DevOps, DataOps, MLOps
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DevOps, DataOps, MLOps
This course is part of MLOps | Machine Learning Operations Specialization
Instructors: Noah Gift
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What you'll learn
Build operations pipelines using DevOps, DataOps, and MLOps
Explain the principles and practices of MLOps (i.e., data management, model training and development, continuous integration and delivery, etc.)
Build and deploy machine learning models in a production environment using MLOps tools and platforms.
Skills you'll gain
Details to know
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There are 5 modules in this course
Learn how to apply Machine Learning Operations (MLOps) to solve real-world problems. The course covers end-to-end solutions with Artificial Intelligence (AI) pair programming using technologies like GitHub Copilot to build solutions for machine learning (ML) and AI applications. This course is for people working (or seeking to work) as data scientists, software engineers or developers, data analysts, or other roles that use ML.
By the end of the course, you will be able to use web frameworks (e.g., Gradio and Hugging Face) for ML solutions, build a command-line tool using the Click framework, and leverage Rust for GPU-accelerated ML tasks. Week 1: Explore MLOps technologies and pre-trained models to solve problems for customers. Week 2: Apply ML and AI in practice through optimization, heuristics, and simulations. Week 3: Develop operations pipelines, including DevOps, DataOps, and MLOps, with Github. Week 4: Build containers for ML and package solutions in a uniformed manner to enable deployment in Cloud systems that accept containers. Week 5: Switch from Python to Rust to build solutions for Kubernetes, Docker, Serverless, Data Engineering, Data Science, and MLOps.
In this module, you will learn how to apply foundational skills in MLOps to build machine learning solutions and apply it by building microservices in Python.
What's included
22 videos11 readings4 assignments1 discussion prompt1 ungraded lab
22 videos•Total 121 minutes
- Introduction to MLOps•5 minutes
- MLOps Background•2 minutes
- MLOps Trends and Techniques•13 minutes
- What is DevOps?•3 minutes
- What is DataOps?•1 minute
- MLOPs: Heavy vs Light•3 minutes
- MLOps: Hierarchy of Needs•3 minutes
- Data Poisoning Machine Learning Systems•3 minutes
- What are the Key Components in MLOPs?•4 minutes
- Considering the MLOps Maturity Models•4 minutes
- What is Continuous Integration?•32 minutes
- What is Continuous Delivery?•3 minutes
- What is a Feature Store?•2 minutes
- What is Data Drift?•2 minutes
- Operationalizing a Microservice•2 minutes
- CI for Microservices•7 minutes
- End to End MLOps HuggingFace Spaces•11 minutes
- App Runner Example•6 minutes
- Flask Example•4 minutes
- Building Golang GCP App Engine Microservice•5 minutes
- Getting Started with Makefile•3 minutes
- The Three Most Important Files in a Python Project•3 minutes
11 readings•Total 105 minutes
- Getting Started and Course Gotchas•10 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
- Report a problem with the course•5 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
4 assignments•Total 120 minutes
- Key Concepts in MLOps•30 minutes
- Quiz: What is MLOPs?•30 minutes
- Key Concepts in MLOps•30 minutes
- Quiz: Key Concepts in Microservices•30 minutes
1 discussion prompt•Total 10 minutes
- Meet and Greet (optional)•10 minutes
1 ungraded lab•Total 60 minutes
- Build CI/CD Solution•60 minutes
In this module, you will learn how to apply essential skills in math and data science for MLOps and apply it by building simulations.
What's included
5 videos9 readings3 assignments3 ungraded labs
5 videos•Total 141 minutes
- Doing Data Science Your First Day•46 minutes
- What is Colab?•6 minutes
- Understanding the Traveling Salesman Problem (TSP)•56 minutes
- Simulations vs. Experiment Tracking•6 minutes
- Machine Learning and AI in Practice with Clustering •26 minutes
9 readings•Total 90 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
3 assignments•Total 90 minutes
- Essential Math and Data Science•30 minutes
- Quiz: Doing Data Science Your First Day•30 minutes
- Quiz: Optimization, Heuristics and Simulations•30 minutes
3 ungraded labs•Total 180 minutes
- Exploring Jupyter Notebook•60 minutes
- Poker Simulation•60 minutes
- Probability Simulations•60 minutes
In this module, you will learn how to build operations pipelines and then apply these skills by building solutions for pre-trained Hugging Face models.
