Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications. Understand what AI engineering is and how it differs from traditional machine learning engineering Learn the process for developing an AI application, the challenges at each step, and approaches to address them Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them Choose the right model, dataset, evaluation benchmarks, and metrics for your needs Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI. AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly). Read more

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problemAutomating the process for continually developing, evaluating, deploying, and updating modelsDeveloping a monitoring system to quickly detect and address issues your models might encounter in productionArchitecting an ML platform that serves across use casesDeveloping responsible ML systems Read more

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Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."Karolis Urbonas, Head of Data Science at Amazon: "A great introduction to machine learning from a world-class practitioner."Chao Han, VP, Head of R&D at Lucidworks: "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."Sujeet Varakhedi, Head of Engineering at eBay: "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''Vincent Pollet, Head of Research at Nuance: "The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: "This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."Everything you really need to know in Machine Learning in a hundred pages. Read more

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code Read more

Learn to understand and implement the latest machine learning innovations to improve your investment performanceMachine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.In the book, readers will learn how to:Structure big data in a way that is amenable to ML algorithmsConduct research with ML algorithms on big dataUse supercomputing methods and back test their discoveries while avoiding false positivesAdvances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting.Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance. Read more

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add "Machine Learning" to your LinkedIn profile?Well, hold on there.... Before you embark on your journey, there are some high-level theory and statistical principles to weave through first.But rather than spend $30-$50 USD on a thick textbook, you may want to listen to this book first. As a clear and concise alternative, this book provides a high-level introduction to machine learning, free downloadable code exercises, and video demonstrations.Machine Learning for Absolute Beginners Third Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy to follow along at home.New Updated EditionThis new edition features extended chapters with quizzes, free supplementary online video tutorials for coding models in Python, and downloadable resources not included in the Second Edition.Disclaimer: If you have passed the "beginner" stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and give a clear lay of the land.In this step-by-step guide, you will learn:How to download free datasetsWhat tools and machine learning libraries you needData scrubbing techniques, including one-hot encoding, binning and dealing with missing dataPreparing data for analysis, including k-fold ValidationRegression analysis to create trend linesk-Means Clustering to find new relationshipsThe basics of Neural NetworksBias/Variance to improve your machine learning modelPLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio. Read more

We've arrived in a new era—GenAI is reshaping industries and decision-making processes across the board. As a result, understanding their potential and pitfalls has become crucial. But in order to stay ahead of the curve, you'll need to develop fresh perspectives on leveraging AI beyond mere technical know-how. Geared toward business leaders and tech professionals alike, this book demystifies the strategic integration of AI into business practices, ensuring you're equipped not just to participate but to lead in this new landscape.This insightful guide by industry leaders Rob Thomas, Paul Zikopoulos, and Kate Soule goes beyond the basics, offering real-life success stories and learned lessons to provide a blueprint for meaningful AI engagement. Whether you're a novice or a seasoned expert, you'll come away with an enhanced understanding of GenAI.Recognize the transformative potential of AI in business and how to harness itNavigate the ethical and operational challenges posed by AI with confidenceUnderstand the dynamic interplay between AI technology and business strategyImplement actionable strategies to integrate AI into your organizational cultureStep confidently into the role of an AI value creator, equipped to lead and innovate Read more

Large language models (LLMs) have proven themselves to be powerful tools for solving a wide range of tasks, and enterprises have taken note. But transitioning from demos and prototypes to full-fledged applications can be difficult. This book helps close that gap, providing the tools, techniques, and playbooks that practitioners need to build useful products that incorporate the power of language models. Experienced ML researcher Suhas Pai offers practical advice on harnessing LLMs for your use cases and dealing with commonly observed failure modes. You’ll take a comprehensive deep dive into the ingredients that make up a language model, explore various techniques for customizing them such as fine-tuning, learn about application paradigms like RAG (retrieval-augmented generation) and agents, and more. Understand how to prepare datasets for training and fine-tuning Develop an intuition about the Transformer architecture and its variants Adapt pretrained language models to your own domain and use cases Learn effective techniques for fine-tuning, domain adaptation, and inference optimization Interface language models with external tools and data and integrate them into an existing software ecosystem Read more

