Practicing Trustworthy Machine Learning
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Practicing Trustworthy Machine Learning
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Author : Yada Pruksachatkun
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-01-03
Practicing Trustworthy Machine Learning written by Yada Pruksachatkun and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-03 with Business & Economics categories.
With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention
Trustworthy Machine Learning For Healthcare
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Author : Hao Chen
language : en
Publisher: Springer Nature
Release Date : 2023-07-30
Trustworthy Machine Learning For Healthcare written by Hao Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-30 with Computers categories.
This book constitutes the proceedings of First International Workshop, TML4H 2023, held virtually, in May 2023. The 16 full papers included in this volume were carefully reviewed and selected from 30 submissions. The goal of this workshop is to bring together experts from academia, clinic, and industry with an insightful vision of promoting trustworthy machine learning in healthcare in terms of scalability, accountability, and explainability.
Machine Learning And Principles And Practice Of Knowledge Discovery In Databases
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Author : Michael Kamp
language : en
Publisher: Springer Nature
Release Date : 2022-02-17
Machine Learning And Principles And Practice Of Knowledge Discovery In Databases written by Michael Kamp and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-17 with Computers categories.
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online. The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops:Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021)Workshop on Parallel, Distributed and Federated Learning (PDFL 2021)Workshop on Graph Embedding and Mining (GEM 2021)Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021)Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021)Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021)Workshop on Bias and Fairness in AI (BIAS 2021)Workshop on Workshop on Active Inference (IWAI 2021)Workshop on Machine Learning for Cybersecurity (MLCS 2021)Workshop on Machine Learning in Software Engineering (MLiSE 2021)Workshop on MIning Data for financial applications (MIDAS 2021)Sixth Workshop on Data Science for Social Good (SoGood 2021)Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021)Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020)Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021)
Building Recommendation Systems In Python And Jax
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Author : Bryan Bischof Ph.D
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-12-04
Building Recommendation Systems In Python And Jax written by Bryan Bischof Ph.D and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-04 with Computers categories.
Implementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available. Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems (RecSys) provide the way. In this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed. This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, and Weights & Biases. You'll learn: The data essential for building a RecSys How to frame your data and business as a RecSys problem Ways to evaluate models appropriate for your system Methods to implement, train, test, and deploy the model you choose Metrics you need to track to ensure your system is working as planned How to improve your system as you learn more about your users, products, and business case
Ai And Machine Learning For On Device Development
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Author : Laurence Moroney
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-08-12
Ai And Machine Learning For On Device Development written by Laurence Moroney and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-12 with Computers categories.
Chapter 2. Introduction to Computer Vision -- Using Neurons for Vision -- Your First Classifier: Recognizing Clothing Items -- The Data: Fashion MNIST -- A Model Architecture to Parse Fashion MNIST -- Coding the Fashion MNIST Model -- Transfer Learning for Computer Vision -- Summary -- Chapter 3. Introduction to ML Kit -- Building a Face Detection App on Android -- Step 1: Create the App with Android Studio -- Step 2: Add and Configure ML Kit -- Step 3: Define the User Interface -- Step 4: Add the Images as Assets -- Step 5: Load the UI with a Default Picture.
Lightgbm In Practice
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-11
Lightgbm In Practice written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-11 with Computers categories.
"LightGBM in Practice" "LightGBM in Practice" offers a comprehensive and authoritative exploration of one of the most powerful tools in the modern machine learning landscape. Beginning with in-depth coverage of LightGBM's foundational principles—such as gradient boosting decision trees, histogram-based learning, and innovative strategies for scalable performance—this book demystifies the underlying algorithms that fuel LightGBM’s speed and accuracy. Through clear explanations and mathematical rigor, readers will gain a deep understanding of both the theoretical and practical underpinnings that set LightGBM apart, including exclusive feature bundling, gradient-based sampling, and scalable system architecture. As the journey continues, "LightGBM in Practice" seamlessly bridges theory with real-world engineering. Readers will learn sophisticated data preparation and feature engineering techniques tailored for large-scale tabular and sparse datasets, discover best practices for distributed and GPU-accelerated training, and master advanced model optimization, hyperparameter tuning, and integration within enterprise ML pipelines. Dedicated chapters address model interpretability with industry-leading tools like SHAP and LIME, while also covering the nuances of regulatory compliance, auditability, and transparency—making the book indispensable for production-grade deployment in mission-critical environments. The final sections delve into specialized applications, operational strategies, and the future of LightGBM. From time series forecasting and recommendation engines to privacy preservation and fairness audits, "LightGBM in Practice" empowers practitioners to securely and robustly deploy, monitor, and scale models across diverse domains—including finance, health, and regulated industries. Complete with case studies, actionable code insights, and guidance for contributing to the open-source ecosystem, this essential guide ensures readers remain at the forefront of gradient boosting innovations for years to come.
Federated Learning
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Author : Qiang Yang
language : en
Publisher: Springer Nature
Release Date : 2020-11-25
Federated Learning written by Qiang Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Computers categories.
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Genetic Programming Theory And Practice Xxi
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Author : Stephan M. Winkler
language : en
Publisher: Springer Nature
Release Date : 2025-02-27
Genetic Programming Theory And Practice Xxi written by Stephan M. Winkler and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-27 with Computers categories.
This book brings together some of the most impactful researchers in the field of genetic programming (GP), each one working on unique and interesting intersections of theoretical development and practical applications of this evolutionary-based machine learning paradigm. Topics of particular interest for this year ́s book include powerful modeling techniques through GP-based symbolic regression, novel selection mechanisms that help guide the evolutionary process, modular approaches to GP, and applications in cybersecurity, biomedicine, and program synthesis, as well as papers by practitioner of GP that focus on usability and real-world results. In summary, readers will get a glimpse of the current state-of-the-art in GP research.
Mlflow In Practice
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-14
Mlflow In Practice written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-14 with Computers categories.
"MLflow in Practice" "MLflow in Practice" is a comprehensive guide for data scientists, ML engineers, and enterprise practitioners seeking to harness the full power of MLflow in modern MLOps workflows. The book opens with a thorough exploration of MLflow’s core components—including Experiment Tracking, Projects, Models, and Model Registry—demystifying its architecture, deployment patterns, and seamless integration with leading platforms like Databricks, AzureML, Kubeflow, and Airflow. Readers gain valuable insights into positioning MLflow within the broader MLOps ecosystem, choosing between open source and enterprise offerings, and implementing robust security and governance practices from the outset. Delving deep into practical implementation, the book provides actionable best practices for managing experiments, logging and visualizing runs, packaging reproducible ML projects, and orchestrating scalable deployment pipelines. Advanced chapters address complex scenarios such as distributed experimentation, hybrid and multi-cloud deployments, model lifecycle management, automated retraining, and CI/CD integration. Coverage extends to securing sensitive data, ensuring compliance with industry regulations, and developing enterprise-ready ML systems with full traceability, auditability, and disaster recovery. Enriched with real-world case studies and forward-looking insights, "MLflow in Practice" showcases MLflow’s transformative role across diverse domains—from regulated enterprise environments and academic research to edge IoT and AI startups. Readers will not only learn how to deploy, monitor, and optimize ML models in production, but also stay ahead of emerging trends in generative AI, open standards, and collaborative experimentation. Whether you are modernizing machine learning operations or scaling ML workflows globally, this book equips you with the strategies, patterns, and technical know-how to maximize impact with MLflow.
Data Science On Aws
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Author : Chris Fregly
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-04-07
Data Science On Aws written by Chris Fregly and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-07 with Computers categories.
With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more