The Car Learning Solutions Architect Handbook - Moment Edition: Practical Strategies Together With Best Practices On The Ml Lifecycle, Organization Blueprint, Mlops, Together With Generative Ai
Design, construct, together with secure scalable auto learning (ML) systems to solve real-world business concern problems alongside Python and AWS
Purchase of the impress or Kindle volume includes a costless PDF eBook
Key Features- Go in-depth into the ML lifecycle, from ideation together with information direction to deployment as well as scaling
- Apply hazard management techniques inwards the ML lifecycle together with pattern architectural patterns for diverse ML platforms in addition to solutions
- Understand the generative AI lifecycle, its center technologies, too implementation risks
David Ping, Head of GenAI too ML Solution Architecture for global industries at AWS, provides skillful insights in addition to practical examples to aid yous get a proficient ML solutions architect, linking technical architecture to business-related skills.
You'll learn well-nigh ML algorithms, cloud infrastructure, arrangement blueprint, MLOps, too how to use ML to solve existent-globe concern problems. David explains the generative AI project lifecycle in addition to examines Retrieval Augmented Generation (RAG), an effective architecture blueprint for generative AI applications. You'll as well acquire nigh open-origin technologies, such every bit Kubernetes/Kubeflow, for building a information science environs and ML pipelines earlier building an corporation ML architecture using AWS. As good every bit ML adventure management and the unlike 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 accept gained a comprehensive agreement of AI/ML across all cardinal aspects, including business concern use cases, data science, existent-Earth solution architecture, gamble management, and governance. You'll own the skills to pattern and make ML solutions that effectively cater to mutual function cases together with follow established ML architecture patterns, enabling yous to excel every bit a truthful professional person inwards the plain.
What y'all volition learn- Apply ML methodologies to solve business concern problems across industries
- Design a practical enterprise ML platform architecture
- Gain an understanding of AI take a chance direction frameworks together with techniques
- Build an end-to-stop data management architecture using AWS
- Train large-scale ML models and optimize model inference latency
- Create a business organisation application using artificial intelligence services and custom models
- Dive into generative AI alongside purpose cases, architecture patterns, in addition to RAG
This book is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, too MLOps engineers. Additionally, data scientists too analysts who desire to enhance their practical noesis of ML systems technology, besides every bit AI/ML production managers as well as adventure officers who desire to reach an agreement of ML solutions in addition to AI risk management, volition also discover this volume useful. A basic knowledge of Python, AWS, linear algebra, probability, as well as cloud infrastructure is required before you lot become started amongst this handbook.
Table of Contents- Navigating the ML Lifecycle with 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 amongst AWS ML Services
- Advanced ML Engineering
- Building ML Solutions alongside AWS AI Services
- AI Risk Management
- Bias, Explainability, Privacy, together with Adversarial Attacks