Decision intelligence (DI) has been widely named as a top technology trend for several years, and Gartner reports that more than a third of large organizations are adopting it. Some even say that DI is the next step in the evolution of AI. Many software vendors offer DI solutions today, as they help organizations implement their evidence-based or data-driven decision strategies.But until now, there has been little practical guidance for organizations to formalize decision making and integrate their decisions with data.With this book, authors L. Y. Pratt and N. E. Malcolm fill this gap. They present a step-by-step method for integrating technology into decisions that bridge from actions to desired outcomes, with a focus on systems that act in an advisory, human-in-the-loop capacity to decision makers.This handbook addresses three widespread data-driven decision-making problems:- How can decision makers use data and technology to ensure desired outcomes?- How can technology teams communicate effectively with decision makers to maximize the return on their data and technology investments?- How can organizational decision makers assess and improve their decisions over time?PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio. Read more

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Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandasKey FeaturesDiscover new and updated content on NLP transformers, PyTorch, and computer vision modelingIncludes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutionsImplement ML models, such as neural networks and linear and logistic regression, from scratchPurchase of the print or Kindle book includes a free PDF copyBook DescriptionThe fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts.Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine.This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.What you will learnFollow machine learning best practices throughout data preparation and model developmentBuild and improve image classifiers using convolutional neural networks (CNNs) and transfer learningDevelop and fine-tune neural networks using TensorFlow and PyTorchAnalyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIPBuild classifiers using support vector machines (SVMs) and boost performance with PCAAvoid overfitting using regularization, feature selection, and moreWho this book is forThis expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.Table of ContentsGetting Started with Machine Learning and PythonBuilding a Movie Recommendation EnginePredicting Online Ad Click-Through with Tree-Based AlgorithmsPredicting Online Ad Click-Through with Logistic RegressionPredicting Stock Prices with Regression AlgorithmsPredicting Stock Prices with Artificial Neural NetworksMining the 20 Newsgroups Dataset with Text Analysis TechniquesDiscovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic ModelingRecognizing Faces with Support Vector MachineMachine Learning Best PracticesCategorizing Images of Clothing with Convolutional Neural NetworksMaking Predictions with Sequences Using Recurrent Neural NetworksAdvancing Language Understanding and Generation with Transformer ModelsBuilding An Image Search Engine Using Multimodal ModelsMaking Decisions in Complex Environments with Reinforcement Learning Read more

Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming languageMachine learning―a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions―allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reductionCovers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clusteringDescribes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniquesExplains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoostPractical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field. Read more

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Unlock the core math and understand the technical nuances of quantum computing in this detailed guide. Delve into the practicality of NISQ algorithms, and survey promising advancements in quantum machine learning.Key FeaturesDiscover how quantum computing works and delve into the math behind it with practical examplesLearn about and assess the most up-to-date quantum computing topics including quantum machine learningExplore the inner workings of existing quantum computing technologies to understand how they may perform significantly better than their classical counterpartsBook DescriptionDancing with Qubits, Second Edition, is a comprehensive quantum computing textbook that starts with an overview of why quantum computing is so different from classical computing and describes several industry use cases where it can have a major impact. A full description of classical computing and the mathematical underpinnings of quantum computing follows, helping you better understand concepts such as superposition, entanglement, and interference. Next up are circuits and algorithms, both basic and sophisticated, as well as a survey of the physics and engineering ideas behind how quantum computing hardware is built. Finally, the book looks to the future and gives you guidance on understanding how further developments may affect you.This new edition is updated throughout with more than 100 new exercises and includes new chapters on NISQ algorithms and quantum machine learning.Understanding quantum computing requires a lot of math, and this book doesn't shy away from the necessary math concepts you'll need. Each topic is explained thoroughly and with helpful examples, leaving you with a solid foundation of knowledge in quantum computing that will help you pursue and leverage quantum-led technologies.What you will learnExplore the mathematical foundations of quantum computingDiscover the complex, mind-bending concepts that underpin quantum systemsUnderstand the key ideas behind classical and quantum computingRefresh and extend your grasp of essential mathematics, computing, and quantum theoryExamine a detailed overview of qubits and quantum circuitsDive into quantum algorithms such as Grover’s search, Deutsch-Jozsa, Simon’s, and Shor’sExplore the main applications of quantum computing in the fields of scientific computing, AI, and elsewhereWho this book is forDancing with Qubits, Second Edition, is a quantum computing textbook for all those who want to understand and explore the inner workings of quantum computing. This entails building up from basic to some sophisticated mathematics and is therefore best suited for those with a healthy interest in mathematics, physics, engineering, or computer science.Table of ContentsWhy Quantum ComputingThey're Not Old, They're ClassicsMore Numbers than You Can ImaginePlanes and Circles and Spheres, Oh MyDimensionsWhat Do You Mean “Probably”?One QubitTwo Qubits, ThreeWiring Up the CircuitsFrom Circuits to AlgorithmsGetting PhysicalConsidering NISQ AlgorithmsIntroduction to Quantum Machine LearningQuestions about the Future Read more