What's included
20 videos9 readings1 assignment2 ungraded labs
20 videos•Total 308 minutes
- Cloud Developer Workspace Advantage•4 minutes
- Key Components of GitHub Ecosystem•4 minutes
- Using GitHub Templates•3 minutes
- Demo of GitHub Codespaces•6 minutes
- GPU Code Whisperer•2 minutes
- Fine-Tuning with Hugging Face•3 minutes
- Demo of GitHub Copilot•8 minutes
- GitHub Actions•4 minutes
- Pipelines for DataOps using Step Functions•17 minutes
- Query Databricks Pipeline•26 minutes
- Building Data Ingestion Pipelines on AWS•2 minutes
- Marco Polo Step Functions •8 minutes
- Transforming Data in Transit on AWS•2 minutes
- Demo AWS Batch Service•3 minutes
- Serverless Data Engineering Pipelines on AWS•2 minutes
- Building Python Functions from Zero •139 minutes
- Building a Python NLP Project with Python Fire •43 minutes
- Extending Google Cloud Functions•10 minutes
- Using Google Cloud Functions•7 minutes
- Deploying a Rust Azure Function with GitHub Actions•15 minutes
9 readings•Total 90 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
1 assignment•Total 30 minutes
- Operations Pipelines: DevOps, DataOps, MLOps•30 minutes
2 ungraded labs•Total 120 minutes
- Marco Polo Python•60 minutes
- Greedy Optimizations•60 minutes
In this module, you will learn how to build end to end MLOps and AIOps solutions and apply it by building solutions with pre-trained models from OpenAI while benefiting from using AI Pair Programming tools like GitHub Copilot.
What's included
12 videos9 readings1 assignment2 ungraded labs
12 videos•Total 362 minutes
- Containerized Microservices•3 minutes
- Containerized Continuous Delivery•9 minutes
- Containerized Machine Learning•39 minutes
- Containerized End-to-End Machine Learning•4 minutes
- Building Distroless Containers•8 minutes
- Use AI to Write AI•2 minutes
- Learn Key Skills for Python DevOps with Copilot•172 minutes
- Amazon CodeWhisperer vs. GitHub Copilot•57 minutes
- Enabling AI Workflows•2 minutes
- Prototyping AI APIs•14 minutes
- Using Transfer Learning•2 minutes
- Assimilate OpenAI Technology using Streamlit•51 minutes
9 readings•Total 90 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
1 assignment•Total 30 minutes
- End to End Containerized MLOps•30 minutes
2 ungraded labs•Total 120 minutes
- Convert Code with AI•60 minutes
- Build a Hugging Face Gradio Web Application•60 minutes
In this module, you will learn how to switch from Python to Rust, a powerful and efficient systems programming language. This module will cover various practical applications of Rust, such as CLI, Web, and MLOps solutions, as well as cloud computing solutions for AWS, GCP, and Azure. You'll also learn how to build Rust solutions for Kubernetes, Docker, Serverless, Data Engineering, Data Science, and Machine Learning Operations (MLOps). By the end of this module, you will have a strong understanding of Rust's key syntax and features, and be able to leverage Rust for GPU-accelerated machine learning tasks.
What's included
25 videos12 readings4 assignments3 ungraded labs
25 videos•Total 197 minutes
- Introduction to Switching to Rust from Python•4 minutes
- Introduction to Rust Lecture Notes•4 minutes
- Configure Rust for AWS Cloud9•8 minutes
- GitHub Copilot Enabled Rust Programming•9 minutes
- Using Rust Packaging for Web Development•10 minutes
- Comparing Energy Efficiency of Rust vs. Python•6 minutes
- Comparing Rust vs. Python for MLOps•7 minutes
- Continuous Integration for Rust with GitHub Actions•8 minutes
- Demo Unit Testing Rust•7 minutes
- Building a Deduplication Tool with Rust•9 minutes
- Zero Shot Classification Rust Hugging Face•10 minutes
- Rust GPU Hugging Face Translator•6 minutes
- PyTorch Stable Diffusion Rust with GPU•7 minutes
- Rust PyTorch Demo•8 minutes
- Building GPU Stress Test•8 minutes
- Using Rust ONNX with EFS for AWS Lambda•10 minutes
- Onboarding to GCP with Python and Rust via CloudShell•8 minutes
- Run Rust Actix Microservice with Google Cloud Run•26 minutes
- Build and Deploy Rust Microservice via Google Cloud Run•7 minutes
- Monitoring and Logging with Rust for Google App Engine•4 minutes
- Load Testing a Rust Microservice•6 minutes
- Building a Containerized Rust Microservice with AWS•9 minutes
- AWS Step Functions with Rust•7 minutes
- Deploy an App Engine Rust Microservice•5 minutes
- Size Calculator in AWS S3•5 minutes
12 readings•Total 120 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson 1 Reflection: Introduction to Rust•10 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- External Lab: Hugging Face Chatbot Arena•10 minutes
- Lesson Reflection•10 minutes
- Key Terms•10 minutes
- Additional Readings•10 minutes
- Lesson Reflection•10 minutes
- Next Steps•10 minutes
- Share your learning experience •10 minutes
4 assignments•Total 120 minutes
- Rust for MLOps•30 minutes
- Quiz: Leveling Up from Python to Rust: An Introduction•30 minutes
- Quiz: Build MLOps Solutions using Rust•30 minutes
- Quiz: Build Cloud Solutions using Rust•30 minutes
3 ungraded labs•Total 180 minutes
- Hello World Rust•60 minutes
- Rust Cargo Lambda•60 minutes
- Rust Sandbox: Discovering Rust•60 minutes
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- 1 star
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Reviewed on Dec 22, 2025
A slightly shorter duration could have been better.
Reviewed on Jun 24, 2024
Very well explained and great step by step examples
Reviewed on Jun 23, 2024
Extremely usefull to understand concepts of MLOps, containers, CI/CD
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