Enterprise AI Platform Engineering Certified Course

ESSENTIAL SKILLS YOU WILL DEVELOP – Enterprise AI Platform Engineering

• Enterprise AI Platform Architecture & Design
• LLM & Generative AI Infrastructure
• AI Model Deployment & Model Serving
• MLOps & LLMOps Practices
• AI API Development & Integration
• RAG Pipelines & Vector Database Management
• Docker & Kubernetes for AI Workloads
• Cloud AI Platform Engineering
• CI/CD & AI Application Automation
• AI Monitoring, Observability & Logging
• Scalability, Reliability & High Availability
• AI Security, Governance & Access Control
• Performance, Latency & Cost Optimization
• Infrastructure as Code & Automation
• Production AI Platform Operations

Tools Covered:

• AWS SageMaker
• Microsoft Azure Machine Learning
• Google Vertex AI
• Kubernetes & Docker
• Terraform
• MLflow
• Kubeflow
• LangChain & LangGraph
• LlamaIndex
• OpenAI APIs
• Hugging Face
• FastAPI
• Pinecone / FAISS / Chroma
• Prometheus & Grafana
• OpenTelemetry
• GitHub Actions & Jenkins

Syllabus:

Module 1: Enterprise AI Platform Fundamentals

• Enterprise AI Architecture & Platform Components
• Generative AI, LLMs & AI Workloads
• Enterprise AI Use Cases & Requirements

Module 2: AI Platform Architecture & Design

• Scalable AI Platform Architecture
• Compute, Storage & Networking for AI
• High Availability & Reliability Design

Module 3: LLM & Generative AI Infrastructure

• LLM Integration & Model Management
• Model Serving & Inference Infrastructure
• Prompt, Context & Model Configuration

Module 4: Data, RAG & Vector Infrastructure

• Enterprise Data Pipelines & Processing
• Embeddings & Vector Database Architecture
• RAG Pipeline Development & Optimization

Module 5: MLOps & LLMOps

• Model Lifecycle & Experiment Management
• Model Evaluation, Versioning & Registry
• LLMOps Workflows & Production Management

Module 6: AI Application & API Engineering

• FastAPI & AI Service Development
• AI APIs, Tool Calling & Integrations
• Microservices Architecture for AI Applications

Module 7: Cloud, Docker & Kubernetes

• AWS, Azure & Google Cloud AI Platforms
• Docker Containerization for AI Workloads
• Kubernetes Deployment, Scaling & Management

Module 8: CI/CD & Infrastructure Automation

• CI/CD Pipelines for AI Applications
• Terraform & Infrastructure as Code
• Automated Testing, Deployment & Release Management

Module 9: AI Security, Governance & Observability

• AI Security, Identity & Access Management
• Monitoring, Logging, Tracing & Observability
• Governance, Compliance & Responsible AI

Module 10: Production AI Platform Engineering

• Enterprise AI Platform Implementation
• Performance, Cost & Resource Optimization
• End-to-End Production AI Platform Project

Industry Projects:

• Enterprise Generative AI Platform
• Production LLM Deployment & Model Serving Platform
• Enterprise RAG & Vector Search Platform
• AI Agent Platform for Business Automation
• Cloud-Based MLOps & LLMOps Platform
• Kubernetes-Based AI Application Platform
• Enterprise AI API & Microservices Platform
• AI Monitoring & Observability Platform
• Secure AI Governance & Access Control System

Who is this program for?

• AI/ML Engineers and Generative AI Professionals
• AI Platform and Infrastructure Engineers
• MLOps & LLMOps Engineers
• Cloud & DevOps Engineers
• Software Developers and AI Application Developers
• Data Engineers and Machine Learning Engineers
• Kubernetes & Cloud Infrastructure Professionals
• AI Solutions Architects and Technical Leads
• Enterprise IT Professionals transitioning into AI Platform Engineering
• Students and Freshers interested in Enterprise AI Technologies