The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion modelsPurchase of the print or Kindle book includes a free eBook in PDF formatKey FeaturesUnderstand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion modelsBuild solutions for real-world computer vision problems using PyTorchAll the code files are available on GitHub and can be run on Google ColabBook DescriptionWhether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks.The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion.You’ll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You’ll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you’ll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you’ll learn best practices for deploying a model to production.By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.What you will learnGet to grips with various transformer-based architectures for computer vision, CLIP, Segment-Anything, and Stable Diffusion, and test their applications, such as in-painting and pose transferCombine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasksImplement multi-object detection and segmentationLeverage foundation models to perform object detection and segmentation without any training data pointsLearn best practices for moving a model to productionWho this book is forThis book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using deep learning and PyTorch. It's useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the book.Table of ContentsArtificial Neural Network FundamentalsPyTorch FundamentalsBuilding a Deep Neural Network with PyTorchIntroducing Convolutional Neural NetworksTransfer Learning for Image ClassificationPractical Aspects of Image ClassificationBasics of Object DetectionAdvanced Object DetectionImage SegmentationApplications of Object Detection and SegmentationAutoencoders and Image ManipulationImage Generation Using GANs(N.B. Please use the Read Sample option to see further chapters) Read more

How can startups successfully scale customer acquisition and revenue growth with a Lean team? Out-of-the-box acquisition solutions from Facebook, Google, and others provide a good start, but the companies that can tailor those solutions to meet their specific needs, objectives, and goals will come out winners. But that hasn’t been an easy task―until now. With this practical book, author Lomit Patel shows you how to use AI and automation to provide an operational layer atop those acquisition solutions to deliver amazing results for your company. You’ll learn how to adapt, customize, and personalize cross-channel user journeys to help your company attract and retain customers―to usher in the new age of Autonomous Marketing. Learn how AI and automation can support the customer acquisition efforts of a Lean Startup Dive into Customer Acquisition 3.0, an initiative for gaining and retaining customers Explore ways to use AI for marketing purposes Understand the key metrics for determining the growth of your startup Determine the right strategy to foster user acquisition in your company Manage the increased complexity and risk inherent in AI projects Read more

Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architecturesKey FeaturesApply ML and global models to improve forecasting accuracy through practical examplesEnhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATSLearn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressionsPurchase of the print or Kindle book includes a free eBook in PDF formatBook DescriptionPredicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both.Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.What you will learnBuild machine learning models for regression-based time series forecastingApply powerful feature engineering techniques to enhance prediction accuracyTackle common challenges like non-stationarity and seasonalityCombine multiple forecasts using ensembling and stacking for superior resultsExplore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time seriesEvaluate and validate your forecasts using best practices and statistical metricsWho this book is forThis book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended.Table of ContentsIntroducing Time SeriesAcquiring and Processing Time Series DataAnalyzing and Visualizing Time Series DataSetting a Strong Baseline Forecast Time Series Forecasting as Regression Feature Engineering for Time Series ForecastingTarget Transformations for Time Series Forecasting Forecasting Time Series with Machine Learning Models Ensembling and StackingGlobal Forecasting Models Introduction to Deep LearningBuilding Blocks of Deep Learning for Time SeriesCommon Modeling Patterns for Time Series(N.B. Please use the Read Sample option to see further chapters) Read more

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The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly Read more