AI/ML Engineer (MLOps) 90-day Learning Plan

Overview

Introduction to MLOps: Understanding the need for MLOps in AI/ML projects.
Overview of the Machine Learning Lifecycle: Data collection, model training, deployment, and monitoring.
Basic DevOps Concepts: Version control, CI/CD pipelines, and containerization.
Setting Up a Development Environment: Tools and platforms commonly used in MLOps.

Data Versioning and Management: Tools like DVC and data lineage tracking.
Model Versioning: Techniques and tools for managing different versions of ML models.
Introduction to Docker: Containerization basics and creating Docker images for ML applications.
Building a Simple CI/CD Pipeline: Automating the ML workflow.

Advanced CI/CD for ML: Integrating model training and deployment into CI/CD pipelines.
Experiment Tracking: Using tools like MLflow or Weights & Biases to track experiments.
Introduction to Kubernetes: Basics of orchestration and deploying ML models on Kubernetes.
Monitoring ML Models: Setting up basic monitoring for deployed models.

Feature Stores: Understanding and implementing feature stores for managing features.
Advanced Docker and Kubernetes: Best practices for scaling ML applications.
Model Deployment Strategies: A/B testing, canary releases, and blue-green deployments.
Introduction to Cloud Platforms: Overview of AWS, GCP, and Azure for MLOps.

Security in MLOps: Securing data, models, and infrastructure.
Advanced Monitoring Techniques: Implementing alerting and logging for ML models.
Automating Data Pipelines: Using tools like Apache Airflow or Prefect.
Introduction to Infrastructure as Code (IaC): Using Terraform or AWS CloudFormation.

Hyperparameter Tuning: Techniques and tools for optimizing model performance.
Continuous Training Pipelines: Automating retraining of models with new data.
Advanced Cloud Services for MLOps: Deep dive into AWS SageMaker, GCP AI Platform, or Azure ML.
[Optional] Introduction to Edge Deployment: Deploying models on edge devices.

Ethical Considerations in MLOps: Bias detection and mitigation strategies.
Cost Optimization in MLOps: Managing cloud costs effectively.
Advanced Experimentation Techniques: Bayesian optimization and other advanced methods.
[Optional] Introduction to Federated Learning: Basics and applications.

Performance Optimization: Techniques for optimizing model inference time and resource usage.
Scalability in MLOps: Strategies for scaling ML systems efficiently.
Case Studies in MLOps: Analyzing real-world implementations and best practices.
[Optional] Advanced Topics in Edge Deployment: Challenges and solutions.

Final Project Planning: Designing a comprehensive MLOps project from scratch.
Implementation of Final Project: Applying all learned concepts to build a production-ready MLOps pipeline.
Review and Feedback Session: Evaluating the final project and identifying areas for improvement.
[Optional] Exploration of Emerging Trends in MLOps: Keeping up with the latest advancements.

Project Deployment and Presentation: Deploying the final project and presenting it to peers or mentors.
Post-Deployment Monitoring and Maintenance: Setting up long-term monitoring and maintenance plans.
Reflection and Future Learning Pathways: Identifying areas for further learning and specialization.
[Optional] Networking with MLOps Professionals: Joining communities and attending meetups.

Advanced Optimization Techniques: Exploring cutting-edge optimization methods for ML models.
Exploring New Tools and Technologies in MLOps: Staying updated with the latest tools in the industry.
Final Review of Comprehensive Learning Journey: Summarizing key learnings and achievements.
[Optional] Contribution to Open Source MLOps Projects: Engaging with the community through contributions.

Capstone Project Completion: Finalizing all aspects of the capstone project with a focus on comprehensive application of skills learned.
Peer Review Sessions: Engaging with peers to review each other's projects for feedback and improvement.
Preparation for Real-World Application: Tailoring resumes, preparing for interviews, and understanding job market demands in MLOps.
[Optional] Advanced Research Topics in MLOps: Exploring academic papers and research areas for further study.

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