Duke University - DevOps, DataOps, MLOps
- Offered byCoursera
DevOps, DataOps, MLOps at Coursera Overview
Duration | 26 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
DevOps, DataOps, MLOps at Coursera Highlights
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Coursera Labs Includes hands on learning projects. Learn more about Coursera Labs External Link
- Advanced Level Intermediate experience in working with Python, Git for version control, Docker for containerization and Kubernetes for deployment and scaling.
- Approx. 26 hours to complete
- English Subtitles: English
DevOps, DataOps, MLOps at Coursera Course details
- 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.
DevOps, DataOps, MLOps at Coursera Curriculum
Week 1: Introduction to MLOps
Introduction to MLOps
MLOps Background
MLOps Trends and Techniques
What is DevOps?
What is DataOps?
MLOPs: Heavy vs Light
MLOps: Hierarchy of Needs
Data Poisoning Machine Learning Systems
What are the Key Components in MLOPs?
Considering the MLOps Maturity Models
What is Continuous Integration?
What is Continuous Delivery?
What is a Feature Store?
What is Data Drift?
Operationalizing a Microservice
CI for Microservices
End to End MLOps HuggingFace Spaces
App Runner Example
Flask Example
Building Golang GCP App Engine Microservice
Getting Started with Makefile
The Three Most Important Files in a Python Project
Key Concepts in MLOps
Week 2: Essential Math and Data Science
Doing Data Science Your First Day
What is Colab?
Understanding the Traveling Salesman Problem (TSP)
Simulations vs. Experiment Tracking
Machine Learning and AI in Practice with Clustering
Essential Math and Data Science
Week 3: Operations Pipelines: DevOps, DataOps, MLOps
Cloud Developer Workspace Advantage
Key Components of GitHub Ecosystem
Using GitHub Templates
Demo of GitHub Codespaces
GPU Code Whisperer
Fine-Tuning with Hugging Face
Demo of GitHub Copilot
GitHub Actions
Pipelines for DataOps using Step Functions
Query Databricks Pipeline
Building Data Ingestion Pipelines on AWS
Marco Polo Step Functions
Transforming Data in Transit on AWS
Demo AWS Batch Service
Serverless Data Engineering Pipelines on AWS
Building Python Functions from Zero
Building a Python NLP Project with Python Fire
Extending Google Cloud Functions
Using Google Cloud Functions
Deploying a Rust Azure Function with GitHub Actions
Operations Pipelines: DevOps, DataOps, MLOps
End to End MLOps and AIOps
Containerized Microservices
Containerized Continuous Delivery
Containerized Machine Learning
Containerized End-to-End Machine Learning
Building Distroless Containers
Use AI to Write AI
Learn Key Skills for Python DevOps with Copilot
Amazon CodeWhisperer vs. GitHub Copilot
Enabling AI Workflows
Prototyping AI APIs
Using Transfer Learning
Assimilate OpenAI Technology using Streamlit
End to End Containerized MLOps
Rust for MLOps: The Practical Transition from Python to Rust
Introduction to Switching to Rust from Python
Introduction to Rust Lecture Notes
Configure Rust for AWS Cloud9
GitHub Copilot Enabled Rust Programming
Using Rust Packaging for Web Development
Comparing Energy Efficiency of Rust vs. Python
Comparing Rust vs. Python for MLOps
Continuous Integration for Rust with GitHub Actions
Demo Unit Testing Rust
Building a Deduplication Tool with Rust
Zero Shot Classification Rust Hugging Face
Rust GPU Hugging Face Translator
PyTorch Stable Diffusion Rust with GPU
Rust PyTorch Demo
Building GPU Stress Test
Using Rust ONNX with EFS for AWS Lambda
Onboarding to GCP with Python and Rust via CloudShell
Run Rust Actix Microservice with Google Cloud Run
Build and Deploy Rust Microservice via Google Cloud Run
Monitoring and Logging with Rust for Google App Engine
Load Testing a Rust Microservice
Building a Containerized Rust Microservice with AWS
AWS Step Functions with Rust
Deploy an App Engine Rust Microservice
Size Calculator in AWS S3
Rust for MLOps
DevOps, DataOps, MLOps at Coursera Admission Process
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