LEARN AND APPLY

AI Engineering Professional

Build AI systems you can evaluate, secure and operate. Learn LLMs, RAG, agents and model serving through 32 modules and 12 practical projects.

32 modules · 450 topics 12 practical projects Self-paced learning

Who this course is for

Developers with basic programming knowledge who want a structured path into AI engineering, and backend engineers moving into production AI systems.

CURRICULUM

From foundations to production AI systems

Progress through six phases from Python and ML foundations to RAG, AI agents, multimodal systems and production architecture.

32modules450topics12projects
01Phase 1 · About 7 weeksFoundations of AI Engineering

Build foundations in Python, mathematics, machine learning and PyTorch.

Module 01Introduction to AI Engineering10 topics
  1. What Is Artificial Intelligence?
  2. AI vs Machine Learning vs Deep Learning vs Generative AI
  3. AI Engineer vs ML Engineer vs AI Research Scientist
  4. The Modern AI Technology Stack
  5. Foundation Models and Their Capabilities
  6. Closed-Source vs Open-Weight Models
  7. AI Systems Architecture
  8. The AI Application Development Lifecycle
  9. Real-World AI Engineering Use Cases
  10. Common AI Engineering Anti-Patterns

Hands-on practice

Visualize the components and request lifecycle of an AI system.

Module 02Python for AI Engineering12 topics
  1. Python Fundamentals for Engineers
  2. Virtual Environments and Dependency Management
  3. Type Hints, Dataclasses, and Pydantic
  4. NumPy Arrays and Vectorized Computing
  5. Pandas and Data Manipulation
  6. Asynchronous Programming with asyncio
  7. HTTP Clients and REST APIs
  8. FastAPI Fundamentals
  9. Streaming Responses and Async Generators
  10. Testing, Logging, and Project Structure
  11. Jupyter Notebooks and Experimentation
  12. Building a Production-Ready Python Service

Hands-on practice

Build an asynchronous AI gateway with FastAPI.

Module 03Mathematics for Machine Learning12 topics
  1. Scalars, Vectors, Matrices, and Tensors
  2. Vector Operations and Matrix Multiplication
  3. Dot Product and Cosine Similarity
  4. Norms, Distances, and Vector Spaces
  5. Eigenvalues, Eigenvectors, and SVD
  6. Probability and Conditional Probability
  7. Bayes' Theorem
  8. Statistics and Distributions
  9. Derivatives and Partial Derivatives
  10. Gradients and Chain Rule
  11. Loss Functions and Optimization
  12. Gradient Descent and Backpropagation Mathematics

Hands-on practice

Implement linear regression and gradient descent with NumPy.

Module 04Machine Learning Fundamentals12 topics
  1. Supervised, Unsupervised, and Self-Supervised Learning
  2. Training, Validation, and Test Datasets
  3. Features, Labels, and Data Preparation
  4. Linear and Logistic Regression
  5. Decision Trees and Random Forests
  6. Classification and Regression Metrics
  7. Bias and Variance
  8. Overfitting and Underfitting
  9. Cross-Validation and Data Leakage
  10. Feature Engineering
  11. Hyperparameter Optimization
  12. Model Deployment Fundamentals

Hands-on practice

Build a fraud detection or classification model with Scikit-learn.

Module 05Deep Learning with PyTorch12 topics
  1. Neural Network Fundamentals
  2. Perceptrons and Multilayer Networks
  3. Activation Functions
  4. Forward and Backward Propagation
  5. Autograd and Computational Graphs
  6. PyTorch Tensors and Modules
  7. Training Loops and Optimizers
  8. Batch Size, Epochs, and Learning Rates
  9. Regularization, Dropout, and Normalization
  10. CNNs, RNNs, and Sequence Modeling
  11. GPU Acceleration and Memory
  12. Saving, Loading, and Evaluating Models

Hands-on practice

Train a neural network from scratch with PyTorch.

02Phase 2 · About 7 weeksLarge Language Models & Generative AI

Understand Transformers and build applications with model APIs, streaming and structured outputs.

