Ementorhub

What you'll learn

Build operations pipelines using DevOps, DataOps, and MLOps

Explain the principles and practices of MLOps (i.e., data management, model training and development, continuous integration and delivery, etc.)

Build and deploy machine learning models in a production environment using MLOps tools and platforms.

Skills you'll gain

Command-Line Interface

GitHub

DevOps

MLOps (Machine Learning Operations)

Serverless Computing

Docker (Software)

Containerization

Rust (Programming Language)

Python Programming

Cloud Solutions

Artificial Intelligence and Machine Learning (AI/ML)

Data Ethics

Applied Machine Learning

Generative AI Agents

CI/CD

Application Frameworks

There are 5 modules in this course

By the end of the course, you will be able to use web frameworks (e.g., Gradio and Hugging Face) for ML solutions, build a command-line tool using the Click framework, and leverage Rust for GPU-accelerated ML tasks. Week 1: Explore MLOps technologies and pre-trained models to solve problems for customers. Week 2: Apply ML and AI in practice through optimization, heuristics, and simulations. Week 3: Develop operations pipelines, including DevOps, DataOps, and MLOps, with Github. Week 4: Build containers for ML and package solutions in a uniformed manner to enable deployment in Cloud systems that accept containers. Week 5: Switch from Python to Rust to build solutions for Kubernetes, Docker, Serverless, Data Engineering, Data Science, and MLOps.

Essential Math and Data Science

Operations Pipelines: DevOps, DataOps, MLOps

End to End MLOps and AIOps

Rust for MLOps: The Practical Transition from Python to Rust