Certified MLOps Engineer

Certified MLOps Engineer

$200.00

Get certified in the most required skill for developing machine learning applications.

  • Get certified for your knowledge
  • Work on projects and get experience certificate

Description

This certification-oriented course covers the fundamentals of MLOps, the practice of applying software engineering best practices to machine learning development. Attendees will learn to build scalable and efficient machine learning pipelines, automate model deployment, and integrate with production systems. Topics include version control, containerization, continuous integration and delivery, monitoring, and experiment tracking. 

Through hands-on exercises and real-world case studies, attendees will gain experience with popular MLOps tools and frameworks such as Docker, Kubernetes, TensorFlow, and PyTorch. Upon completion of this course, attendees will have the skills to bring machine learning models into production with greater speed, reliability, and scalability.

Program Outcome

  • Get job ready with in-depth understanding of MLOps best practices
  • Collaboration with different stakeholders in the organization
  • Implementing security measures to protect the data and models.

By the end of the course, learners will be equipped with the skills and knowledge necessary to deploy and manage machine learning models in production environments, and contribute to an effective MLOps strategy.

Prerequisite

  • Basic knowledge of machine learning
  • Basic knowledge of software engineering and cloud frameworks
  • Intermediate-level knowledge of python programming

MLOps & AutoML Training Outline

    • Overview of MLOps and AutoML concepts.
    • Importance in the machine learning lifecycle.

     

    • Installing essential tools and libraries.
    • Configuring version control (e.g., Git).

     

    • Creating a simple ML pipeline.
    • Integrating version control into the workflow.

     

    • Model monitoring and management.
    • Scalability and orchestration in MLOps.

     

    • Understanding AutoML and its benefits.
    • Exploring popular AutoML platforms.

     

    • Strategies for hyperparameter tuning.
    • Techniques for effective feature engineering.

     

    • Different approaches to model deployment.
    • Deploying models in a production environment.

     

    • Setting up monitoring for deployed models.
    • Troubleshooting and optimizing models.

     

    • Tailoring AutoML workflows to specific needs.
    • Advanced AutoML customization.

     

    • Participants work on real-world projects.
    • Project presentations and feedback.

     

FAQs

  • This course is suitable for beginners as well as professionals working in different domains.

  • This course has different modes of delivery. It can be taken in person from a center over the weeks, or it can be taken online as well based on your own convenience.

  • Yes, an instructor will be there for in-person delivery and online (instructor-led) delivery.

  • Basic understanding of machine learning, software engineering, cloud technology and python are required to attend this course.

  • The course has beginner-level difficulty.

  • Machine Learning Engineer, ML Architect, AI Architect, Solution Architect etc. roles can be explored after taking this course.

  • All attendees who complete all sections of the course along with the capstone project will get the certificate.

  • There are MCQ-based short tests given after each section to assess the understanding of a learner in each section. Finally, the capstone project is evaluated manually by the instructors.

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