Module 06Natural Language Processing Foundations10 topics
  1. Introduction to Natural Language Processing
  2. Text Cleaning and Normalization
  3. Bag of Words and TF-IDF
  4. Word2Vec, GloVe, and Word Embeddings
  5. Tokenization Fundamentals
  6. BPE, WordPiece, and SentencePiece
  7. Language Modeling and Probability
  8. Sequence-to-Sequence Models
  9. Encoder-Decoder Architectures
  10. Attention Mechanisms
Module 07Transformers Architecture Deep Dive15 topics
  1. Why Transformers Replaced Many Recurrent Architectures
  2. Transformer Architecture Overview
  3. Token and Positional Embeddings
  4. Self-Attention and Scaled Dot-Product Attention
  5. Query, Key, and Value Matrices
  6. Multi-Head Attention
  7. Causal Masking and Attention Masks
  8. Feed-Forward Networks
  9. Residual Connections and Layer Normalization
  10. Encoder-Only, Decoder-Only, and Encoder-Decoder Models
  11. Autoregressive Text Generation
  12. KV Cache and Attention Complexity
  13. Long-Context Transformers
  14. Mixture-of-Experts Models
  15. Building a Small Transformer from Scratch

Hands-on practice

Simulate the token, embedding, attention, logits and next-token pipeline.

Module 08LLM Inference & Model APIs14 topics
  1. LLM Inference Lifecycle
  2. Tokens, Context Windows, and Output Limits
  3. Temperature, Top-p, and Sampling
  4. Greedy Decoding and Beam Search
  5. Model Providers and API Architecture
  6. OpenAI Responses API and Comparable Provider APIs
  7. Model Routing and Provider Abstraction
  8. Streaming with Server-Sent Events
  9. Structured Outputs and JSON Schema
  10. Function Calling Fundamentals
  11. Retries, Timeouts, and Rate Limits
  12. Token Usage, Pricing, and Latency
  13. Prompt Caching
  14. Model Fallback and Reliability Patterns

Hands-on practice

Build a unified gateway for multiple LLM providers.

Module 09Prompt & Context Engineering14 topics
  1. Prompt Engineering Fundamentals
  2. System, Developer, and User Instructions
  3. Zero-Shot and Few-Shot Prompting
  4. Role and Task Definition
  5. Structured Output Design
  6. Prompt Templates and Versioning
  7. Decomposition and Reasoning-Oriented Prompts
  8. Context Engineering Fundamentals
  9. Context Window Management
  10. Context Compression and Summarization
  11. Grounding and Source Attribution
  12. Prompt Injection Fundamentals
  13. Prompt Testing and A/B Experiments
  14. Building Reusable Prompt Libraries
Module 10Building LLM-Powered Applications12 topics
  1. AI Chat Application Architecture
  2. Stateful and Stateless Conversations
  3. Conversation History Management
  4. Chat Session Persistence
  5. Token-Aware History Truncation
  6. Streaming Chat Interfaces
  7. User Authentication and Session Isolation
  8. Multi-Tenant AI Applications
  9. AI Gateway Design Patterns
  10. Error Handling and Graceful Degradation
  11. Usage Tracking and Quotas
  12. Deploying an End-to-End LLM Application

Hands-on practice

Build a chat platform with persistent sessions, streaming and model selection.

03Phase 3 · About 8 weeksRAG & Knowledge Engineering

Build document retrieval pipelines with source attribution, access controls and measurable quality.

Module 11Embeddings & Vector Search12 topics
  1. What Are Embeddings?
  2. Dense vs Sparse Representations
  3. Embedding Model Architectures
  4. Similarity Metrics: Cosine, Dot Product, Euclidean
  5. Semantic Search
  6. Approximate Nearest Neighbor Search
  7. HNSW, IVF, and Vector Indexing
  8. Vector Quantization
  9. Embedding Dimensions and Model Selection
  10. Multilingual Embeddings
  11. Embedding Evaluation
  12. Embedding Drift and Versioning
Module 12Vector Databases & Hybrid Search12 topics
  1. Vector Database Internals
  2. PostgreSQL with pgvector
  3. Qdrant and Weaviate
  4. Milvus and Managed Vector Services
  5. Elasticsearch Vector Search
  6. Metadata Filtering
  7. Hybrid Search: BM25 + Dense Retrieval
  8. Reciprocal Rank Fusion
  9. Index Building and Updates
  10. Sharding and Replication
  11. Multi-Tenant Retrieval Isolation
  12. Vector Database Performance Benchmarking

Hands-on practice

Compare retrieval with pgvector, Qdrant and Elasticsearch.

