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Noah Gift
Apply Real-World Machine Learning with DevOps, DataOps & MLOps
Read more
Apply Real-World Machine Learning with DevOps, DataOps & MLOps
Read more
Apply Real-World Machine Learning with DevOps, DataOps & MLOps
- Master end-to-end MLOps solutions through hands-on AI pair programming
- Leverage cutting-edge tools like GitHub Copilot, Gradio & Hugging Face
- Build containerized ML apps deployable across cloud platforms
Course Journey:
- Explore MLOps landscape and pre-trained models to solve business problems
- Apply ML/AI in practice through optimization, simulation & heuristics
- Develop integrated DevOps, DataOps & MLOps pipelines on GitHub
- Package ML solutions in containers for seamless cloud deployment
- Transition to Rust for high-performance GPU-accelerated ML tasks
Ideal for data scientists, software engineers, analysts & professionals working with machine learning. Gain holistic MLOps skills through real-world projects.
Register for this course and see more details by visiting:
OpenCourser.com/course/4yo90n/devops
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What's inside
Learning objectives
- Use web frameworks like gradio & hugging face for interactive ml
- Build command-line tools for ml/ai applications with click
- Leverage rust's performance for kubernetes, docker & serverless use cases
- Containerize and deploy ml pipelines across cloud environments
- Use web frameworks like gradio & hugging face for interactive ml
- Build command-line tools for ml/ai applications with click
- Leverage rust's performance for kubernetes, docker & serverless use cases
- Containerize and deploy ml pipelines across cloud environments
Syllabus
Week 1: Introduction to MLOps
\- Introduction to MLOps (Video, 4 minutes, Preview module)
\- MLOps Background (Video, 2 minutes)
\- MLOps Trends and Techniques (Video, 13 minutes)
Read more
Syllabus
Week 1: Introduction to MLOps
\- Introduction to MLOps (Video, 4 minutes, Preview module)
\- MLOps Background (Video, 2 minutes)
\- MLOps Trends and Techniques (Video, 13 minutes)
Read more
\- What is DevOps? (Video, 2 minutes)
\- What is DataOps? (Video, 1 minute)
\- MLOPs: Heavy vs Light (Video, 3 minutes)
\- MLOps: Hierarchy of Needs (Video, 3 minutes)
\- Data Poisoning Machine Learning Systems (Video, 2 minutes)
\- What are the Key Components in MLOPs? (Video, 3 minutes)
\- Considering the MLOps Maturity Models (Video, 4 minutes)
\- What is Continuous Integration? (Video, 32 minutes)
\- What is Continuous Delivery? (Video, 2 minutes)
\- What is a Feature Store? (Video, 2 minutes)
\- What is Data Drift? (Video, 1 minute)
\- Operationalizing a Microservice (Video, 1 minute)
\- CI for Microservices (Video, 7 minutes)
\- End to End MLOps HuggingFace Spaces (Video, 11 minutes)
\- App Runner Example (Video, 5 minutes)
\- Flask Example (Video, 3 minutes)
\- Building Golang GCP App Engine Microservice (Video, 5 minutes)
\- Getting Started with Makefile (Video, 2 minutes)
\- The Three Most Important Files in a Python Project (Video, 3 minutes)
\- Getting Started and Course Gotchas (Reading, 10 minutes)
\- Key Terms (Reading, 10 minutes)
\- Additional Readings (Reading, 10 minutes)
\- Transforming Data in Transit on AWS (Video, 2 minutes)
\- Lesson Reflection (Reading, 10 minutes)
\- Demo AWS Batch Service (Video, 3 minutes)
\- Serverless Data Engineering Pipelines on AWS (Video, 1 minute)
\- Building Python Functions from Zero (Video, 138 minutes)
\- Key Concepts in MLOps (Quiz, 30 minutes)
\- Building a Python NLP Project with Python Fire (Video, 43 minutes)
\- Quiz: What is MLOPs? (Quiz, 30 minutes)
\- Quiz: Key Concepts in Microservices (Quiz, 30 minutes)
