Student Success
Bootcamp Programs
Short Courses
Portfolio Courses
Bootcamp Programs

Launch your career in Data and AI through our bootcamp programs

  • Industry-leading curriculum
  • Real portfolio/industry projects
  • Career support program
  • Both Full-time & Part-time options.
Data Science Bootcamp

Become a data engineer by learning how to build end-to-end data pipelines


Become a data analyst through building hands-on data/business use cases

Become an AI/ML engineer by getting specialized in deep learning, computer vision, NLP, and MLOps

Become a DevOps Engineer by learning AWS, Docker, Kubernetes, IaaS, IaC (Terraform), and CI/CD

Short Courses

Improve your data & AI skills through self-paced and instructor-led courses

  • Industry-leading curriculum
  • Portfolio projects
  • Part-time flexible schedule
Portfolio Courses

Learn to build impressive data/AI portfolio projects that get you hired

  • Portfolio project workshops
  • Work on real industry data & AI project
  • Job readiness assessment
  • Career support & job referrals

Build data strategies and solve ML challenges for real clients

Help real clients build BI dashboard and tell data stories

Build end to end data pipelines in the cloud for real clients


Choose to learn at your comfort home or at one of our campuses

Corporate Partners

We’ve partnered with many companies on corporate upskilling, branding events, talent acquisition, as well as consulting services.

AI/Data Transformations with our customized and proven curriculum

Do you need expert help on data strategies and project implementations? 

Hire Data, AI, and Engineering talents from WeCloudData

Student Success

Meet our amazing alumni working in the Data industry

Read our students’ stories on how WeCloudData have transformed their career


Check out our events and blog posts to learn and connect with like-minded professionals working in the industry

Read blogs and updates from our community and alumni

Explore different Data Science career paths and how to get started

Our free courses and workshops gives you the skills and knowledge needed to transform your career in tech


ML Engineering

According to, only 22% of companies using machine learning have successfully deployed a model. The need for ML Engineers is growing exponentially as the industry moves towards data-centric AI.

ML Engineering (MLOps) is at the intersection of Machine Learning, DevOps, and Data Engineering. It is a critical role that ensures AI products get deployed in production in a scalable and reliable way.

If you want to take your ML skills to the next level, WeCloudData has good news for you. Our ML Engineer Certificate program is created for professionals like you who want to sharpen their skills in deep learning, computer vision, NLP, big data, and MLOps.

Unsure which path to take?
Become a Machine Learning Engineer

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ML Engineering

Many AI projects fail due to the lack of MLOps expertise to help put ML in production. Companies realize that building successful AI products requires data engineers, scientists, ML engineers, and product managers to work in a team.

ML Engineers play an essential role in putting models in production and ensuring models can be continuously integrated and deployed (CI/CD) with high-quality data (data-centric AI).

If you currently work as a Data Scientist, Software Engineer, or DevOps professional, ML Engineer could be a great natural progression.

MLOps’ most important task is to make high quality data available through all stages of the ML project lifecycle.

-Andrew Ng on data-centric AI-

Common skill sets





Source: Indeed

ML in production is more than just the code

Source: Google NIPS Paper

common tools


What do ML Engineers do?

On a typical day, an ML Engineer may be involved in the following tasks:

  • Use ML frameworks such as Tensorflow and PyTorch to build ML pipelines
  • Work with distributed ML engines such as Spark ML to scale ML model training
  • Automate ML and data pipelines using Apache Airflow
  • Build reproducible models using Docker, DVC, and MLflow
  • Deploy and orchestrate ML pipelines to Kubernetes
  • Create the infrastructure for model-serving in production
  • Build infrastructure to continuously monitor model performance and detect feature drift in production

Who can become an ML Engineer?

ML Engineering has a higher entry bar. An ideal candidate will have a solid software/programming background, decent knowledge in data science and machine learning, as well as experience with CI/CD and Data Engineering. 

It may sound scary to beginners. The reality is that it’s very hard to find the ideal candidates. So as long as you are a relentless learner who can demonstrate great desire for learning, strong aptitude for problem solving, and the dedication to make it happen. 

If you are a data scientist, then learning data engineering, DevOps, and the principles of software engineering is the key. 

If you are coming from a DevOps background, learning AI frameworks, improving data wrangling skills, and knowing the difference between AI pipelines and traditional software CI/CD pipelines are important. 

How much do ML Engineers earn?

The average base pay for ML Engineers is between $110k and $130k in the U.S. It is the highest in different data & AI roles. 


ML Engineering Courses

Bootcamp Programs

This instructor-led part-time program gives you the opportunity to learn from the industry’s best and practice what you learn through hands-on projects. You will build awesome portfolios and receive job support for 6 months after graduation.

Machine Learning Engineering
Full-time, Part-time
| 6 months
Online Live
Short Courses

Short-term online live courses that focus on specific data skills. Learn in-demand data analytics, data science, data engineering, or AI skills with industry experts in the evening, or on weekends.

Machine Learning Engineering
6 weeks

Special discount for bundles

Talk to our Program Advisor


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Machine learning applications in healthcare was a great hit with the NYC audience. At least 130 enthusiastic attendees joined…
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