This course is part of the MLOps | Machine Learning Operations Specialization

Noah Gift +1 more instructor

Offered By

MLOps | Machine Learning Operations SpecializationDuke University

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 2 of 4 in the

MLOps | Machine Learning Operations Specialization

Advanced Level

Intermediate experience in working with Python, Git for version control, Docker for containerization and Kubernetes for deployment and scaling.

Approx. 26 hours to complete

English

What you will learn

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.

Skills you will gain

Devops
Python Libraries
Machine Learning
Big Data
Rust Programming

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 2 of 4 in the

MLOps | Machine Learning Operations Specialization

Advanced Level

Intermediate experience in working with Python, Git for version control, Docker for containerization and Kubernetes for deployment and scaling.

Approx. 26 hours to complete

English

Instructors

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Interdisciplinary Data Science (MIDS)

44,599 Learners

12 Courses

Alfredo Deza

Adjunct Assistant Professor in the Pratt School of Engineering

22,545 Learners

8 Courses

Offered by

Duke University

Syllabus - What you will learn from this course

Week1

Week 1

4 hours to complete

Week 1: Introduction to MLOps

4 hours to complete

22 videos (Total 121 min)

22 videos

Introduction to MLOps4mMLOps Background2mMLOps Trends and Techniques13mWhat is DevOps?2mWhat is DataOps?1mMLOPs: Heavy vs Light3mMLOps: Hierarchy of Needs3mData Poisoning Machine Learning Systems2mWhat are the Key Components in MLOPs?3mConsidering the MLOps Maturity Models4mWhat is Continuous Integration?32mWhat is Continuous Delivery?2mWhat is a Feature Store?2mWhat is Data Drift?1mOperationalizing a Microservice1mCI for Microservices7mEnd to End MLOps HuggingFace Spaces11mApp Runner Example5mFlask Example3mBuilding Golang GCP App Engine Microservice5mGetting Started with Makefile2mThe Three Most Important Files in a Python Project3m

1 practice exercise

Key Concepts in MLOps30m

Week2

Week 2

5 hours to complete

Week 2: Essential Math and Data Science

5 hours to complete

5 videos (Total 141 min)

Week3

Week 3

7 hours to complete

Week 3: Operations Pipelines: DevOps, DataOps, MLOps

7 hours to complete

20 videos (Total 308 min)

20 videos

Cloud Developer Workspace Advantage4mKey Components of GitHub Ecosystem3mUsing GitHub Templates2mDemo of GitHub Codespaces6mGPU Code Whisperer1mFine-Tuning with Hugging Face3mDemo of GitHub Copilot8mGitHub Actions3mPipelines for DataOps using Step Functions16mQuery Databricks Pipeline26mBuilding Data Ingestion Pipelines on AWS2mMarco Polo Step Functions 8mTransforming Data in Transit on AWS2mDemo AWS Batch Service3mServerless Data Engineering Pipelines on AWS1mBuilding Python Functions from Zero 2h 18mBuilding a Python NLP Project with Python Fire 43mExtending Google Cloud Functions10mUsing Google Cloud Functions6mDeploying a Rust Azure Function with GitHub Actions14m

1 practice exercise

Operations Pipelines: DevOps, DataOps, MLOps30m

Week4

Week 4

8 hours to complete

End to End MLOps and AIOps

8 hours to complete

12 videos (Total 398 min)

12 videos

Containerized Microservices2mContainerized Continuous Delivery8mContainerized Machine Learning39mContainerized End-to-End Machine Learning39mBuilding Distroless Containers8mUse AI to Write AI1mLearn Key Skills for Python DevOps with Copilot2h 51mAmazon CodeWhisperer vs. GitHub Copilot56mEnabling AI Workflows1mPrototyping AI APIs14mUsing Transfer Learning2mAssimilate OpenAI Technology using Streamlit50m

1 practice exercise

End to End Containerized MLOps30m

About the MLOps | Machine Learning Operations Specialization

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
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.

More questions? Visit the Learner Help Center.

DevOps, DataOps, MLOps

About this Course

What you will learn

Skills you will gain