This comprehensive course series focuses on equipping individuals with programming knowledge, such as software developers, data scientists, and researchers, with critical MLOps skills. Through this series, participants will acquire essential skills to succeed in various career paths, including data science, machine learning engineering, cloud ML solutions architect, and artificial intelligence (AI) product management.
The course covers a wide range of topics, including Python essentials for MLOps, DevOps, DataOps, and MLOps principles, MLOps platforms such as Amazon SageMaker and Azure ML, and MLOps tools like MLflow and Hugging Face. Participants will learn to master Python fundamentals, MLOps principles, and data management to build and deploy ML models in production environments. They will also gain hands-on experience with platforms like Amazon SageMaker, Azure ML, and MLflow, as well as Hugging Face for end-to-end ML solutions, pipeline creation, and API development. Additionally, the course delves into fine-tuning and deploying Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face, preparing participants for the evolving field of MLOps.
Offered by Duke University, this course series aims to help individuals become proficient in machine learning engineering and level-up their programming skills with MLOps. With a focus on practical application and real-world problem-solving, participants will be well-equipped to analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.
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This course series covers essential topics such as Python essentials for MLOps, DevOps, DataOps, MLOps principles, MLOps platforms including Amazon SageMaker and Azure ML, and MLOps tools such as MLflow and Hugging Face, providing participants with a comprehensive understanding of MLOps and its applications.
Python Essentials for MLOps: This module focuses on working with logic in Python, writing, running, and debugging tests using Pytest, and interacting with APIs and SDKs to build command-line tools and HTTP APIs to solve and automate Machine Learning problems.
DevOps, DataOps, MLOps: Participants will learn to build operations pipelines using DevOps, DataOps, and MLOps, and gain an understanding of the principles and practices of MLOps, including data management, model training and development, continuous integration, and delivery. They will also learn how to build and deploy machine learning models in a production environment using MLOps tools and platforms.
MLOps Platforms: Amazon SageMaker and Azure ML: This module covers applying exploratory data analysis (EDA) techniques to data science problems and datasets, building machine learning modeling solutions using both AWS and Azure technology, and training and deploying machine learning solutions to a production environment using cloud technology.
MLOps Tools: MLflow and Hugging Face: Participants will create new MLflow projects to create and register models, use Hugging Face models and datasets to build their own APIs, and package and deploy Hugging Face to the Cloud using automation.
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