\- Meet and Greet (optional) (Discussion Prompt, 10 minutes)
\- Let Us Know if Something's Not Working (Discussion Prompt, 10 minutes)
\- Build CI/CD Solution (Ungraded Lab, 60 minutes)
Week 2: Essential Math and Data Science
\- Doing Data Science Your First Day (Video, 46 minutes, Preview module)
\- What is Colab? (Video, 5 minutes)
\- Understanding the Traveling Salesman Problem (TSP) (Video, 56 minutes)
\- Simulations vs. Experiment Tracking (Video, 6 minutes)
\- Machine Learning and AI in Practice with Clustering (Video, 26 minutes)
\- Extending Google Cloud Functions (Video, 10 minutes)
\- Using Google Cloud Functions (Video, 6 minutes)
\- Deploying a Rust Azure Function with GitHub Actions (Video, 14 minutes)
\- Assimilate OpenAI Technology using Streamlit (Video, 50 minutes)
\- Essential Math and Data Science (Quiz, 30 minutes)
\- Quiz: Doing Data Science Your First Day (Quiz, 30 minutes)
\- Quiz: Optimization, Heuristics and Simulations (Quiz, 30 minutes)
\- Exploring Jupyter Notebook (Ungraded Lab, 60 minutes)
\- Poker Simulation (Ungraded Lab, 60 minutes)
\- Probability Simulations (Ungraded Lab, 60 minutes)
Week 3: Operations Pipelines: DevOps, DataOps, MLOps
\- Cloud Developer Workspace Advantage (Video, 4 minutes, Preview module)
\- Key Components of GitHub Ecosystem (Video, 3 minutes)
\- Using GitHub Templates (Video, 2 minutes)
\- Demo of GitHub Codespaces (Video, 6 minutes)
\- GPU Code Whisperer (Video, 1 minute)
\- Fine-Tuning with Hugging Face (Video, 3 minutes)
\- Demo of GitHub Copilot (Video, 8 minutes)
\- GitHub Actions (Video, 3 minutes)
\- Pipelines for DataOps using Step Functions (Video, 16 minutes)
\- Query Databricks Pipeline (Video, 26 minutes)
\- Building Data Ingestion Pipelines on AWS (Video, 2 minutes)
\- Marco Polo Step Functions (Video, 8 minutes)
\- Comparing Rust vs. Python for MLOps (Video, 7 minutes)
\- Continuous Integration for Rust with GitHub Actions (Video, 7 minutes)
\- Demo Unit Testing Rust (Video, 6 minutes)
\- Building a Deduplication Tool with Rust (Video, 9 minutes)
\- Operations Pipelines: DevOps, DataOps, MLOps (Quiz, 30 minutes)
\- Marco Polo Python (Ungraded Lab, 60 minutes)
\- Greedy Optimizations (Ungraded Lab, 60 minutes)
Week 4: End to End MLOps and AIOps
\- Containerized Microservices (Video, 2 minutes, Preview module)
\- Containerized Continuous Delivery (Video, 8 minutes)
\- Containerized Machine Learning (Video, 39 minutes)
\- Containerized End-to-End Machine Learning (Video, 3 minutes)
\- Building Distroless Containers (Video, 8 minutes)
\- Use AI to Write AI (Video, 1 minute)
\- Learn Key Skills for Python DevOps with Copilot (Video, 171 minutes)
\- Amazon CodeWhisperer vs. GitHub Copilot (Video, 56 minutes)
\- Enabling AI Workflows (Video, 1 minute)
\- Prototyping AI APIs (Video, 14 minutes)
\- Using Transfer Learning (Video, 2 minutes)
\- End to End Containerized MLOps (Quiz, 30 minutes)
\- Convert Code with AI (Ungraded Lab, 60 minutes)
\- Build a Hugging Face Gradio Web Application (Ungraded Lab, 60 minutes)
Week 5: Rust for MLOps: The Practical Transition from Python to Rust
\- Introduction to Switching to Rust from Python (Video, 4 minutes, Preview module)
\- Introduction to Rust Lecture Notes (Video, 4 minutes)
\- Configure Rust for AWS Cloud9 (Video, 8 minutes)
\- GitHub Copilot Enabled Rust Programming (Video, 9 minutes)
\- Using Rust Packaging for Web Development (Video, 9 minutes)
\- Comparing Energy Efficiency of Rust vs. Python (Video, 5 minutes)
\- Zero Shot Classification Rust Hugging Face (Video, 9 minutes)
\- Rust GPU Hugging Face Translator (Video, 6 minutes)
\- PyTorch Stable Diffusion Rust with GPU (Video, 7 minutes)
\- Rust PyTorch Demo (Video, 7 minutes)
\- Building GPU Stress Test (Video, 7 minutes)
