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  • MLOps with Open-Source Tools

MLOps with Open-Source Tools

  • By Certiedge official
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Course Duration: 32 Hours of Comprehensive Learning

Mode of Learning:

Choose the learning style that best fits your schedule and preferences:

  1. Online Instructor-Led Training – Learn in real time with live expert-led sessions.
  2. Online Self-Paced Learning – Study anytime, anywhere with flexible recorded modules.
  3. Onsite/Classroom Training – Experience in-person, hands-on learning guided by certified trainers.

What’s Included in the Training Program:

Our industry-leading Training Program is designed to provide a complete learning experience through a perfect blend of theory and practical exposure. Here’s what you’ll get:

  • Live, Instructor-Led Online Training Sessions
  • Hands-on Lab Exercises
  • Certified and Experienced Trainers
  • Post-Training Support & Mentorship
  • Comprehensive Learning Resources including eBooks, guides, and reference materials.
  • Real-World Case Studies & Application Scenarios
  • Networking Opportunities
  • Group Projects and Collaborative Learning Activities
  • Best Practices and Industry Insights.
  • Access to Recorded Sessions (available for online learners, based on client permissions).
  • Follow-up Review Sessions.
  • Tool Demonstrations and Guided Walkthroughs

This training helps you gain the knowledge and hands-on experience needed to advance your career in today’s competitive IT landscape.

 

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Course Content

Introduction to MLOps & Testing Perspective

  • What is MLOps? Difference from DevOps
  • Why Testing Teams need MLOps (model testing vs. software testing)
  • Lifecycle of ML systems: data → model → deployment → monitoring
  • Key challenges: reproducibility, versioning, testing bias

Open-Source Tools Overview

  • MLflow → Experiment tracking, model registry
  • DVC → Data & pipeline versioning
  • Evidently AI → Model monitoring, drift detection
  • Great Expectations → Data quality testing

Data Testing & Validation

  • Importance of data testing in ML lifecycle
  • Validating data pipelines (missing values, schema drift, anomalies)
  • Data versioning with DVC
  • Automated data checks with Great Expectations

Deployment, Monitoring & CI/CD

  • Deployment options (batch, REST API, streaming)
  • Using Docker + Kubernetes for model serving
  • Workflow orchestration with Apache Airflow / Kubeflow Pipelines

Monitoring models with Evidently AI

  • Drift detection (data drift, concept drift)
  • Performance monitoring in production

Capstone Project & Roadmap

  • Build a CI/CD pipeline for ML model using GitHub Actions + MLflow + DVC
  • End-to-End ML Pipeline with monitoring (Airflow + Evidently AI)
  • Automated Data & Model Testing Framework with Great Expectations + Pytest

Presentations & Peer Review

  • Roadmap for adopting open-source MLOps in testing workflows
  • Best practices for QA in ML systems

Course Includes:

  • Price:
    ₹44,000.00 ₹55,000.00
  • Lessons:22
  • Level:Intermediate
₹44,000.00 ₹55,000.00
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