Introduction to MLOps

Overview:

This module introduces MLOps and its critical role in bridging machine learning and operational systems. Students will explore the MLOps lifecycle, industry standards, and how MLOps enables scalable, reproducible, and reliable ML deployments in production environments.

Topics to Cover:

• Understanding the end-to-end ML workflow, from model development to deployment and monitoring.
• Key similarities and differences, with a focus on challenges unique to machine learning systems.:
• An introduction to popular tools in the MLOps ecosystem, such as Kubernetes, MLflow, and CI/CD platforms, with examples of their applications.

Overview:

This module dives into efficient and scalable model training techniques, focusing on distributed training and tools for high-performance ML development.

Topics to Cover:

• Gain hands-on experience with Ray for distributed training, SageMaker for cloud-based training, and MLflow for tracking experiments.

Overview:

In this module, students will learn to package models for smooth deployment, with attention to portability, reproducibility, and compatibility across different environments.

Topics to Cover:

• Learn to package models using Pickle for Python-specific applications and ONNX for cross-platform compatibility, enabling efficient deployment and inference on various platforms.

Overview:

This module covers the essentials of serving ML models in production, focusing on building reliable, scalable, and low-latency deployments.

Topics to Cover:

• Develop skills in deploying models locally and in cloud environments.
• Learn how to use AWS Lambda and other serverless options to deploy models with minimal infrastructure management.
• Build automated pipelines for continuous integration and continuous delivery (CI/CD) to maintain model freshness and robustness in production.

Overview:

This module teaches techniques for monitoring models in production, with a focus on maintaining performance, identifying drift, and ensuring model reliability over time.

Topics to Cover:

• Implement monitoring solutions to track key metrics, such as accuracy and latency, and set up alerts for anomaly detection.
• Learn methods to detect data and concept drift, ensuring the model’s relevance and accuracy.
• Design feedback loops and set retraining triggers to keep models up-to-date with changing data patterns.