Optimizing Machine Learning Pipelines Advanced Techniques With Tensorflow And Kubeflow
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Optimizing Machine Learning Pipelines: Advanced Techniques with TensorFlow and Kubeflow
'Optimizing Machine Learning Pipelines: Advanced Techniques with TensorFlow and Kubeflow' is the definitive guide for data scientists, AI practitioners, and technology enthusiasts committed to optimizing their machine learning workflows. This meticulously crafted book offers an in-depth exploration of advanced machine learning operations (MLOps), with a strong focus on the practical deployment, monitoring, and management of machine learning models using TensorFlow and Kubeflow. The journey begins with an overview of machine learning fundamentals and the inner workings of TensorFlow. As readers progress, they delve deeper into data preprocessing, feature engineering, and model building, gradually mastering the complexities of fine-tuning and optimizing models for production readiness. The pivotal aspect of automating machine learning pipelines with Kubeflow is thoroughly examined, empowering readers to deploy TensorFlow models with utmost confidence. Furthermore, the book provides valuable insights into advanced TensorFlow techniques, ethical AI development, and model management with TensorFlow Serving, ensuring comprehensive coverage of key topics. 'Optimizing Machine Learning Pipelines: Advanced Techniques with TensorFlow and Kubeflow' is crafted to elevate its readers into proficient MLOps practitioners, adept at harnessing the power of TensorFlow and Kubeflow to deliver impactful AI solutions. Whether you are embarking on your first machine learning project or seeking to enhance your existing AI capabilities, this book is your essential resource for mastering advanced machine learning operations.
Building Machine Learning Pipelines
Author: Hannes Hapke
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2020-07-13
Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques
Data Science on AWS
Author: Chris Fregly
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2021-04-07
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