MLOps(ML Operations)

MLOps, or Machine Learning Operations, is a crucial practice that combines machine learning, DevOps, and data engineering to streamline the deployment and management of machine learning models in production. By automating workflows, enhancing collaboration, and ensuring robust monitoring, MLOps enables organizations to effectively scale their ML initiatives. It focuses on the entire ML lifecycle, from data preprocessing to model training, deployment, and maintenance, ensuring models remain accurate and reliable over time. Embracing MLOps leads to more efficient, reproducible, and manageable ML systems, driving better business outcomes and fostering innovation.

Our mission

To empower professionals with the skills and knowledge needed to seamlessly integrate machine learning models into production environments, ensuring efficient, scalable, and reliable operations. Our program aims to bridge the gap between data science and IT operations, fostering a culture of collaboration and innovation through cutting-edge tools and best practices.

Our vision

To be a leading provider of MLOps education, driving the evolution of machine learning deployment and management. We envision a future where MLOps is a standard practice, enabling organizations to unlock the full potential of their data-driven initiatives, achieving greater efficiency, accuracy, and impact in their operations.

Course Content

Our strength lies in our individuality. Set up by Esther Bryce, the team strives to bring in the best talent in various fields, from knowledge to the implementation.

Play on real time- Productions (7 hours)

  • ML Model Workflows

  • Introduction to MLops

  • Building Data Pipelines

  • Time Series Data Pipeline: AWS - S3

  • Development Frameworks and Tools installation

  • ML Model Development Framework: Jupyter

  • Replicating Production Environment: Docker

  • Development of Prod Code-Base: VS Code

Is machine performing the way we have designed for (3 hours)

  • Deployment and Monitoring

  • Deployment to Production: Server (GitLab)

  • Monitoring Models in Production: Sentry

  • Monitoring Model Output: Grafana

  • Tracking Model Performance: CSI and PSI

  • Learning Algorithms Effectively

  • Better Ways to Learn Algorithms

  • Study All Algorithms and Search Real-Life Problems