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Bootcamp Programs
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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


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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

Machine Learning Engineering as a career path

Inquire about Machine Learning programs
Become a Machine Learning Engineer

Contact our advisors now to learn more about our programs and courses. They are here to answer all your questions and help you embark on a successful journey.

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Education and Foundational Skills:

Begin by acquiring a strong foundation in mathematics, statistics, computer science, understand machine learning algorithms, programming and  build projects that provides solution to real-world challenges.

Entry-Level Roles:

Start your career as a Junior Machine Learning Engineer or Data Scientist. In these roles, you will work on projects under the guidance of more experienced professionals. Gain hands-on experience in implementing machine learning models, data preprocessing, feature engineering, and model evaluation.

Model Development and Training:

As you gain experience, you will take on more responsibilities in developing and training machine learning models. You will work on selecting appropriate algorithms, tuning hyperparameters, and optimizing models for performance and accuracy. Collaborate with cross-functional teams to gather requirements and understand the business context for the models.

Deployment and Productionization:

Progress to roles where you focus on deploying machine learning models into production systems. This involves working closely with software engineers and MLOps teams to ensure the models are integrated into scalable and efficient production environments. You will be responsible for monitoring the models’ performance, addressing issues, and ensuring reliable and real-time inference.

Leadership and Project Management:

As you gain expertise, you may advance to leadership roles, such as Machine Learning Engineering Manager or Technical Lead. In these positions, you will guide and mentor junior team members, lead projects, and make strategic decisions regarding technology adoption, resource allocation, and team growth.

MLOps developer:

The role of an MLOps developer is to bridge the gap between data science and IT operations. They focus on implementing and maintaining the infrastructure and processes required to deploy, monitor, and scale machine learning models in production. Their responsibilities include model deployment, version control, automated testing, monitoring, and collaboration with cross-functional teams.

Specialization and Research:

Along the career path, you may choose to specialize in specific domains such as Natural Language Processing (NLP), Computer Vision, or Forecasting. Pursue advanced courses, certifications, or research opportunities in these areas to deepen your expertise.

Check out other career guides

Our Career Guide provides all the resources you will need to help you get started in navigating data careers. We include free resources, guides, and tools to help you get started.

RECOMMENDED COURSES FOR Machine Learning Engineering