Master MLOps Engineering

MLOps Engineering Course

MLOps Engineering is an industry-focused course that teaches how to build, deploy, monitor, and mana

Bridge the gap between machine learning development and production engineering with our comprehensive 3-Month MLOps Engineering Masterclass in Calicut. Designed for data scientists, ML engineers, software developers, and DevOps professionals, this course delivers hands-on experience in building reliable, scalable, and automated machine learning systems.

Month 1 - Machine Learning & MLOps Foundations

  • Introduction to Machine Learning
  • Introduction to MLOps
  • MLOps Lifecycle
  • Python for Machine Learning
  • NumPy & Pandas
  • Scikit-learn
  • Data Preprocessing
  • Model Training & Evaluation
  • Git & GitHub
  • Virtual Environments
  • Experiment Tracking

Month 2 - Model Deployment & Automation

  • MLflow
  • DVC (Data Version Control)
  • Model Versioning
  • FastAPI for ML APIs
  • Docker Fundamentals
  • Containerizing ML Models
  • Docker Compose
  • CI/CD Pipelines
  • GitHub Actions
  • Jenkins Basics
  • Model Registry

Month 3 - Cloud MLOps & Production Deployment

  • Kubernetes Fundamentals
  • Model Serving with Kubernetes
  • TensorFlow Serving
  • TorchServe Basics
  • AWS SageMaker Overview
  • Azure Machine Learning
  • Google Vertex AI
  • Monitoring ML Models
  • Model Drift Detection
  • Logging & Performance Monitoring
  • Security & Best Practices
  • Production Deployment

Real-Time Projects

  1. House Price Prediction Deployment
  2. Customer Churn Prediction API
  3. Sales Forecasting System
  4. Image Classification Deployment
  5. Sentiment Analysis API
  6. Fraud Detection Pipeline
  7. Recommendation System Deployment
  8. End-to-End MLOps Pipeline

Career Preparation

  • Industry Capstone Project
  • GitHub Portfolio Development
  • Resume Building
  • Mock Interviews
  • System Design for ML Applications
  • MLOps Best Practices
  • Placement Assistance



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