From-Sr-DevOps-to-AI-ML-Ops-Engineer-Role
Coursework for a Senior DevOps Engineer Transitioning to an AI/ML Ops Engineer Role
This coursework is designed to help a Senior DevOps Engineer master the skills required to become an AI/ML Ops Engineer. It focuses on leveraging existing DevOps expertise while building new skills in machine learning, AI, and MLOps-specific tools and practices.
1. Core Concepts of AI/ML and MLOps
Objective: Understand the fundamentals of AI/ML and the role of MLOps in operationalizing machine learning models.
Topics
Learning Goals
Suggested Activities
Introduction to AI/ML
Learn the basics of machine learning, supervised/unsupervised learning, and deep learning.
Take beginner-level courses on AI/ML (e.g., Coursera, Udacity).
MLOps Overview
Understand the purpose of MLOps: streamlining ML workflows, automating processes, and CI/CD for ML.
Read articles on MLOps principles.
AI/ML Lifecycle
Learn about the ML lifecycle: data collection, preprocessing, model training, deployment, and monitoring.
Study ML lifecycle frameworks and tools like TensorFlow Extended (TFX) and MLflow.
2. Programming and Scripting for AI/ML
Objective: Build proficiency in programming languages and tools commonly used in AI/ML workflows.
Topics
Learning Goals
Suggested Activities
Python for AI/ML
Master Python, the primary language for AI/ML development.
Learn Python libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch.
Shell Scripting and Automation
Enhance automation skills for managing ML pipelines.
Practice automating workflows using Bash, Python scripts, or tools like Airflow.
Version Control for ML
Learn Git and GitHub/GitLab for managing ML codebases.
Explore Git workflows for ML projects, including DVC (Data Version Control).
3. Machine Learning Model Deployment
Objective: Learn how to deploy and manage machine learning models in production environments.
Topics
Learning Goals
Suggested Activities
Containerization and Orchestration
Use Docker and Kubernetes to deploy ML models.
Practice deploying ML models in containers and managing them with Kubernetes.
Model Serving Frameworks
Learn tools like TensorFlow Serving, TorchServe, and FastAPI for serving ML models.
Deploy a simple ML model using TensorFlow Serving or FastAPI.
Serverless Architectures
Explore serverless options for ML deployment (e.g., AWS Lambda, Google Cloud Functions).
Build and deploy a serverless ML application.
4. CI/CD for Machine Learning
Objective: Adapt DevOps CI/CD practices for machine learning workflows.
Topics
Learning Goals
Suggested Activities
CI/CD Pipelines for ML
Learn how to build CI/CD pipelines for ML workflows.
Use tools like Jenkins, GitHub Actions, or GitLab CI/CD to automate ML model training and deployment.
Testing in ML Pipelines
Understand testing strategies for ML models (e.g., data validation, model validation).
Implement unit tests for ML code and validate datasets using tools like Great Expectations.
Monitoring and Logging
Learn to monitor ML models in production (e.g., drift detection, performance monitoring).
Use tools like Prometheus, Grafana, and MLflow for monitoring and logging.
5. Data Engineering for AI/ML
Objective: Gain expertise in managing and processing large datasets for machine learning.
Topics
Learning Goals
Suggested Activities
Data Pipelines
Build scalable data pipelines for ML workflows.
Use Apache Airflow or Prefect to create and manage data pipelines.
Big Data Tools
Learn tools like Apache Spark, Hadoop, and Kafka for handling large datasets.
Process large datasets using Spark or Kafka.
Data Storage and Management
Explore databases and storage solutions for ML (e.g., NoSQL, S3, BigQuery).
Practice storing and retrieving data for ML workflows.
6. Cloud Platforms for AI/ML
Objective: Master cloud platforms and services for AI/ML workflows.
Topics
Learning Goals
Suggested Activities
Cloud Platforms
Learn AWS, Google Cloud, or Azure for AI/ML workflows.
Take cloud-specific certifications like AWS Certified Machine Learning or Google Professional ML Engineer.
Cloud-Native ML Tools
Use cloud-native tools like SageMaker (AWS), Vertex AI (Google), or Azure ML.
Deploy and manage ML models using cloud-native tools.
Hybrid and Multi-Cloud Strategies
Explore hybrid cloud solutions for ML workflows.
Practice deploying ML models across multiple cloud platforms.
7. Advanced MLOps Practices
Objective: Learn advanced MLOps techniques for scaling and optimizing AI/ML workflows.
Topics
Learning Goals
Suggested Activities
Feature Stores
Learn to manage and reuse features for ML models.
Use tools like Feast or Tecton to create and manage feature stores.
Model Retraining and Automation
Automate model retraining based on new data or performance metrics.
Build pipelines that trigger retraining when model performance degrades.
Security in MLOps
Learn to secure ML workflows (e.g., data encryption, model access control).
Implement security best practices for ML pipelines.
8. Soft Skills and Collaboration
Objective: Develop collaboration and communication skills for working with data scientists and AI/ML teams.
Topics
Learning Goals
Suggested Activities
Collaboration with Data Scientists
Learn to work effectively with data scientists and understand their workflows.
Participate in cross-functional projects involving data scientists.
Agile for AI/ML Projects
Adapt Agile methodologies for AI/ML workflows.
Use Agile tools like Jira or Trello to manage AI/ML projects.
Documentation and Reporting
Document ML workflows and communicate results effectively.
Practice writing clear documentation for ML pipelines and deployment processes.
9. Capstone Project
Objective: Apply all the learned skills in a real-world project.
Project Ideas
Learning Goals
Suggested Activities
End-to-End MLOps Pipeline
Build a complete MLOps pipeline: data ingestion, model training, deployment, and monitoring.
Use tools like MLflow, Kubernetes, and cloud platforms to implement the pipeline.
AI-Powered Application
Develop and deploy an AI-powered application (e.g., recommendation system, chatbot).
Combine DevOps and AI/ML skills to create a scalable application.
Expected Outcomes
By completing this coursework, a Senior DevOps Engineer will:
Gain a strong understanding of AI/ML concepts and workflows.
Master MLOps tools and practices for operationalizing machine learning models.
Build expertise in deploying, monitoring, and scaling AI/ML systems.
Transition seamlessly into the role of an AI/ML Ops Engineer.
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