Hands-On Auto Learning With Scikit-Larn, Keras, Too Tensorflow: Concepts, Tools, And Techniques To Make Intelligent Systems
Purchase of the print or Kindle volume includes a complimentary PDF eBook
Key Features
- Go inwards-depth into the ML lifecycle, from ideation as well as information direction to deployment as well as scaling
- Apply take a chance management techniques in the ML lifecycle in addition to design architectural patterns for various ML platforms in addition to solutions
- Understand the generative AI lifecycle, its nub technologies, and implementation risks
Book Description
David Ping, Head of GenAI as well as ML Solution Architecture for global industries at AWS, provides practiced insights and practical examples to aid y'all go a practiced ML solutions architect, linking technical architecture to concern-related skills.
You'll larn about ML algorithms, cloud infrastructure, organization pattern, MLOps, as well as how to apply ML to solve existent-earth business organisation problems. David explains the generative AI projection lifecycle together with examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You'll as well larn nearly open up-rootage technologies, such every bit Kubernetes/Kubeflow, for edifice a data science environs as well as ML pipelines earlier building an firm ML architecture using AWS. As well as ML take a chance management and the different stages of AI/ML adoption, the biggest novel addition to the handbook is the deep exploration of generative AI.
By the terminate of this volume, you lot'll take gained a comprehensive understanding of AI/ML across all key aspects, including business concern function cases, data scientific discipline, existent-globe solution architecture, gamble direction, and governance. You'll own the skills to design and construct ML solutions that effectively cater to common use cases and follow established ML architecture patterns, enabling yous to excel every bit a true professional inwards the plain.
What you will larn
- Apply ML methodologies to solve business organization problems across industries
- Design a practical firm ML platform architecture
- Gain an understanding of AI run a risk direction frameworks and techniques
- Build an stop-to-end information direction architecture using AWS
- Train large-scale ML models too optimize model inference latency
- Create a concern application using artificial tidings services and custom models
- Dive into generative AI alongside purpose cases, architecture patterns, in addition to RAG
Who this volume is for
This volume is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, too MLOps engineers. Additionally, information scientists as well as analysts who desire to heighten their practical cognition of ML systems technology, likewise as AI/ML production managers in addition to run a risk officers who want to attain an agreement of ML solutions too AI risk direction, will besides observe this book useful. A basic cognition of Python, AWS, linear algebra, probability, together with cloud infrastructure is required before you go started alongside this handbook.
Table of Contents
- Navigating the ML Lifecycle amongst ML Solutions Architecture
- Exploring ML Business Use Cases
- Exploring ML Algorithms
- Data Management for ML
- Exploring Open-Source ML Libraries
- Kubernetes Container Orchestration Infrastructure Management
- Open-Source ML Platforms
- Building a Data Science Environment using AWS ML Services
- Designing an Enterprise ML Architecture with AWS ML Services
- Advanced ML Engineering
- Building ML Solutions alongside AWS AI Services
- AI Risk Management
- Bias, Explainability, Privacy, together with Adversarial Attacks
