SAP HANA, and other technologies to create your own machine learning applications. Master the SAP HANA Predictive Analysis Library (PAL) and machine learning functional and business services to train and deploy models. Finally, see machine learning in action in industries from manufacturing to banking. a. Foundation Build your understanding of probability concepts and algorithms that drive machine learning. See how linear regression, classification, and cluster analysis algorithms work, before plugging them into your very own machine learning app! b. Development Follow step-by-step instructions to gather and prepare data, create machine learning models, train and fine-tune models, and deploy your final app, all using SAP HANA and SAP Data Intelligence. c. Platforms Use built-in SAP HANA libraries to create applications that consume machine learning algorithms or integrate with the R language for additional statistical capabilities. Work with the SAP Leonardo functional services to customize and embed pre-trained models into applications or bring your own model with the help of Google TensorFlow. 1) Development 2) Retraining 3) Implementation 4) SAP Data Intelligence 5) SAP HANA predictive analysis library 6) SAP HANA extended machine learning library 7) SAP HANA automated predictive library 8) Google TensorFlow 9) Embedded machine learning 10) SAP Conversational AI 11) SAP Analytics Cloud Smart Predict |
| accelerating end to end data science workflows: A Platform for Biomedical Discovery and Data-powered Health National Library of Medicine (U.S.). Board of Regents, 2018 |
| accelerating end to end data science workflows: Artificial Intelligence in Banking Introbooks, 2020-04-07 In these highly competitive times and with so many technological advancements, it is impossible for any industry to remain isolated and untouched by innovations. In this era of digital economy, the banking sector cannot exist and operate without the various digital tools offered by the ever new innovations happening in the field of Artificial Intelligence (AI) and its sub-set technologies. New technologies have enabled incredible progression in the finance industry. Artificial Intelligence (AI) and Machine Learning (ML) have provided the investors and customers with more innovative tools, new types of financial products and a new potential for growth.According to Cathy Bessant (the Chief Operations and Technology Officer, Bank of America), AI is not just a technology discussion. It is also a discussion about data and how it is used and protected. She says, In a world focused on using AI in new ways, we're focused on using it wisely and responsibly. |
| accelerating end to end data science workflows: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| accelerating end to end data science workflows: Introduction to Data Science and Machine Learning Keshav Sud, Pakize Erdogmus, Seifedine Kadry, 2020-03-25 Introduction to Data Science and Machine Learning has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish. This book is divided into four sections: the first section contains an introduction to the book, the second covers the field of data science, software development, and open-source based embedded hardware; the third section covers algorithms that are the decision engines for data science applications; and the final section brings together the concepts shared in the first three sections and provides several examples of data science applications. |
| accelerating end to end data science workflows: Kubeflow Operations Guide Josh Patterson, Michael Katzenellenbogen, Austin Harris, 2020-12-04 Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable. Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft. Dive into Kubeflow architecture and learn best practices for using the platform Understand the process of planning your Kubeflow deployment Install Kubeflow on an existing on-premises Kubernetes cluster Deploy Kubeflow on Google Cloud Platform step-by-step from the command line Use the managed Amazon Elastic Kubernetes Service (EKS) to deploy Kubeflow on AWS Deploy and manage Kubeflow across a network of Azure cloud data centers around the world Use KFServing to develop and deploy machine learning models |
| accelerating end to end data science workflows: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience. |
| accelerating end to end data science workflows: Tools and Techniques for High Performance Computing Guido Juckeland, Sunita Chandrasekaran, 2020-03-25 This book constitutes the refereed proceedings of 3 workshops co-located with International Conference for High Performance Computing, Networking, Storage, and Analysis, SC19, held in Denver, CO, USA, in November 2019. The 12 full papers presented in this proceedings feature the outcome of the 6th Annual Workshop on HPC User Support Tools, HUST 2019, International Workshop on Software Engineering for HPC-Enabled Research, SE-HER 2019, and Third Workshop on Interactive High-Performance Computing, WIHPC 2019. |
| accelerating end to end data science workflows: Data Accelerator for AI and Analytics Simon Lorenz, Gero Schmidt, TJ Harris, Mike Knieriemen, Nils Haustein, Abhishek Dave, Venkateswara Puvvada, Christof Westhues, IBM Redbooks, 2021-01-20 This IBM® Redpaper publication focuses on data orchestration in enterprise data pipelines. It provides details about data orchestration and how to address typical challenges that customers face when dealing with large and ever-growing amounts of data for data analytics. While the amount of data increases steadily, artificial intelligence (AI) workloads must speed up to deliver insights and business value in a timely manner. This paper provides a solution that addresses these needs: Data Accelerator for AI and Analytics (DAAA). A proof of concept (PoC) is described in detail. This paper focuses on the functions that are provided by the Data Accelerator for AI and Analytics solution, which simplifies the daily work of data scientists and system administrators. This solution helps increase the efficiency of storage systems and data processing to obtain results faster while eliminating unnecessary data copies and associated data management. |
ACCELERATING Definition & Meaning - Merriam-Webster
The meaning of ACCELERATING is increasing in speed or rate of occurrence. How to use accelerating in a sentence.
