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DevOps, DataOps, MLOps

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.

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What's inside

Syllabus

Week 1: Introduction to MLOps

This week you will learn how to apply foundational skills in MLOps to build machine learning solutions and apply it by building microservices in Python.

Week 2: Essential Math and Data Science

This week you will learn how to apply essential skills in math and data science for MLOps and apply it by building simulations.

Week 3: Operations Pipelines: DevOps, DataOps, MLOps

This week you will learn how to build operations pipelines and then apply these skills by building solutions for pre-trained Hugging Face models.

End to End MLOps and AIOps

This week you will learn how to build end to end MLOps and AIOps solutions and apply it by building solutions with pre-trained models from OpenAI while benefiting from using AI Pair Programming tools like GitHub Copilot.

Rust for MLOps: The Practical Transition from Python to Rust

This week, you will learn how to switch from Python to Rust, a powerful and efficient systems programming language. This week will cover various practical applications of Rust, such as CLI, Web, and MLOps solutions, as well as cloud computing solutions for AWS, GCP, and Azure. You'll also learn how to build Rust solutions for Kubernetes, Docker, Serverless, Data Engineering, Data Science, and Machine Learning Operations (MLOps). By the end of this week, you will have a strong understanding of Rust's key syntax and features, and be able to leverage Rust for GPU-accelerated machine learning tasks.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores MLOps technologies and strategies, which is standard industry practice

Provides foundational skills in Python, math, and data science, which are core skills for many data-centric careers

Focuses on practical applications of MLOps, DevOps, DataOps, and AIOps

Integrates with GitHub, Hugging Face, and OpenAI, which are widely used tools in the MLOps industry

Covers Rust, a language gaining traction in MLOps for its efficiency in systems programming

Teaches end-to-end MLOps solutions, preparing learners for real-world MLOps pipelines

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in DevOps, DataOps, MLOps with these activities:

Review essential math and data science concepts

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A quick review solidifies essential knowledge and prepares you for the course's quantitative aspects.

Browse courses on Math

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Review online resources or textbooks on math and data science
Focus on concepts such as statistics, probability, and linear algebra
Practice solving problems and applying these concepts

Install Python with PyPI

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This activity will help prepare you to run the code and follow along with the course.

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Follow the official Python installation guide

Brush up on Python syntax

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A quick Python syntax review ensures that you're ready to hit the ground running when the course begins.

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Review online tutorials
Refer to Python documentation
Practice writing simple Python programs

Ten other activities

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Exercises on Jupyter Notebooks

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Regular practice on Jupyter Notebooks will help you build a strong foundation in Python and machine learning.

Browse courses on Python

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Follow the tutorials on Jupyter Notebooks
Attempt solving beginner-level exercises
Participate in online forums for Q&A

Gather resources on MLOps best practices

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A curated collection of MLOps resources serves as a valuable reference throughout the course.

Browse courses on MLOps

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Identify and collect articles, tutorials, and documentation on MLOps best practices
Organize the resources into a central repository or online collection
Share the compilation with peers or contribute it to an online community

Build a Simple Machine Learning Model in Python

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These tutorials will give you hands-on experience in building basic machine learning models, which is essential for this course.

Browse courses on Python

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Follow a tutorial on building a simple machine learning model in Python
Experiment with different models and algorithms
Share your results and insights online

Collaborate on MLOps projects

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Peer collaboration enhances problem-solving and deepens understanding of MLOps concepts.

Browse courses on MLOps

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Form study groups with peers
Identify and collaborate on MLOps projects
Share insights and provide constructive feedback

Discussion on Metrics for Machine Learning Models

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Engage in discussions with peers to develop a deep understanding of different metrics used to evaluate machine learning models.

Browse courses on Machine Learning

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Join a study group or online forum
Participate in discussions on best practices
Share your knowledge and insights

Practice MLOps pipeline optimization

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Additional MLOps practice enhances problem-solving skills and reinforces concepts.

Browse courses on MLOps

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Identify areas for optimization in MLOps pipelines
Implement optimization techniques using Python or Rust
Test and evaluate the optimized pipelines

Explore Rust programming for MLOps

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Targeted Rust tutorials complement the course's transition from Python to Rust.