Module 13Retrieval-Augmented Generation15 topics
  1. Why RAG Exists
  2. RAG vs Fine-Tuning vs Long Context
  3. End-to-End RAG Architecture
  4. Document Ingestion Pipelines
  5. PDF, HTML, DOCX, and Markdown Parsing
  6. Text Chunking Strategies
  7. Semantic and Structure-Aware Chunking
  8. Parent-Child Retrieval
  9. Document Metadata and Provenance
  10. Query Embedding and Retrieval
  11. Context Assembly
  12. Grounded Answer Generation
  13. Citations and Source Verification
  14. Incremental Indexing and Document Updates
  15. Permission-Aware Retrieval

Hands-on practice

Build a RAG assistant that answers questions about documents with citations.

Module 14Advanced RAG Architectures15 topics
  1. Naive RAG vs Advanced RAG
  2. Query Rewriting and Expansion
  3. Multi-Query Retrieval
  4. HyDE - Hypothetical Document Embeddings
  5. Cross-Encoder Reranking
  6. Contextual Retrieval
  7. Adaptive and Corrective RAG
  8. Self-RAG Concepts
  9. Agentic RAG
  10. GraphRAG and Knowledge Graphs
  11. Multi-Hop Reasoning and Retrieval
  12. Multi-Source Federated Retrieval
  13. Multimodal RAG
  14. Long-Context RAG Optimization
  15. Production RAG Architecture Trade-offs
Module 15RAG Evaluation & Optimization14 topics
  1. Retrieval Evaluation Fundamentals
  2. Precision@K and Recall@K
  3. Mean Reciprocal Rank and nDCG
  4. Context Precision and Context Recall
  5. Answer Faithfulness and Groundedness
  6. Answer Relevance
  7. Hallucination Detection
  8. Building Golden Evaluation Datasets
  9. Synthetic Question Generation
  10. RAGAS and RAG Evaluation Tooling
  11. Latency, Cost, and Quality Trade-offs
  12. RAG Regression Testing
  13. A/B Testing Retrieval Strategies
  14. Diagnosing Retrieval vs Generation Failures

Hands-on practice

Build an enterprise knowledge assistant with permission-aware retrieval, hybrid search, reranking and evaluation.

04Phase 4 · About 9 weeksAgentic AI & Intelligent Workflows

Connect tools, memory and durable workflows with explicit permissions and failure recovery.

Module 16AI Agents Fundamentals13 topics
  1. What Is an AI Agent?
  2. Chatbots vs Workflows vs Autonomous Agents
  3. Agent Architecture and Components
  4. The Agent Execution Loop
  5. Reasoning, Actions, and Observations
  6. ReAct and Tool-Using Agents
  7. Planning and Task Decomposition
  8. Agent State Management
  9. Tool Selection and Execution
  10. Stopping Conditions and Execution Budgets
  11. Deterministic vs Autonomous Behavior
  12. Failure Recovery and Agent Reliability
  13. Designing Agents Without Frameworks

Hands-on practice

Build a tool-calling agent without a framework.

Module 17Tool Calling & Model Context Protocol16 topics
  1. Function Calling Deep Dive
  2. Tool Schemas and Argument Validation
  3. Parallel and Sequential Tool Calling
  4. Tool Result Handling
  5. Introduction to Model Context Protocol
  6. MCP Host, Client, and Server Architecture
  7. MCP Tools, Resources, and Prompts
  8. MCP Transport: stdio and Streamable HTTP
  9. Capability Discovery and Negotiation
  10. Building a Custom MCP Server
  11. Building an MCP Client
  12. Authentication and Authorization
  13. MCP Security and Trust Boundaries
  14. Tool Permissions and Human Approval
  15. Connecting Databases and External APIs
  16. Debugging and Testing MCP Integrations

Hands-on practice

Build an MCP server with restricted access to services such as PostgreSQL, Jira and GitHub.

