50000 +
Students Enrolled
(500)
Ratings
18 Weeks
Duration
Our Alumni Work at Top Companies
ML & LLM Ops Course Curriculum
It stretches your mind, think better and create even better.
Topics:
0.1 Programming and DevOps Foundations
Week 1: Core Technical Skills
Python for MLOps
Advanced Python Programming
Object-Oriented Design
Async Programming
Error Handling and Logging
Package Management
Testing with Pytest
Software Engineering Practices
Clean Code Principles
Design Patterns
SOLID Principles
Code Review Practices
Documentation Standards
Agile Methodologies
Linux and Command Line
Shell Scripting (Bash)
Process Management
File Systems
Networking Basics
System Administration
Automation Scripts
Version Control
Git Advanced Features
Branching Strategies
Git Workflows (GitFlow, GitHub Flow)
Merge Conflict Resolution
Git Hooks
Collaborative Development
0.2 DevOps and Cloud Foundations
Week 2: Infrastructure Basics
DevOps Fundamentals
DevOps Culture and Practices
CI/CD Concepts
Infrastructure as Code
Configuration Management
Containerization Basics
Monitoring and Logging
Cloud Computing Essentials
Cloud Service Models (IaaS, PaaS, SaaS)
AWS Fundamentals
Azure Basics
GCP Overview
Cloud Security Basics
Cost Management
Containerization
Docker Fundamentals
Container Images
Docker Compose
Container Registries
Container Security
Best Practices
Basic Machine Learning
ML Pipeline Overview
Model Training Basics
Evaluation Metrics
Overfitting and Validation
Model Selection
Deployment Considerations
Lab Project
Set up a basic ML development environment with Docker and implement a simple CI/CD pipeline
Topics:
1.1 MLOps Fundamentals
Week 1: Core Concepts
What is MLOps?
MLOps vs DevOps
MLOps vs Data Engineering
MLOps Maturity Models
Benefits and ROI
Industry Adoption
Case Studies
MLOps Lifecycle
Problem Definition
Data Management
Model Development
Model Deployment
Monitoring and Maintenance
Feedback Loops
Key Challenges in ML Production
Data Drift
Model Drift
Technical Debt
Reproducibility
Scalability
Governance
MLOps Principles
Automation
Continuous Integration
Continuous Delivery
Continuous Training
Continuous Monitoring
Collaboration
MLOps Team Structure
Roles and Responsibilities
Data Scientists vs ML Engineers
Platform Teams
Cross-functional Collaboration
Skills Matrix
Communication Patterns
1.2 MLOps Tools and Platforms
Week 2: Technology Landscape
MLOps Platforms
MLflow Overview
Kubeflow Components
Azure ML Platform
AWS SageMaker
Google Vertex AI
Databricks MLOps
Experiment Tracking
Weights & Biases
Neptune.ai
Comet ML
TensorBoard
DVC (Data Version Control)
Sacred
Model Registries
Model Versioning
Model Metadata
Model Lineage
Approval Workflows
Model Governance
Access Control
Orchestration Tools
Apache Airflow
Prefect
Dagster
Argo Workflows
Luigi
Kedro
Infrastructure Tools
Terraform
Ansible
Kubernetes
Helm
Cloud Formation
Pulumi
Project
Design an MLOps architecture for a real-world use case
Topics:
2.1 Compute and Storage
Week 1: Infrastructure Management
Compute Resources
CPU vs GPU vs TPU
Cluster Management
Resource Allocation
Auto-scaling
Spot/Preemptible Instances
Cost Optimization
Storage Solutions
Object Storage (S3, GCS, Azure Blob)
File Systems (NFS, EFS)
Block Storage
Data Lakes
Feature Stores
Model Stores
Networking
VPC and Subnets
Load Balancers
API Gateways
Service Mesh
CDN Integration
Security Groups
Infrastructure as Code
Terraform Fundamentals
Resource Provisioning
State Management
Modules and Reusability
Multi-Environment Setup
GitOps Practices
2.2 Kubernetes for ML
Week 2: Container Orchestration
Kubernetes Fundamentals
Pods and Deployments
Services and Ingress
ConfigMaps and Secrets
Persistent Volumes
Namespaces
RBAC
Kubernetes for ML Workloads
GPU Scheduling
Resource Quotas
Job and CronJob
Distributed Training
Model Serving
Autoscaling (HPA/VPA)
Kubeflow Deep Dive
Kubeflow Pipelines
Katib (Hyperparameter Tuning)
KFServing/KServe
Notebooks
Training Operators
Metadata Management
Advanced Topics
Custom Resources (CRDs)
Operators for ML
Service Mesh (Istio)
Multi-cluster Setup
Disaster Recovery
Security Hardening
Lab
