My name is Rustem
and I help to
architect/train/deploy
analytical models
for images at scale
and I help to
architect/train/deploy
analytical models
for images at scale
MY TALKS
Scaling Machine Learning with Containers: Lessons Learned
AI for Real: Improving Yield in SMA and Box Build
DEV317 | Scaling machine learning with containers on AWS: Lessons learned
Leverage ML Inference for Generative AI Models on AWS
Building scalable end-to-end deep learning pipelines in the cloud
Testing Cloud-to-Edge Deep Learning Pipelines: Ensuring Robustness and Efficiency
AWS and Generative AI - Services, Deployments and RAG with Bedrock
Building Scalable End-to-End Deep Learning Pipelines in the Cloud
MY COURSES AND BOOKS
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.
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.
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.
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.
Contributor:
Chapter 23 - Cloud Processing Is Not About Speed
Contributor:
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.
Contributor:
Chapter 67 - The Data Pipeline Is Not About Speed
Contributor:
Chapter 67 - The Data Pipeline Is Not About Speed
97 Things Every Data Engineer Should Know
MY PROJECTS
Single deployment for AWS Step functions with AWS Batch and AWS Fargate
Packaged environments for AWS lambda enable to use various python libraries
Several dynamic alexa skills which envolve scraping data from online sources
Computer vision app enables effortless online communication
Building detection algorithm that won in NGA competition
MY BLOGPOSTS
Top 20 AWS re:Invent 2024 breakout sessions to stay on top of everything related to generative AI
Serverless compute for LLM — with a step-by-step guide for hosting Mistral 7B on AWS Lambda
Re:Invent 2023 attendee guide: Generative AI
Guide for Running Llama 2 Using LLAMA.CPP on AWS Fargate
Making LLMs Scalable: Cloud Inference with AWS Fargate and Copilot
Scalable Cloud inference endpoint using ONNX and AWS Fargate
How AWS Lambda SnapStart drastically reduces cold starts for Serverless Machine Learning Inference
ML No/Low Code services and Use Cases at Re:Invent 2022
12 MLOps breakout sessions I’m looking forward to at Re:Invent 2022
7 things to know before using AWS Panorama
7 MLOps breakout sessions I'm looking forward to at Re:Invent 2021
Machine Learning Inference on AWS Graviton2 Lambda Functions
ML infrastructure @ AWS Summit 2021
MLOps sessions @ re:Invent 2020
Training models using Satellite imagery on Amazon Rekognition Custom Labels
Using custom docker image with SageMaker + AWS Step Functions
Amazon CodeGuru Profiler for monitoring cloud applications
Serverless Deep Learning: Challenges and Benefits
How to serve deep learning models using TensorFlow 2.0 with Cloud Functions
Using the Serverless framework to deploy hybrid serverless/cluster workflows
Using TensorFlow and the Serverless Framework for deep learning and image recognition
Serverless tensorflow on AWS Lambda
ABOUT ME
| 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. |
MY CONTACTS