Module 18LangChain, LangGraph & Agent Frameworks15 topics
  1. LangChain Architecture and Abstractions
  2. Models, Messages, and Tools
  3. Building a Simple LangChain Agent
  4. LangGraph Architecture
  5. Nodes, Edges, and State
  6. Conditional Routing
  7. Checkpointing and Persistence
  8. Short-Term and Long-Term Memory
  9. Interruptions and Human-in-the-Loop
  10. Durable Execution and Resumability
  11. OpenAI Agents SDK
  12. Agent Handoffs and Guardrails
  13. LlamaIndex and Knowledge Agents
  14. Framework Comparison and Selection
  15. Avoiding Unnecessary Framework Complexity

Hands-on practice

Compare the same agent workflow in pure Python, LangGraph and the Agents SDK.

Module 19AI Agent Memory & Context Management14 topics
  1. What Is Memory in AI Systems?
  2. Short-Term vs Long-Term Memory
  3. Working, Episodic, and Semantic Memory
  4. Conversation Memory
  5. Persistent Memory Stores
  6. Memory Extraction and Summarization
  7. Context Compression
  8. Memory Retrieval and Relevance Ranking
  9. User Preferences and Personalization
  10. Memory Consolidation and Forgetting
  11. Memory Conflicts and Stale Information
  12. Privacy, Consent, and Data Deletion
  13. Memory Evaluation and Testing
  14. Designing a Persistent Personal AI Assistant
Module 20Multi-Agent Systems & Orchestration16 topics
  1. Single-Agent vs Multi-Agent Architectures
  2. Agent Roles and Specialization
  3. Supervisor and Worker Patterns
  4. Sequential Agent Pipelines
  5. Parallel Agent Execution
  6. Routing and Dynamic Delegation
  7. Agent Handoffs
  8. Planner-Executor Patterns
  9. Evaluator-Optimizer Patterns
  10. Multi-Agent Shared State
  11. Agent-to-Agent Communication
  12. A2A Protocol Concepts
  13. Agent Coordination and Conflict Resolution
  14. Agentic Workflow Testing
  15. Multi-Agent Cost and Latency Optimization
  16. When Not to Use Multi-Agent Systems

Hands-on practice

Build a research platform with researcher, analyst, reviewer and report writer agents.

Module 21Durable & Event-Driven AI Workflows16 topics
  1. Why AI Agents Need Durable Execution
  2. Long-Running Agent Tasks
  3. Workflow State and Persistence
  4. Temporal for AI Orchestration
  5. Workflow and Activity Boundaries
  6. Retries, Timeouts, and Heartbeats
  7. Signals, Queries, and Human Approval
  8. Idempotency and Side-Effect Control
  9. Compensation and Recovery Patterns
  10. Kafka and Event-Driven AI Processing
  11. Asynchronous Job Execution
  12. Queue-Based Inference Workloads
  13. Distributed Agent Coordination
  14. Recovery After Worker Failure
  15. End-to-End Trace Correlation
  16. Designing Reliable AI Automation Systems

Hands-on practice

Build a durable automation platform with Temporal, AI agents and Kafka.

05Phase 5 · About 8 weeksModel Engineering & Multimodal AI

Run and adapt open-weight models, process images and speech, and benchmark inference.

Module 22Open-Weight LLMs & Local Inference12 topics
  1. Open-Weight Model Ecosystem
  2. Hugging Face Hub and Model Repositories
  3. Model Cards, Licenses, and Usage Restrictions
  4. Model Weights and Checkpoint Formats
  5. Loading Models with Transformers
  6. CPU vs GPU Inference
  7. VRAM and Memory Estimation
  8. Ollama and Local Model Runtimes
  9. Quantization: FP16, BF16, INT8, INT4
  10. GGUF and Quantized Model Formats
  11. Local Embedding and Reranking Models
  12. Model Quality and Hardware Trade-offs
Module 23Fine-Tuning & Model Adaptation15 topics
  1. Pretraining vs Fine-Tuning
  2. Instruction Tuning and Supervised Fine-Tuning
  3. Training Dataset Construction
  4. Chat Templates and Conversation Formats
  5. Full Fine-Tuning vs Parameter-Efficient Fine-Tuning
  6. LoRA and QLoRA
  7. Adapter Training and Management
  8. Training Hyperparameters
  9. Dataset Quality and Contamination
  10. Preference Optimization and DPO
  11. RLHF and Reinforcement Learning Concepts
  12. Distillation and Smaller Specialized Models
  13. Evaluating Fine-Tuned Models
  14. Model Versioning and Deployment
  15. When Fine-Tuning Is Not the Right Solution

Hands-on practice

Fine-tune a small open-weight instruction model on domain data with LoRA.