Deploy an ML pipeline on Kubernetes using Kubeflow
Topics:
3.1 Data Engineering for ML
Week 1: Data Pipelines
Data Pipeline Architecture
Batch vs Streaming
ETL vs ELT
Data Pipeline Patterns
Error Handling
Data Quality Checks
Pipeline Monitoring
Data Processing Tools
Apache Spark
Apache Beam
Pandas at Scale
Dask
Ray
Polars
Data Versioning
DVC (Data Version Control)
Git LFS
Delta Lake
Data Lineage
Reproducibility
Time Travel
Feature Engineering Pipelines
Feature Extraction
Feature Transformation
Feature Selection
Automated Feature Engineering
Feature Validation
Feature Monitoring
3.2 Feature Stores
Week 2: Feature Management
Feature Store Architecture
Online vs Offline Features
Feature Serving
Feature Discovery
Feature Versioning
Feature Governance
Access Control
Feature Store Solutions
Feast
Tecton
AWS Feature Store
Databricks Feature Store
Hopsworks
Custom Implementation
Feature Operations
Feature Ingestion
Feature Computation
Feature Serving Patterns
Point-in-Time Correctness
Feature Freshness
Feature Quality
Integration Patterns
Training Integration
Serving Integration
Batch Predictions
Real-time Predictions
Feature Pipelines
Monitoring Integration
Project
Build an end-to-end data pipeline with feature store integration
Topics:
4.1 Experiment Management
Week 1: Tracking and Reproducibility
Experiment Tracking
Metrics Logging
Parameter Tracking
Artifact Management
Visualization
Comparison Tools
Collaboration Features
MLflow Deep Dive
Tracking Server Setup
Experiments and Runs
Model Registry
Projects
Models
Plugins and Extensions
Reproducibility
Environment Management
Dependency Tracking
Random Seed Management
Data Versioning
Code Versioning
Configuration Management
Hyperparameter Optimization
Grid Search
Random Search
Bayesian Optimization
Hyperband
Population-Based Training
Neural Architecture Search
4.2 Training Pipelines
Week 2: Automated Training
Pipeline Orchestration
Pipeline Design Patterns
DAG Construction
Task Dependencies
Error Recovery
Retry Logic
Alerting
Distributed Training
Data Parallelism
Model Parallelism
Horovod
PyTorch Distributed
TensorFlow Distribution
Ray Train
Continuous Training
Trigger Mechanisms
Incremental Training
Transfer Learning
Active Learning
Online Learning
Model Updates
Training Optimization
Resource Optimization
Cost Management
Training Time Reduction
Caching Strategies
Warm Starting
Early Stopping
Lab
Implement an automated training pipeline with experiment tracking
Topics:
5.1 Continuous Integration for ML
Week 1: Testing and Validation
Code Quality
Linting and Formatting
Type Checking
Code Coverage
Static Analysis
Security Scanning
Documentation Generation
ML-Specific Testing
Data Validation Tests
Feature Validation
Model Validation Tests
Integration Tests
Performance Tests
A/B Testing Framework
Testing Strategies
Unit Testing for ML
Component Testing
Contract Testing
Shadow Testing
Canary Testing
Chaos Engineering
Automated Validation
Model Quality Gates
Data Quality Gates
Performance Thresholds
Business Metrics Validation
Compliance Checks
Security Validation
5.2 Continuous Delivery for ML
Week 2: Deployment Automation
CI/CD Pipelines
GitHub Actions for ML
GitLab CI/CD
Jenkins Pipelines
Azure DevOps
CircleCI
ArgoCD
Deployment Strategies
Blue-Green Deployment
Canary Deployment
Rolling Updates
Feature Flags
Dark Launches
Gradual Rollouts
Model Packaging
Model Serialization
Container Images
Model Artifacts
Dependencies Management
Version Tagging
Registry Management
Release Management
Release Planning
Approval Workflows
Rollback Strategies
Change Management
Documentation Updates
Communication Plans
Project
Build a complete CI/CD pipeline for an ML project
Topics:
6.1 Deployment Patterns
Week 1: Architecture Patterns
Deployment Architectures
Batch Inference
Real-time Serving
Near Real-time
Edge Deployment
Embedded Models
Federated Deployment
Serving Patterns
REST APIs
gRPC Services
GraphQL
WebSockets
Message Queues
Event Streaming
Model Formats
ONNX
TensorFlow SavedModel
PyTorch TorchScript
PMML
Core ML
TensorFlow Lite
Containerization for ML
Docker Best Practices
Multi-stage Builds
Size Optimization
Security Hardening
