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Overview
This Machine Learning Operations course course is offered by Coursera in partnership with Duke University.
What you'll learn
Master Python fundamentals, MLOps principles, and data management to build and deploy ML models in production environments.
Utilize Amazon Sagemaker / AWS, Azure, MLflow, and Hugging Face for end-to-end ML solutions, pipeline creation, and API development.
Fine-tune and deploy Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face.
Design a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.
Skills you'll gain
- Microsoft Azure
- Big Data
- Data Analysis
- Python Programming
- Github
- Machine Learning
- Cloud Computing
- Data Management
- Devops
- Amazon Web Services (Amazon AWS)
- Rust Programming
- MLOps
Programme Structure
Courses included:
- Python Essentials for MLOps
- DevOps, DataOps, MLps
- MLOps Platforms: Amazon SageMaker and Azure ML
- MLOps ToolsL MLflow and Hugging Face
Key information
Duration
- Part-time
- 6 months
- 5 hrs/week
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Campus Location
- Mountain View, United States
Disciplines
Artificial Intelligence Machine Learning View 203 other Short Courses in Machine Learning in United StatesWhat students do after studying
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
- Advanced level
- You should have basic Python programming experience, familiarity with computer science concepts, and a strong foundation in mathematics (especially linear algebra and statistics).
Tuition Fees
Additional Details
Course is free for the first 7 days. After 7 days, the course can be accessed with the Coursera Plus Subscription
Funding
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project
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