The Production ML Blueprint
What happens between a working prototype and a production release? This four-chapter ebook was created with the answer in mind, offering a focused look at system design, data preparation, pipeline orchestration, and deployment. Learn to uncover design weaknesses before implementation, explore faster data access and processing, and follow a batch inference example built with Kubeflow Pipelines. Then examine how to test inference under realistic conditions and compare strategies for releasing model updates. You’ll get concrete examples of the engineering decisions beyond model development as well as practical guidance for your next machine learning project.
Download the The Production ML Blueprint ebook for free!
What's inside:
- Catch design weaknesses before implementation
- Reduce data bottlenecks with Apache Arrow and SQL
- Prepare ML features at scale
- Automate batch inference with Kubeflow Pipelines
- Prepare models for production with packaging, artifact tracking, and testing
- Weigh deployment tradeoffs before releasing model updates
Get all 4 ebooks for $99.99 here!
Machine Learning Platform Engineering
Machine Learning Platform Engineering shows you how to build an effective IDP for ML and AI applications. Each chapter illuminates a vital part of the ML workflow, including setting up orchestration pipelines, selecting models, allocating resources for training, inference, and serving, and more. As you go, you’ll create a versatile modern platform using open source tools like Kubeflow, MLFlow, BentoML, Evidently, Feast, and LangChain.
Machine Learning System Design
Machine Learning System Design shows you how to design and deploy a machine learning project from start to finish. You’ll follow a step-by-step framework for designing, implementing, releasing, and maintaining ML systems. As you go, requirement checklists and real-world examples help you prepare to deliver and optimize your own ML systems. You’ll especially love the campfire stories and personal tips, and ML system design interview tips.
Effective Data Science Infrastructure
Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company’s specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems.
Designing Deep Learning Systems
Designing Deep Learning Systems: A software engineer's guide teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer’s perspective, including its major components and how they are connected. Then, it carefully guides you through the engineering methods you’ll need to build your own maintainable, efficient, and scalable deep learning platforms.