ACCELERATING | English meaning - Cambridge Dictionary
at an accelerating pace Since the crash, the value of the currency has been falling at an accelerating pace. at an accelerating rate Arctic Ocean ice is shrinking at an accelerating rate. …
ACCELERATE Definition & Meaning - Merriam-Webster
The meaning of ACCELERATE is to move faster : to gain speed. How to use accelerate in a sentence.
Accelerating - definition of accelerating by The Free Dictionary
To increase the speed of: accelerated the engine. See Synonyms at speed. b. Physics To change the velocity of. 2. To cause to occur sooner than expected: accelerated his retirement by a …
ACCELERATE Definition & Meaning - Dictionary.com
Accelerate definition: to cause faster or greater activity, development, progress, advancement, etc., in.. See examples of ACCELERATE used in a sentence.
Acceleration | Definition, Facts, & Units | Britannica
Jun 4, 2025 · acceleration, rate at which velocity changes with time, in terms of both speed and direction. A point or an object moving in a straight line is accelerated if it speeds up or slows …
ACCELERATE | definition in the Cambridge English Dictionary
accelerate The vehicle accelerated around the turn. If a person or object accelerates, he, she, or it goes faster. Inflation is likely to accelerate this year, adding further upward pressure on …
ACCELERATE definition and meaning | Collins English Dictionary
Growth will accelerate to 2.9% next year. [VERB] The government is to accelerate its privatisation programme. [VERB noun] When a moving vehicle accelerates, it goes faster and faster. …
Accelerate - Definition, Meaning & Synonyms - Vocabulary.com
Accelerate means to speed up. A car accelerates when you step on the gas. You can accelerate the process of getting a visa if you happen to know someone who works in the consulate.
Acceleration Academies: Flexible and Supportive Approach to …
Our unique model gives students the flexibility and support they need to earn their high school diploma. Discover a different high school experience today.
ACCELERATING Definition & Meaning - Merriam-Webster
The meaning of ACCELERATING is increasing in speed or rate of occurrence. How to use accelerating in a sentence.
ACCELERATING | English meaning - Cambridge Dictionary
at an accelerating pace Since the crash, the value of the currency has been falling at an accelerating pace. at an accelerating rate Arctic Ocean ice is shrinking at an accelerating rate. …
ACCELERATE Definition & Meaning - Merriam-Webster
The meaning of ACCELERATE is to move faster : to gain speed. How to use accelerate in a sentence.
Accelerating - definition of accelerating by The Free Dictionary
To increase the speed of: accelerated the engine. See Synonyms at speed. b. Physics To change the velocity of. 2. To cause to occur sooner than expected: accelerated his retirement by a …
ACCELERATE Definition & Meaning - Dictionary.com
Accelerate definition: to cause faster or greater activity, development, progress, advancement, etc., in.. See examples of ACCELERATE used in a sentence.
Acceleration | Definition, Facts, & Units | Britannica
Jun 4, 2025 · acceleration, rate at which velocity changes with time, in terms of both speed and direction. A point or an object moving in a straight line is accelerated if it speeds up or slows …
ACCELERATE | definition in the Cambridge English Dictionary
accelerate The vehicle accelerated around the turn. If a person or object accelerates, he, she, or it goes faster. Inflation is likely to accelerate this year, adding further upward pressure on …
ACCELERATE definition and meaning | Collins English Dictionary
Growth will accelerate to 2.9% next year. [VERB] The government is to accelerate its privatisation programme. [VERB noun] When a moving vehicle accelerates, it goes faster and faster. …
Accelerate - Definition, Meaning & Synonyms - Vocabulary.com
Accelerate means to speed up. A car accelerates when you step on the gas. You can accelerate the process of getting a visa if you happen to know someone who works in the consulate.
Acceleration Academies: Flexible and Supportive Approach to …
Our unique model gives students the flexibility and support they need to earn their high school diploma. Discover a different high school experience today.
