RCreative Architects | IT CADDESK Centre
+91 93524 99979 | [email protected]
Active Course

MLOps & AI Deployment

4 to 6 Months
Mode: Offline Classroom Training
Certificate included
Expert instructor

About this course

A specialized production engineering course in MLOps, containerizing AI models, CI/CD pipelines, experiment tracking, model registry, monitoring, and cloud deployment.

Course Curriculum

• MLOps Fundamentals • What is MLOps? • Machine Learning Lifecycle • MLOps vs DevOps vs DataOps • Why MLOps is Important • Challenges in Production AI • End-to-End ML Pipeline • Model Development Lifecycle • Continuous Training Concepts • Continuous Deployment Concepts • Career Opportunities in MLOps

• Linux Fundamentals • Linux Basics • File System Structure • Shell Commands • File Permissions • Process Management • Environment Variables • Package Management • SSH • Cron Jobs • Bash Scripting Basics • Version Control • Introduction to Git • Git Installation • Git Workflow • Branching & Merging • GitHub Repositories • Pull Requests • Conflict Resolution • Git Tags • Release Management • Git Best Practices • Development Environment • Python Virtual Environments • pip & Dependency Management • Conda Environment • Project Structure • Configuration Management • Logging • Debugging • Secrets Management

• Docker Fundamentals • Introduction to Docker • Containers vs Virtual Machines • Docker Architecture • Docker Installation • Docker CLI • Docker Images • Docker Containers • Docker Registries • Docker Volumes • Docker Networks • Building AI Containers • Dockerfile • Docker Compose • Environment Variables • Multi-Stage Builds • Container Optimization • Running ML Models in Containers • Managing Dependencies • Image Versioning • Container Security • Best Practices

• Kubernetes Fundamentals • Introduction to Kubernetes • Kubernetes Architecture • Pods • Deployments • ReplicaSets • Services • ConfigMaps • Secrets • Namespaces • Ingress • AI Workload Management • Deploying ML Models • Scaling Applications • Auto Scaling • Rolling Updates • Resource Management • Persistent Storage • GPU Workloads • Monitoring Applications • Load Balancing • High Availability

• Model Deployment • Introduction to Model Deployment • Model Serialization • Pickle • Joblib • ONNX • TensorFlow SavedModel • PyTorch Model Export • Model Versioning • Model Registry Concepts • Deployment Strategies • Serving Machine Learning Models • Flask APIs • FastAPI • REST APIs • Batch Predictions • Real-Time Inference • Asynchronous Predictions • API Documentation • Health Checks • Error Handling • Production Best Practices

• Building ML Pipelines • Introduction to ML Pipelines • Data Ingestion • Data Validation • Data Transformation • Feature Engineering Pipelines • Model Training Pipelines • Model Evaluation Pipelines • Pipeline Automation • Pipeline Scheduling • Pipeline Monitoring • Workflow Orchestration • Apache Airflow (Introduction) • Kubeflow (Introduction) • Prefect (Introduction) • DAG Concepts • Scheduled Jobs • Workflow Automation • Pipeline Versioning • Error Recovery • Notifications • Workflow Optimization

• ML Experiment Tracking • Introduction to MLflow • Experiment Tracking • Parameter Logging • Metric Tracking • Artifact Management • Model Registry • Model Comparison • Model Promotion • Model Versioning • Model Lifecycle Management • Data Versioning • Introduction to DVC • Dataset Versioning • Pipeline Versioning • Artifact Storage • Collaboration • Reproducible Experiments • Data Lineage • Storage Backends

• Continuous Integration • Introduction to CI/CD • GitHub Actions • Jenkins Basics • Automated Testing • Code Quality Checks • Dependency Scanning • Unit Testing • Integration Testing • Build Automation • Release Pipelines • Continuous Deployment • Deployment Automation • Blue-Green Deployment • Canary Deployment • Rolling Deployment • Rollback Strategies • Infrastructure as Code (Introduction) • Environment Management • Production Releases • Automated Validation • Deployment Monitoring

• Production Monitoring • Application Monitoring • Model Monitoring • Infrastructure Monitoring • Logging • Metrics Collection • Alerting • Performance Monitoring • Resource Monitoring • API Monitoring • Dashboard Creation • Model Observability • Model Drift • Data Drift • Concept Drift • Prediction Monitoring • Feature Monitoring • Data Quality Monitoring • Model Retraining Triggers • Error Analysis • Root Cause Analysis • Performance Optimization

• Cloud Platforms • Introduction to Cloud Computing • AWS AI Services • Microsoft Azure AI • Google Cloud AI • Cloud Storage • Object Storage • Compute Services • Serverless Computing • Managed AI Services • Cloud Security • Deployment Strategies • Cloud Model Deployment • Auto Scaling • Load Balancing • Cost Optimization • Backup & Recovery • Disaster Recovery • Multi-Region Deployment • Infrastructure Monitoring • Production Scaling • Cloud Best Practices

• AI Infrastructure Security • Authentication • Authorization • API Security • Environment Variables • Secret Management • Encryption Basics • Network Security • Access Control • Compliance Concepts • Infrastructure Hardening • Production Operations • High Availability • Fault Tolerance • Disaster Recovery • Backup Strategies • Capacity Planning • Cost Monitoring • Incident Management • Production Debugging • Performance Tuning • Production Documentation

Hands-on Projects

End-to-End Machine Learning Pipeline
ML Model Deployment using FastAPI
Dockerized AI Application
Kubernetes-Based AI Deployment
MLflow Model Registry Platform
AI Model Monitoring Dashboard
Automated ML Retraining Pipeline
CI/CD Pipeline for Machine Learning
Cloud-Based AI Deployment
Scalable AI Inference API
Enterprise MLOps Platform
Production Recommendation System
AI Infrastructure Monitoring Solution
End-to-End Production AI System

Key Skills Covered

  • Docker Containerization for ML
  • Kubernetes AI Cluster Management
  • ML Pipeline Automation (Kubeflow/Airflow)
  • Experiment Tracking (MLflow/Weights & Biases)
  • Model Serving & APIs (FastAPI/Triton)
  • Model Monitoring & Drift Detection
  • Cloud AI Deployment (AWS/GCP)

Technologies Covered

Python Linux Bash Git GitHub Docker Docker Compose Kubernetes FastAPI Flask MLflow DVC Apache Airflow Kubeflow Prefect GitHub Actions Jenkins ONNX TensorFlow PyTorch AWS Microsoft Azure Google Cloud Platform (GCP) REST APIs Prometheus Grafana Nginx Docker Registry Model Monitoring CI/CD MLOps

Course Details

Duration

4 to 6 Months

Certificate

Upon completion

Start Date

Flexible

Instructor

CDC Training Services Pvt.Ltd

Interested in this course?

What's included:
  • Lifetime access
  • Certificate of completion
  • Expert instruction
  • Practical projects
  • 24/7 support
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