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Become a Machine Learning Engineer. Level-up your programming skills with MLOps
Instructors: Noah Gift
16,972 already enrolled
Included with
(227 reviews)
Recommended experience
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).
(227 reviews)
Recommended experience
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).
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.
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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.
Applied Learning Project
Explore and practice your MLOps skills with hands-on practice exercises and Github repositories.
1. Building a Python script to automate data preprocessing and feature extraction for machine learning models.
2. Developing a real-world ML/AI solution using AI pair programming and GitHub Copilot, showcasing your ability to collaborate with AI.
4. Creating web applications and command-line tools for ML model interaction using Gradio, Hugging Face, and the Click framework.
3. Implementing GPU-accelerated ML tasks using Rust for improved performance and efficiency.
4. Training, optimizing, and deploying ML models on Amazon SageMaker and Azure ML for cloud-based MLOps.
5. Designing a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.
6. Fine-tuning and deploying Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face. Creating interactive demos to effectively showcase your work and advancements.
Work with logic in Python, assigning variables and using different data structures.
Write, run and debug tests using Pytest to validate your work.
Interact with APIs and SDKs to build command-line tools and HTTP APIs to solve and automate Machine Learning problems.
Build operations pipelines using DevOps, DataOps, and MLOps
Explain the principles and practices of MLOps (i.e., data management, model training and development, continuous integration and delivery, etc.)
Build and deploy machine learning models in a production environment using MLOps tools and platforms.
Apply exploratory data analysis (EDA) techniques to data science problems and datasets.
Build machine learning modeling solutions using both AWS and Azure technology.
Train and deploy machine learning solutions to a production environment using cloud technology.
Create new MLflow projects to create and register models.
Use Hugging Face models and datasets to build your own APIs.
Package and deploy Hugging Face to the Cloud using automation.
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
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The course series takes approximately 6 months to complete.
You should have basic Python programming experience, familiarity with computer science concepts, and a strong foundation in mathematics (especially linear algebra and statistics).
The course series is designed to be completed in the order outlined here on this Specialization Description Page.
Note that the Specialization Certificate does not represent official academic credit from the partner institution offering the course. Duke cannot provide a transcript for your completion of the Specialization; however, we encourage you to share your Coursera completion certificate with your employer and community to demonstrate your completion of the course series.
Through this series, you will begin to learn skills for various career paths:
Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.
Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems.
Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner.
Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
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