Base Images
Layer Caching
6.2 Scalable Model Serving
Week 2: Production Serving
Model Serving Frameworks
TensorFlow Serving
TorchServe
ONNX Runtime
Triton Inference Server
BentoML
Seldon Core
Scaling Strategies
Horizontal Scaling
Vertical Scaling
Auto-scaling Policies
Load Balancing
Request Routing
Circuit Breakers
Performance Optimization
Model Optimization
Batching Strategies
Caching
Hardware Acceleration
Quantization
Pruning
Multi-Model Serving
Model Routing
Model Versioning
A/B Testing
Multi-Armed Bandits
Ensemble Serving
Model Composition
Lab
Deploy and scale multiple model versions in production
Topics:
7.1 ML Monitoring
Week 1: Monitoring Systems
Metrics and KPIs
Model Performance Metrics
Business Metrics
System Metrics
Data Quality Metrics
User Engagement Metrics
Cost Metrics
Monitoring Stack
Prometheus
Grafana
ELK Stack
Datadog
New Relic
Custom Solutions
Data and Model Drift
Distribution Drift
Concept Drift
Feature Drift
Label Drift
Detection Methods
Alerting Strategies
Performance Monitoring
Latency Tracking
Throughput Monitoring
Error Rates
Resource Utilization
Queue Depths
Cache Hit Rates
7.2 Observability and Debugging
Week 2: Production Insights
Logging and Tracing
Structured Logging
Distributed Tracing
Correlation IDs
Log Aggregation
Log Analysis
Trace Analysis
Model Explainability
SHAP Values
LIME
Feature Importance
Counterfactual Explanations
Model Cards
Bias Detection
Alerting and Incident Response
Alert Design
Alert Fatigue
Escalation Policies
Runbooks
Post-Mortems
Root Cause Analysis
Feedback Loops
User Feedback Collection
Model Performance Feedback
Data Quality Feedback
Continuous Improvement
Retraining Triggers
Model Updates
Project
Implement comprehensive monitoring for a production ML system
Topics:
8.1 LLM-Specific Challenges
Week 1: Unique Aspects of LLMOps
LLMOps vs Traditional MLOps
Scale Differences
Cost Considerations
Latency Requirements
Memory Constraints
Quality Challenges
Safety Concerns
LLM Lifecycle Management
Model Selection
Fine-tuning Pipelines
Prompt Engineering
Evaluation Strategies
Deployment Patterns
Update Cycles
Infrastructure for LLMs
GPU Clusters
Memory Requirements
Network Bandwidth
Storage Needs
Cost Optimization
Vendor Lock-in
LLM Development Workflow
Experimentation
Prompt Development
Fine-tuning
Evaluation
Deployment
Monitoring
8.2 Prompt Engineering Operations
Week 2: Prompt Management
Prompt Management Systems
Prompt Versioning
Prompt Templates
Prompt Testing
Prompt Registry
Access Control
Audit Trails
Prompt Optimization
A/B Testing Prompts
Prompt Performance Metrics
Cost per Prompt
Latency Optimization
Quality Metrics
User Feedback
Prompt CI/CD
Prompt Validation
Automated Testing
Deployment Pipelines
Rollback Mechanisms
Progressive Rollouts
Feature Flags
Dynamic Prompting
Context Injection
Personalization
Template Systems
Variable Management
Chain Management
Error Handling
Lab
Build a prompt management system with versioning and testing
Topics:
9.1 Fine-tuning Pipelines
Week 1: Automated Fine-tuning
Data Preparation
Dataset Curation
Data Quality Checks
Format Conversion
Train/Val/Test Splits
Data Augmentation
Synthetic Data
Fine-tuning Infrastructure
Multi-GPU Setup
Distributed Training
Memory Optimization
Checkpointing
Resume Training
Cost Management
Fine-tuning Strategies
Full Fine-tuning
LoRA/QLoRA
Prefix Tuning
Adapter Layers
Instruction Tuning
RLHF Pipelines
Experiment Management
Hyperparameter Tracking
Loss Curves
Validation Metrics
Model Comparison
Best Model Selection
Artifact Storage
9.2 LLM Training Operations
Week 2: Large-Scale Training
Distributed Training for LLMs
Data Parallelism
Model Parallelism
Pipeline Parallelism
ZeRO Optimization
FSDP
DeepSpeed
Training Monitoring
Loss Tracking
Gradient Statistics
Memory Usage
Training Speed
Checkpoint Management
Failure Recovery
Quality Assurance
Evaluation Suites
Benchmark Testing
Human Evaluation
Safety Testing
Bias Detection
Output Validation
Continuous Training
Incremental Updates
Online Learning
Feedback Integration
Model Merging
Version Management
Rollout Strategies
Project
Implement an automated fine-tuning pipeline for LLMs