\- Using Rust ONNX with EFS for AWS Lambda (Video, 9 minutes)
\- Onboarding to GCP with Python and Rust via CloudShell (Video, 8 minutes)
\- Run Rust Actix Microservice with Google Cloud Run (Video, 25 minutes)
\- Build and Deploy Rust Microservice via Google Cloud Run (Video, 7 minutes)
\- Monitoring and Logging with Rust for Google App Engine (Video, 3 minutes)
\- Load Testing a Rust Microservice (Video, 5 minutes)
\- Building a Containerized Rust Microservice with AWS (Video, 8 minutes)
\- AWS Step Functions with Rust (Video, 6 minutes)
\- Deploy an App Engine Rust Microservice (Video, 5 minutes)
\- Size Calculator in AWS S3 (Video, 4 minutes)
\- Lesson 1 Reflection: Introduction to Rust (Reading, 10 minutes)
\- External Lab: Hugging Face Chatbot Arena (Reading, 10 minutes)
\- Next Steps (Reading, 10 minutes)
\- Rust for MLOps (Quiz, 30 minutes)
\- Quiz: Leveling Up from Python to Rust: An Introduction (Quiz, 30 minutes)
\- Quiz: Build MLOps Solutions using Rust (Quiz, 30 minutes)
\- Quiz: Build Cloud Solutions using Rust (Quiz, 30 minutes)
\- Hello World Rust (Ungraded Lab, 60 minutes)
\- Rust Cargo Lambda (Ungraded Lab, 60 minutes)
\- Rust Sandbox: Discovering Rust (Ungraded Lab, 60 minutes)
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops end-to-end MLOps solutions through hands-on practice
Provides a holistic approach to MLOps, covering DevOps, DataOps, and MLOps
Leverages cutting-edge tools and technologies, including GitHub Copilot, Gradio, and Hugging Face
Instructors Noah Gift are experienced professionals in the field of MLOps
Suitable for data scientists, software engineers, analysts, and professionals working with machine learning
Save this course
Save DevOps, DataOps, MLOps to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in DevOps, DataOps, MLOps with these
activities:
Review basic probability and statistics concepts
Show steps
Review basic probability and statistics concepts to strengthen your foundation for machine learning.
Browse courses on
Probability
Show steps
-
Revisit relevant textbooks or online resources.
-
Solve practice problems to test your understanding.
Read 'Machine Learning Engineering' by Andriy Burkov
Show steps
Read 'Machine Learning Engineering' to gain in-depth knowledge and practical insights into the MLOps field.
View
Machine Learning Engineering
on Amazon
Show steps
-
Purchase or borrow the book.
-
Read and understand the concepts presented in the book.
-
Take notes and highlight important sections.
Attend a workshop on MLOps tools and techniques
Show steps
Attend a workshop to enhance your practical knowledge of MLOps tools and techniques.
Browse courses on
Machine Learning Tools
Show steps
-
Identify and register for a relevant workshop.
-
Attend the workshop and actively participate in the activities.
-
Network with other attendees and learn from their experiences.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow online tutorials on Rust for MLOps
Show steps
Follow online tutorials to enhance your understanding of Rust and its applications in MLOps.
Show steps
-
Identify relevant online tutorials.
-
Follow the tutorials and complete the exercises.
-
Build small projects using Rust for MLOps.
Solve optimization problems
Show steps
Practice solving optimization problems to strengthen your problem-solving and analytical skills.
Browse courses on
Optimization
Show steps
-
Review basic optimization concepts.
-
Identify and formulate optimization problems.
-
Solve optimization problems using appropriate techniques.
-
Analyze and interpret optimization results.
Build a machine learning model pipeline
Show steps
Build a machine learning model pipeline to practice and reinforce the concepts learned in the course.
