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

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

     

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

     

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

     

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

     

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

     

  • Hyperparameter Tuning and Feature Engineering
    • Strategies for hyperparameter tuning.
    • Techniques for effective feature engineering.

     

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

     

  • Monitoring Model Performance
    • Setting up monitoring for deployed models.
    • Troubleshooting and optimizing models.

     

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

     

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

     

FAQs

  • Who can attend this course?

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

  • What is the mode of delivery?

    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.

  • Will there be an instructor also in this course?

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

  • What are the prerequisites to attend this course?

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

  • How difficult is this course to learn?

    The course has beginner-level difficulty.

  • What job roles can i take if i attend this course?

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

  • Who can get the certificate?

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

  • How the completeness of each section is measured in the course?

    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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