Topics:
10.1 LLM Serving Infrastructure
Week 1: Deployment Strategies
Serving Frameworks
vLLM
Text Generation Inference (TGI)
TensorRT-LLM
llama.cpp
Triton Inference Server
Custom Solutions
Optimization Techniques
Quantization (INT8, INT4)
Flash Attention
KV Cache Optimization
Continuous Batching
Speculative Decoding
Model Compression
Deployment Patterns
API Endpoints
Streaming Responses
Batch Processing
Edge Deployment
Serverless LLMs
Multi-Model Serving
Cost Optimization
Token Management
Request Batching
Caching Strategies
Model Selection
Spot Instance Usage
Reserved Capacity
10.2 RAG and Agent Operations
Week 2: Complex LLM Systems
RAG Operations
Vector DB Management
Index Updates
Embedding Management
Retrieval Monitoring
Context Management
Citation Tracking
Agent Systems Ops
Tool Management
Memory Systems
State Management
Action Monitoring
Error Recovery
Performance Tracking
Orchestration
Chain Management
Workflow Orchestration
Error Handling
Retry Logic
Timeout Management
Fallback Strategies
Production Considerations
Rate Limiting
Authentication
Usage Tracking
Billing Integration
Compliance
Audit Logging
Lab
Deploy a production RAG system with monitoring
Topics:
11.1 ML Security
Security Threats
Model Stealing
Data Poisoning
Adversarial Attacks
Prompt Injection
Data Leakage
Model Inversion
Security Measures
Access Control
Encryption
Secure APIs
Network Security
Container Security
Secrets Management
LLM Security
Prompt Injection Defense
Output Validation
Content Filtering
Rate Limiting
Token Security
API Key Management
11.2 Compliance and Governance
Regulatory Compliance
GDPR
CCPA
HIPAA
SOC 2
ISO 27001
Industry Standards
Model Governance
Model Risk Management
Approval Workflows
Documentation Requirements
Audit Trails
Version Control
Change Management
Ethical AI
Bias Detection
Fairness Metrics
Transparency
Explainability
Accountability
Human Oversight
Project
Implement security and compliance measures for ML systems
Topics:
12.1 MLOps at Scale
Week 1: Enterprise Architecture
Platform Engineering
Platform Design
Multi-Tenancy
Resource Management
Service Catalog
Self-Service Capabilities
Developer Experience
Team Collaboration
Role-Based Access
Project Management
Knowledge Sharing
Documentation
Training Programs
Best Practices
Cost Management
Budget Tracking
Resource Allocation
Chargeback Models
Optimization Strategies
Vendor Management
ROI Analysis
Integration Patterns
Enterprise Systems
Data Warehouses
BI Tools
CRM/ERP Integration
API Management
Event Streaming
12.2 Advanced Topics
Week 2: Cutting-Edge Practices
Edge MLOps
Edge Deployment
Model Optimization
OTA Updates
Offline Capabilities
Resource Constraints
Monitoring
Federated Learning Ops
Distributed Training
Privacy Preservation
Model Aggregation
Client Management
Communication Protocols
Quality Assurance
AutoML Operations
AutoML Pipelines
Neural Architecture Search
Hyperparameter Optimization
Automated Feature Engineering
Model Selection
Deployment Automation
Green MLOps
Carbon Footprint
Energy Efficiency
Sustainable Practices
Green Computing
Optimization Strategies
Reporting
Final Project
Design and implement an enterprise MLOps platform
TOOlS & PLATFORMS
Our AI Programs
Build a complete multi-agent customer service system with: - Natural language understanding - Intent recognition and routing - Knowledge base integration - Escalation handling - Sentiment analysis - Performance monitoring
Develop an AI research agent capable of: - Literature review automation - Data collection and analysis - Report generation - Citation management - Collaborative research - Quality validation
Create an agent system for business process automation: - Workflow orchestration - Document processing - Decision automation - Integration with enterprise systems - Compliance checking - Performance optimization
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Come and chat with us about your goals over a cup of coffee.
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3rd Floor, Site No 1&2 Saroj Square, Whitefield Main Road, Munnekollal Village Post, Marathahalli, Bengaluru, Karnataka.
