ML in Production: From Data Scientist to ML Engineer

Turn any ML model within Jupyter Notebook into a deployed production-ready microservice.

Created by: Andrew Wolf
Head of ML/DS

Created by: Ilya Fursov
ML Engineer

What Will I Learn?

• Transform ML models from Jupyter notebooks into production-ready microservices, focusing on production and clean code.
• Learn how to write clean code and utilize tools to maintain high standards of code quality.
• Learn to create robust APIs for ML models, covering API design, request handling, and ensuring scalability and security.
• Master Docker containerization for deploying ML models, including container management and best practices for ML applications.
• Gain hands-on experience with real-world deployment strategies, including CI/CD pipelines, version control, MLOps frameworks and maintaining live models.

Requirements

• Knowledge and practical experience of Python syntax
• Experience in model development in Python (train-test split, tuning hyperparameters, evaluating model performance, making predictions)
• Git basics: git clone, git push, git commit, git pull, git fetch, git branch

Target audience

• Junior/Mid/Senior Data Scientists