MLOps Bootcamp: Mastering AI Operations for Success – AIOps

1. Python for MLOps:

• Dive into the fundamentals of Python tailored specifically for MLOps.
• Explore Python’s role in streamlining and enhancing Machine Learning processes.
• Develop proficiency in leveraging Python for effective MLOps practices.

2. Python for Data Science:

• Uncover the power of Python in the context of Data Science.
• Learn essential data manipulation and analysis techniques using Python.
• Understand how Python enhances the entire data science lifecycle.

3. Git and GitHub Fundamentals:

• Master the essentials of version control with Git.
• Understand how GitHub facilitates collaborative development in MLOps.
• Learn to manage and track changes effectively within MLOps projects.

4. Packaging the ML Models:

• Delve into the art of packaging Machine Learning models.
• Explore different packaging techniques and their implications.
• Ensure your ML models are easily deployable and reproducible.

5. MLflow – Manage ML Experiments:

• Learn to effectively manage and track Machine Learning experiments.
• Understand the features and benefits of MLflow for experiment tracking and management.
• Implement MLflow in your MLOps projects for enhanced experimentation.

6. Crash Course on YAML:

• Acquire a solid foundation in YAML, a key configuration language.
• Learn how YAML is used in MLOps for configuration and deployment.
• Gain practical skills in writing and interpreting YAML files.

7. Docker for Machine Learning:

• Explore Docker and its role in containerizing Machine Learning applications.
• Understand the advantages of containerization for MLOps.
• Learn to build and deploy Docker containers for Machine Learning projects.

8. Build MLApps using FastAPI:

• Dive into FastAPI, a modern, fast web framework for building APIs.
• Learn to develop ML applications using FastAPI for efficient and scalable deployments.
• Implement best practices for building robust MLApps.

9. Build MLApps using Streamlit:

• Explore Streamlit, a powerful framework for creating interactive web applications.
• Develop hands-on experience in building MLApps with Streamlit.
• Understand how Streamlit enhances the user interface for Machine Learning applications.

10. Build MLApps using Flask:

• Gain proficiency in Flask, a popular web framework for Python.
• Learn to build and deploy Machine Learning applications using Flask.
• Understand the integration of Flask with MLOps workflows.

11. CI/CD for Machine Learning:

• Explore Continuous Integration and Continuous Deployment (CI/CD) pipelines in the context of MLOps.
• Implement automation to streamline the development, testing, and deployment of ML models.
• Learn to build robust CI/CD workflows for Machine Learning projects.

12. Linux Operating System for DevOps and Data Scientists:

• Understand the fundamentals of the Linux operating system.
• Explore how Linux is essential for both DevOps and Data Scientists in MLOps.
• Gain practical skills in working with Linux for MLOps tasks.

13. Working with Jenkins:

• Dive into Jenkins, an open-source automation server.
• Learn to set up and configure Jenkins for automating MLOps workflows.
• Understand how Jenkins enhances the efficiency of continuous integration and deployment in MLOps.

14. Monitoring and Debugging of ML System:

• Gain insights into effective monitoring and debugging strategies for MLOps.
• Learn tools and techniques to identify and address issues in Machine Learning systems.
• Implement best practices for maintaining the health and performance of ML systems.

15. Continuous Monitoring with Prometheus:

• Explore Prometheus, an open-source monitoring and alerting toolkit.
• Learn to set up continuous monitoring for MLOps using Prometheus.
• Understand how Prometheus enhances observability in Machine Learning applications.

16. Deploy Applications with Docker Compose:

• Extend your Docker skills by mastering Docker Compose.
• Learn to deploy multi-container applications seamlessly using Docker Compose.
• Understand how Docker Compose enhances the deployment of complex MLOps architectures.

17. Continuous Monitoring of Machine Learning Application:

• Dive into continuous monitoring practices specifically tailored for Machine Learning applications.
• Explore tools and strategies to ensure ongoing performance monitoring in MLOps.
• Implement solutions for proactively addressing issues in production ML systems.

18. Monitor the ML System with WhyLogs:

• Explore WhyLogs, a data logging library for Machine Learning.
• Learn how WhyLogs facilitates efficient monitoring and logging of ML data.
• Implement WhyLogs to enhance the observability and traceability of your ML system.

19. Post Productionizing ML Models:

• Understand the crucial steps involved in post-productionizing Machine Learning models.
• Explore strategies for maintaining and updating ML models in a production environment.
• Gain insights into best practices for ensuring the long-term success of deployed ML systems.