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AI/ML Engineer (MLOps) 90-day Learning Plan
Overview
- Skills: MLOps
- Level: Comprehensive
- Current Experience: No Experience
- Checkpointly Hours: 20 hours/Checkpoint
- Duration: 90 days
Learning Journey
Checkpoint 1
- 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
Checkpoint 2
- 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
Checkpoint 3
- 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
Checkpoint 4
- 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
Checkpoint 5
- 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
Checkpoint 6
- 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
- Introduction to Edge Deployment (Optional): Deploying models on edge devices
Checkpoint 7
- 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
- Introduction to Federated Learning (Optional): Basics and applications
Checkpoint 8
- 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
- Advanced Topics in Edge Deployment (Optional): Challenges and solutions
Checkpoint 9
- 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
- Exploration of Emerging Trends in MLOps (Optional): Keeping up with the latest advancements
Checkpoint 10
- 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
- Networking with MLOps Professionals (Optional): Joining communities and attending meetups
Checkpoint 11
- 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
- Contribution to Open Source MLOps Projects (Optional): Engaging with the community through contributions
Checkpoint 12
- 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
- Advanced Research Topics in MLOps (Optional): Exploring academic papers and research areas for further study
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