Browse courses on
Machine Learning Pipeline
Show steps
-
Define the problem and gather data.
-
Choose and train a machine learning model.
-
Evaluate and refine the model.
-
Deploy the model to a production environment.
-
Monitor and maintain the model.
Participate in a Kaggle competition related to MLOps
Show steps
Participate in a Kaggle competition to apply your MLOps skills and gain experience in solving real-world problems.
Browse courses on
Kaggle Competitions
Show steps
-
Identify a relevant Kaggle competition.
-
Build a machine learning model and pipeline.
-
Submit your solution and track your progress.
-
Analyze the results and learn from your experience.
Write a blog post about MLOps best practices
Show steps
Write a blog post to share your knowledge and insights on MLOps best practices, reinforcing your understanding of the concepts.
Show steps
-
Research and gather information on MLOps best practices.
-
Organize and outline your content.
-
Write and edit your blog post.
-
Publish and promote your blog post.
Review basic probability and statistics concepts
Show steps
Review basic probability and statistics concepts to strengthen your foundation for machine learning.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Revisit relevant textbooks or online resources.
- Solve practice problems to test your understanding.
Read 'Machine Learning Engineering' by Andriy Burkov
Show steps
Read 'Machine Learning Engineering' to gain in-depth knowledge and practical insights into the MLOps field.
View
Machine Learning Engineering
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow the book.
- Read and understand the concepts presented in the book.
- Take notes and highlight important sections.
Attend a workshop on MLOps tools and techniques
Show steps
Attend a workshop to enhance your practical knowledge of MLOps tools and techniques.
Browse courses on
Machine Learning Tools
Show steps
Show steps
Show steps
- Identify and register for a relevant workshop.
- Attend the workshop and actively participate in the activities.
- Network with other attendees and learn from their experiences.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow online tutorials on Rust for MLOps
Show steps
Follow online tutorials to enhance your understanding of Rust and its applications in MLOps.
Show steps
Show steps
Show steps
- Identify relevant online tutorials.
- Follow the tutorials and complete the exercises.
- Build small projects using Rust for MLOps.
Solve optimization problems
Show steps
Practice solving optimization problems to strengthen your problem-solving and analytical skills.
Browse courses on
Optimization
Show steps
Show steps
Show steps
- Review basic optimization concepts.
- Identify and formulate optimization problems.
- Solve optimization problems using appropriate techniques.
- Analyze and interpret optimization results.
Build a machine learning model pipeline
Show steps
Build a machine learning model pipeline to practice and reinforce the concepts learned in the course.
Browse courses on
Machine Learning Pipeline
Show steps
Show steps
Show steps
- Define the problem and gather data.
- Choose and train a machine learning model.
- Evaluate and refine the model.
- Deploy the model to a production environment.
- Monitor and maintain the model.
Participate in a Kaggle competition related to MLOps
Show steps
Participate in a Kaggle competition to apply your MLOps skills and gain experience in solving real-world problems.
Browse courses on
Kaggle Competitions
Show steps
Show steps
Show steps
- Identify a relevant Kaggle competition.
- Build a machine learning model and pipeline.
- Submit your solution and track your progress.
- Analyze the results and learn from your experience.
Write a blog post about MLOps best practices
Show steps
Write a blog post to share your knowledge and insights on MLOps best practices, reinforcing your understanding of the concepts.
Show steps
Show steps
Show steps
- Research and gather information on MLOps best practices.
- Organize and outline your content.
- Write and edit your blog post.
- Publish and promote your blog post.
Career center
Learners who complete DevOps, DataOps, MLOps will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/4yo90n/devops
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For more information about how these books relate to this course, visit:
OpenCourser.com/course/4yo90n/devops
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Effort
3 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructor
Noah Gift
Session
March 19, 2024
- March 20, 2025
Language
English
Transcript
English
Good to know
Develops end-to-end MLOps solutions through hands-on practice
Provides a holistic approach to MLOps, covering DevOps, DataOps, and MLOps
Leverages cutting-edge tools and technologies, including GitHub Copilot, Gradio, and Hugging Face
Instructors Noah Gift are experienced professionals in the field of MLOps
Suitable for data scientists, software engineers, analysts, and professionals working with machine learning
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