Browse courses on Rust

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Identify suitable Rust tutorials for MLOps
Follow the tutorials and implement Rust solutions for MLOps tasks
Experiment with Rust features for GPU-accelerated ML

Contribute to Open-Source MLOps Projects

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Contribute to open-source MLOps projects to gain practical experience and enhance your portfolio.

Browse courses on MLOps

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Identify open-source MLOps projects that align with your interests
Start contributing by fixing bugs or adding features
Collaborate with other developers and maintainers

Build a containerized ML solution

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Hands-on containerization experience solidifies understanding of deployment and packaging.

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Design the ML solution architecture
Implement the solution using appropriate frameworks and tools
Containerize the solution using Docker or Kubernetes
Deploy and test the containerized solution

Develop an MLOps solution for a real-world problem

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A comprehensive MLOps project provides a practical application of skills and reinforces learning.

Browse courses on MLOps

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Define the real-world problem and identify the appropriate solution
Design and implement the MLOps solution using best practices
Evaluate the solution's performance and make necessary adjustments
Document the project and present your findings

Career center

Learners who complete DevOps, DataOps, MLOps will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use machine learning and other techniques to extract insights from data. This course will help you develop the skills needed to succeed in this role, including data analysis, machine learning, and cloud computing.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and maintaining machine learning models. This course will help you build a foundation in the skills and knowledge needed to succeed in this role, including model building, data analysis, and cloud computing.

See salaries and explore the career path for Machine Learning Engineer

DevOps Engineer

DevOps Engineers work to bridge the gap between development and operations teams. This course will help you develop the skills needed to succeed in this role, including continuous integration, continuous delivery, and cloud computing.

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Data Analyst

Data Analysts use data to make informed decisions. This course will help you develop the skills needed to succeed in this role, including data analysis, data visualization, and cloud computing.

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Cloud Architect

Cloud Architects design, develop, and maintain cloud computing systems. This course will help you develop the skills needed to succeed in this role, including cloud computing, networking, and security.

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AI Engineer

AI Engineers design, develop, and maintain artificial intelligence systems. This course will help you develop the skills needed to succeed in this role, including machine learning, deep learning, and natural language processing.

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Big Data Engineer

Big Data Engineers work with large datasets to extract insights. This course will help you develop the skills needed to succeed in this role, including data analysis, data mining, and cloud computing.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course will help you build a foundation in the skills and knowledge needed to succeed in this role, including object-oriented programming, data structures, and algorithms.

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Data Engineer

Data Engineers design, build, and maintain data pipelines. This course will help you develop the skills needed to succeed in this role, including data engineering, cloud computing, and machine learning.

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Business Analyst

Business Analysts work with businesses to identify and solve problems. This course will help you develop the skills needed to succeed in this role, including data analysis, problem solving, and cloud computing.

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Product Manager

Product Managers work with teams to develop and launch new products. This course will help you develop the skills needed to succeed in this role, including data analysis, product management, and cloud computing.

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Project Manager

Project Managers work with teams to plan and execute projects. This course will help you develop the skills needed to succeed in this role, including project management, communication, and cloud computing.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to make investment decisions. This course will help you develop the skills needed to succeed in this role, including machine learning, data analysis, and cloud computing.

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Research Scientist

Research Scientists conduct research in a variety of fields, including machine learning, artificial intelligence, and cloud computing. This course will help you develop the skills needed to succeed in this role, including research methods, data analysis, and cloud computing.

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Systems Engineer

Systems Engineers design, build, and maintain computer systems. This course will help you develop the skills needed to succeed in this role, including systems engineering, cloud computing, and machine learning.

See salaries and explore the career path for Systems Engineer

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in DevOps, DataOps, MLOps.

The Rust Programming Language, 2nd Edition

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Is the official Rust programming language documentation, providing a comprehensive and authoritative reference for Rust syntax, semantics, and libraries. It is an essential resource for anyone who wants to learn Rust or use it in their projects.

DevOps, DataOps, MLOps

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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