This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You'll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps
Through this series, you will begin to learn skills for various career paths:
1. Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.
2. Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems.
3. Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner.
4. Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.
Overview
Syllabus
Course 1: Python Essentials for MLOps
- Offered by Duke University. Python Essentials for MLOps (Machine Learning Operations) is a course designed to provide learners with the ... Enroll for free.
Course 2: DevOps, DataOps, MLOps
- Offered by Duke University. Learn how to apply Machine Learning Operations (MLOps) to solve real-world problems. The course covers ... Enroll for free.
Course 3: MLOps Platforms: Amazon SageMaker and Azure ML
- Offered by Duke University. InMLOps (Machine Learning Operations) Platforms: Amazon SageMaker and Azure MLyou will learn the necessary ... Enroll for free.
Course 4: MLOps Tools: MLflow and Hugging Face
- Offered by Duke University. This course covers two of the most popular open source platforms for MLOps (Machine Learning Operations): MLflow ... Enroll for free.
- Offered by Duke University. Python Essentials for MLOps (Machine Learning Operations) is a course designed to provide learners with the ... Enroll for free.
Course 2: DevOps, DataOps, MLOps
- Offered by Duke University. Learn how to apply Machine Learning Operations (MLOps) to solve real-world problems. The course covers ... Enroll for free.
Course 3: MLOps Platforms: Amazon SageMaker and Azure ML
- Offered by Duke University. InMLOps (Machine Learning Operations) Platforms: Amazon SageMaker and Azure MLyou will learn the necessary ... Enroll for free.
Course 4: MLOps Tools: MLflow and Hugging Face
- Offered by Duke University. This course covers two of the most popular open source platforms for MLOps (Machine Learning Operations): MLflow ... Enroll for free.
Courses
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In MLOps (Machine Learning Operations) Platforms: Amazon SageMaker and Azure ML you will learn the necessary skills to build, train, and deploy machine learning solutions in a production environment using two leading cloud platforms: Amazon Web Services (AWS) and Microsoft Azure. This course is also a great resource for individuals looking to prepare for AWS or Azure machine learning certifications or who are working (or seek to work) as data scientists, software engineers, software developers, data analysts, or other roles that use machine learning.
Through a series of hands-on exercises, you will gain an intuition for basic machine learning algorithms and practical experience working with these leading Cloud platforms. By the end of the course, you will be able to deploy machine learning solutions in a production environment using AWS and Azure technology.
Week 1. Explore data engineering with AWS technology. We’ll discuss topics such as getting started with machine learning on AWS, creating data repositories, and identifying and implementing solutions for data ingestion and transformation.
Week 2. Gain basic data science skills with AWS technology. You will learn data cleaning techniques, perform feature engineering, data analysis, and data visualization for machine learning. We’ll prioritize using serverless solutions that are available on AWS to make the process more efficient.
Week 3. Learn machine learning models with AWS technology. We’ll examine how to select appropriate models for the task at hand, choose hyperparameters, train models on the platform, and evaluate models.
Week 4. Learn MLOps with AWS: the final phase of putting machine learning into production. We’ll discuss topics such as operationalizing a machine learning model, deciding between CPU and GPU, and deploying and maintaining the model.
Week 5. Learn how to work with data and machine learning in a second leading Cloud-based platform: Azure ML. -
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. -
This course covers two of the most popular open source platforms for MLOps (Machine Learning Operations): MLflow and Hugging Face. We’ll go through the foundations on what it takes to get started in these platforms with basic model and dataset operations. You will start with MLflow using projects and models with its powerful tracking system and you will learn how to interact with these registered models from MLflow with full lifecycle examples. Then, you will explore Hugging Face repositories so that you can store datasets, models, and create live interactive demos.
By the end of the course, you will be able to apply MLOps concepts like fine-tuning and deploying containerized models to the Cloud. This course is ideal for anyone looking to break into the field of MLOps or for experienced MLOps professionals who want to improve their programming skills. -
Python Essentials for MLOps (Machine Learning Operations) is a course designed to provide learners with the fundamental Python skills needed to succeed in an MLOps role. This course covers the basics of the Python programming language, including data types, functions, modules and testing techniques. It also covers how to work effectively with data sets and other data science tasks with Pandas and NumPy. Through a series of hands-on exercises, learners will gain practical experience working with Python in the context of an MLOps workflow. By the end of the course, learners will have the necessary skills to write Python scripts for automating common MLOps tasks. This course is ideal for anyone looking to break into the field of MLOps or for experienced MLOps professionals who want to improve their Python skills.
Taught by
Alfredo Deza and Noah Gift
