- Microsoft Azure
- Big Data
- Data Analysis
- Python Programming
- Github
- Machine Learning
- Cloud Computing
- Data Management
- Devops
- Amazon Web Services (Amazon AWS)
- Rust Programming
- MLOps
MLOps | Machine Learning Operations
Completed by Jayabharathi Hari
June 18, 2024
Approximately 6 months at 5 hours a week to completeJayabharathi Hari's account is verified. Coursera certifies their successful completion of Duke University MLOps | Machine Learning Operations Specialization.
Course Certificates Completed
Python Essentials for MLOps
DevOps, DataOps, MLOps
MLOps Platforms: Amazon SageMaker and Azure ML
MLOps Tools: MLflow and Hugging Face
What you will 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 will gain
Course Certificates
Earned after completing each course in the Specialization
Python Essentials for MLOps
Duke University
Taught by: Noah Gift & Alfredo Deza
Completed by: Jayabharathi Hari by June 17, 2024
5 weeks of study, 4-6 hours/week
DevOps, DataOps, MLOps
Duke University
Taught by: Noah Gift & Alfredo Deza
Completed by: Jayabharathi Hari by June 17, 2024
5 weeks of study, 3-5 hours/week
MLOps Platforms: Amazon SageMaker and Azure ML
Duke University
Taught by: Noah Gift & Alfredo Deza
Completed by: Jayabharathi Hari by June 18, 2024
5 weeks of study, 3-5 hours/week
MLOps Tools: MLflow and Hugging Face
Duke University
Taught by: Noah Gift & Alfredo Deza
Completed by: Jayabharathi Hari by June 18, 2024
4 weeks of study, 3-5 hours/week