My name is Rustem
and I help to
analytical models
for images at scale
Practical Deep Learning on the Cloud
You will learn how to solve problems that ML and data engineers encounter when training many models in a cost-effective way and building data pipelines to enable high scalability.

This course will heavily utilize contemporary public cloud services such as AWS Lambda, Step functions, Batch and Fargate.
Course overview
Serverless Deep Learning with TensorFlow and AWS Lambda
In deep learning it is critical to find the right way to deploy models. Serverless architecture allows us to focus specifically on training, instead of cluster management and scalability.

This course gives all the necessary instructions to start deploying deep learning models using AWS Serverless infrastructure.
Course overview
This book prepares you to use your own custom-trained models with AWS Lambda to achieve a simplified serverless computing approach without spending much time and money. You'll learn to deploy deep learning models with serverless infrastructure, create APIs, process pipelines, and more with the tips included in this book.

By the end of the book, you will have implemented your own project that demonstrates how to use AWS Lambda effectively so as to serve your TensorFlow models in the best possible way.

Hands-On Serverless Deep Learning with TensorFlow and AWS Lambda
Use the serverless computing approach to save time and money
If you create, manage, operate, or configure systems running in the cloud, you're a cloud engineer--even if you work as a system administrator, software developer, data scientist, or site reliability engineer. With this book, professionals from around the world provide valuable insight into today's cloud engineering role.

Chapter 23 - Cloud Processing Is Not About Speed

97 Things Every Cloud Engineer Should Know
Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.

Chapter 67 - The Data Pipeline Is Not About Speed

97 Things Every Data Engineer Should Know
Rustem Feyzkhanov
Machine Learning Engineer
AWS Machine Learning hero and passionate about the use of cloud infrastructure for AI/ML applications. Have experience with architecting and deploying deep learning training and inference pipelines.

Ideas-driven researcher and developer of computer vision systems with a focus on remote sensing and satellite imagery. Independently and as a part of a team completed commercial and Open Source projects in data analysis including spectral analysis, shape vision, time series analysis with deterministic, ML and AI methods. Have experience in bringing several projects from technological idea to the market.