MLOps Foundation Certification

The MLOps Foundation Certification by DevOpsSchool, in association with Rajesh Kumar from www.RajeshKumar.xyz, is designed to provide a foundational understanding of MLOps principles, tools, and best practices. This certification is tailored for students and professionals seeking to integrate machine learning (ML) within DevOps pipelines, streamlining the deployment, monitoring, and management of ML models.

Who Should Take This Certification?

• Machine Learning Engineers and Data Scientists
• DevOps Engineers and Software Engineers
• Data Engineers and IT Operations professionals
• Project Managers in data-centric projects
• Anyone interested in operationalizing machine learning within DevOps frameworks

Learning Objectives

• Understand MLOps principles and their role in modern ML workflows
• Implement end-to-end ML pipelines for model development and deployment
• Apply version control and CI/CD for ML models and data
• Use automation tools for reproducible ML workflows
• Ensure monitoring and compliance in deployed ML systems

Agenda: MLOps Foundation Certification

Here’s the MLOps Foundation Certification Manual Content in a tabular format:

Section	Topics	Subtopics
Welcome and Introduction	Overview of the Certification Program	Introduction to the certification, objectives, and program structure
	Expectations and Outcomes	What students will gain and learn through this certification
Understanding MLOps	Definition and Importance of MLOps	Overview of MLOps and its significance in operationalizing ML
	Key Components of the MLOps Lifecycle	Core components and stages in the MLOps lifecycle
	Differences between Traditional DevOps and MLOps	Comparison of DevOps and MLOps workflows
Machine Learning Basics	Overview of Machine Learning Concepts	Key ML concepts essential for MLOps
	Types of Machine Learning	Supervised, unsupervised, and reinforcement learning
MLOps Lifecycle	Stages of the MLOps Lifecycle	Data collection, model training, deployment, monitoring, and maintenance
	Importance of Collaboration	Enhancing teamwork between data scientists and operations teams
Tools and Technologies	Overview of Popular MLOps Tools	Introduction to tools such as MLflow, Kubeflow, TFX
	Setting Up the Environment for Labs	Preparing the environment for hands-on labs
Data Management in MLOps	Data Versioning and Management Techniques	Approaches to manage and version data
	Data Pipelines and ETL Processes	Building and managing data pipelines
	Tools for Data Management	Tools like DVC and Apache Airflow for data handling
Model Development and Training	Best Practices for Model Development	Techniques for efficient and accurate model development
	Experiment Tracking and Management	Tracking model experiments and results
	Introduction to AutoML Tools	Overview of automated machine learning (AutoML) tools
Model Deployment Strategies	Techniques for Model Deployment	Strategies for effectively deploying ML models
	CI/CD for Machine Learning	Integrating continuous integration and deployment for ML models
	Using Docker and Kubernetes for Deployment	Leveraging Docker and Kubernetes to manage and deploy models
Hands-On Lab: Model Deployment	Deploying a Model Using Selected Tool	Practical deployment exercise using Flask, FastAPI, or similar
	Hands-On Exercises	Lab exercises to reinforce deployment concepts
Model Monitoring and Maintenance	Importance of Model Monitoring in Production	Reasons for tracking model performance over time
	Techniques for Monitoring Model Performance	Methods to assess and maintain model quality
	Handling Model Drift and Retraining	Strategies to detect model drift and retrain models accordingly
MLOps Governance and Compliance	Governance Practices in MLOps	Best practices for maintaining governance and oversight in MLOps
	Regulatory Compliance and Ethical Considerations	Understanding compliance requirements and ethical issues in ML
Capstone Project	Group Activity: End-to-End MLOps Pipeline	Group project to build a complete MLOps pipeline
	Presentation of Projects and Feedback	Presenting project outcomes and receiving instructor feedback
Certification Exam	Review of Key Concepts	Recap of major topics covered in the certification
	Administer the Certification Exam	Final examination to assess knowledge gained
Closing Remarks and Next Steps	Wrap-Up and Future Opportunities	Summary, closing remarks, and guidance on next steps in MLOps

This table provides a structured view of each section, topic, and subtopic in the MLOps Foundation Certification, designed to guide students through the entire learning path, from understanding MLOps fundamentals to implementing end-to-end solutions.

Practical Labs and Hands-On Exercises

• Building and deploying an ML pipeline using Kubeflow or Apache Airflow
• Setting up data versioning with DVC and experiment tracking with MLFlow
• Configuring CI/CD pipelines for automated model testing and deployment
• Deploying a Dockerized model to a Kubernetes environment
• Implementing monitoring with Prometheus and Grafana for live model tracking

Certification Exam Details

• Exam Format: Multiple-choice questions, case studies, and hands-on assessments
• Duration: 2 hours
• Passing Score: 70%
• Prerequisites: Basic understanding of ML, DevOps, and cloud environments

Study Resources

• Books: “Practical MLOps: Operationalizing Machine Learning Models”, “Machine Learning Engineering”
• Video Tutorials and Webinars from DevOpsSchool
• Tool Documentation: Kubeflow, DVC, MLFlow, Docker, Kubernetes

Trainer Profile

Rajesh Kumar is a seasoned trainer specializing in MLOps, DevOps, and cloud-based machine learning solutions. His hands-on training approach helps students effectively bridge the gap between data science and production-grade machine learning.

Certification Benefits

Completing the MLOps Foundation Certification equips professionals to manage and scale ML models effectively within DevOps frameworks, making them highly valuable in data-driven organizations. This certification highlights proficiency in MLOps best practices, automation, and monitoring, critical for robust ML operations.