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Introduction to GitHub Actions

Math for Machine Learning

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Introduction to Git and Version Control

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Data Wrangling with Python

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Introduction to MLOps

Big Data for Data Science

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LEARNING TRACK

MLOps Engineering Track

Advanced

Online

Self-paced

Master the end-to-end MLOps lifecycle and gain hands-on skills with tools like Docker, Git, and MLflow to build scalable, automated, and production-ready ML solutions.

About the Track

Embark on your MLOps Engineer journey with the MLOps Engineer Track. This learning track provides a comprehensive foundation in MLOps, covering essential skills in programming, data engineering for MLOps, and machine learning pipeline management. Through the track, learners build expertise in the MLOps lifecycle, mastering tools like Docker, Git, and MLflow to support scalable and reliable ML operations. The curriculum balances theory with practical application, emphasizing automation, data handling, and best practices.

Top data skills program voted by students and partners

Courses in this track

Python Fundamentals

Fundamental

By the end of this course, participants will be able to:

Comprehend Python syntax and foundational programming concepts.
Effectively use data structures and perform file operations.
Implement control flow statements to create logic-driven programs.
Design and use functions to promote code reusability and modular programming.
Apply object-oriented programming principles to design and implement classes.
Handle exceptions to build robust, error-resistant Python applications.

Introduction to Linux

Fundamental

By the end of this course, participants will be able to:

Understand the fundamentals of Linux operating systems and distributions.
Create and manage virtual machines
Use essential Linux commands
Basic Shell scripting and debugging

Introduction to Docker

Fundamental

By the end of this course, participants will be able to:

Understand the principles of containerization and its advantages.
Use Docker to create, manage, and deploy containers.
Create and push images to Docker Hub.
Configure and deploy multi-container applications using Docker Compose

Introduction to Git and Version Control

Fundamental

By the end of this course, participants will be able to:

Understand the fundamentals of Git and version control systems.
Manage local Git repositories and track changes effectively.
Collaborate on projects using GitHub and remote repositories.
Apply advanced Git techniques for collaboration using GitFlow and GitHub Flow.

AWS Fundamentals

Intermediate

By the end of this course, participants will be able to:

Understand core AWS services and cloud computing principles.
Set up and manage secure AWS accounts.
Deploy virtual servers and manage configurations with EC2.
Store and manage data securely with S3.
Build serverless applications using AWS Lambda and API Gateway.
Monitor, log, and troubleshoot with CloudWatch.

Machine Learning

Intermediate

By the end of this course, participants will be able to:

Understand and apply core machine learning concepts.
Leverage mathematical principles to solve machine learning problems and perform exploratory data analysis.
Preprocess and transform data through cleaning, feature engineering, and dimensionality reduction.
Implement, validate and optimize machine learning models. Explore advanced applications.

Introduction to MLOps

Advanced

By the end of this course, participants will be able to:

Understand MLOps principles, Python tools, and the ML pipeline lifecycle.
Use Ray, SageMaker, MLflow, and DVC for training, tracking, and versioning.
Package models with Pickle/ONNX and manage features with modern data tools.
Deploy models using serverless tech and build CI/CD pipelines.
Monitor model performance, detect drift, and automate retraining.
Explore LLMOps workflows, prompt engineering, and RAG pipelines.

This Learning Track include:

Multiple courses in one track — build skills step by step
Engaging video lessons + hands-on quizzes
Capstone projects to strengthen your skills
Certificate of Completion
Full access with your active subscription

Not the right path for you?

We also offer personalized and customized path that suits your learning and career goals. Talk to our advisors.

student success

What our graduates are saying

⭐️⭐️⭐️⭐️⭐️
The Learning Track was exactly what I needed. The courses were well-structured, the projects were practical, and I loved being able to learn on my own schedule. I feel so much more confident in my skills now – and my portfolio looks amazing!

OUR ALUMNI ARE WORKING AT

Common Questions

Find answers to your questions about the Learning Track

What is a Learning Track?

A Learning Track is a curated series of courses, projects, and assessments designed to help you master a specific skill set or career path.

How is a Learning Track different from a single course?

Learning Tracks include multiple courses plus capstone or portfolio projects, offering a more comprehensive and structured learning experience.

How long will I have access to the Learning Track?

Access is tied to your subscription. As long as your subscription is active, you can continue learning.

Do I get hands-on projects?

Yes, each Learning Track includes hands-on labs and a final capstone project to build your portfolio.

Will I get a certificate?

Absolutely. Once you complete the entire Learning Track, you’ll receive a certificate of completion.

Still have questions?

If you have other queries or specific concerns about our Learning Track Subscription, don’t hesitate to let us know. Your feedback is important to us, and we aim to provide the best support possible.

Your Learning Journey Awaits 🚀

Grow your skills, build projects you’ll be proud of, and unlock new opportunities — all at your pace.

WeCloudData is the leading data science and AI academy. Our blended learning courses have helped thousands of learners and many enterprises make successful leaps in their data journeys.

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