Module 24Multimodal AI Engineering15 topics
  1. Introduction to Multimodal Models
  2. Vision-Language Model Architecture
  3. Image Understanding and Visual Question Answering
  4. OCR and Document AI
  5. Tables, Charts, and Layout Understanding
  6. Speech Recognition and Transcription
  7. Text-to-Speech Systems
  8. Speech-to-Speech Architectures
  9. Audio Streaming and Voice Activity Detection
  10. Image Generation Fundamentals
  11. Diffusion Models and Latent Representations
  12. Video Understanding and Frame Processing
  13. Multimodal Embeddings
  14. Multimodal RAG Architecture
  15. Evaluating Multimodal Outputs

Hands-on practice

Build an assistant that reads documents, analyzes images and responds in text and speech.

Module 25Realtime Voice & Conversational AI15 topics
  1. Realtime AI System Architecture
  2. Speech-to-Text → LLM → Text-to-Speech Pipelines
  3. Native Speech-to-Speech Models
  4. WebSocket and WebRTC Fundamentals
  5. Audio Encoding and Streaming
  6. Voice Activity Detection
  7. Interruption and Barge-In Handling
  8. Turn Detection and Conversation Flow
  9. Realtime Function Calling
  10. Streaming Tool Results
  11. Latency Budgeting
  12. Voice Agent State Management
  13. Multilingual Voice Assistants
  14. Realtime Quality and Latency Testing
  15. Production Voice Agent Architecture
Module 26Efficient LLM Inference & Serving16 topics
  1. LLM Serving Architecture
  2. Prefill vs Decode Phases
  3. Time to First Token and Tokens per Second
  4. Throughput vs Latency
  5. Continuous Batching
  6. KV Cache Management
  7. PagedAttention
  8. vLLM and Model Serving Engines
  9. Speculative Decoding
  10. Quantization for Serving
  11. Tensor and Pipeline Parallelism
  12. GPU Memory Management
  13. Model Warmup and Cold Starts
  14. Autoscaling GPU Inference
  15. Model Routing and Load Balancing
  16. Serving Benchmarks and Capacity Planning

Hands-on practice

Serve a model with vLLM and benchmark concurrency, throughput and load.

06Phase 6 · About 9 weeksProduction AI, LLMOps & Security

Evaluate, secure, deploy and operate AI systems with tracing, capacity planning and release gates.

Module 27LLM Evaluation & AI Testing16 topics
  1. Why Traditional Testing Is Not Enough
  2. Deterministic vs Probabilistic Outputs
  3. Unit, Integration, and End-to-End AI Tests
  4. Golden Datasets and Test Cases
  5. Exact Match, Semantic Similarity, and Task Success
  6. LLM-as-a-Judge
  7. Pairwise Model Comparisons
  8. Human Evaluation and Labeling
  9. Agent Trajectory Evaluation
  10. Tool-Calling Accuracy
  11. Hallucination and Grounding Tests
  12. Adversarial and Safety Evaluation
  13. Offline and Online Evaluations
  14. Evaluation-Driven Development
  15. CI/CD Evaluation Gates
  16. Production Quality Monitoring
Module 28AI Security, Privacy & Guardrails16 topics
  1. Threat Modeling for AI Applications
  2. OWASP Top 10 for LLM Applications
  3. Direct and Indirect Prompt Injection
  4. Jailbreaks and Instruction Hierarchy Attacks
  5. Sensitive Information Disclosure
  6. Tool Output and Document Trust Boundaries
  7. Excessive Agency and Privilege Escalation
  8. Data and Model Poisoning
  9. Secure Tool Execution and Sandboxing
  10. Least-Privilege Agent Permissions
  11. PII Detection and Redaction
  12. Input, Output, and Retrieval Guardrails
  13. Human Approval for High-Impact Actions
  14. API Key and Secret Management
  15. Audit Logs and Retention Policies
  16. AI Red Teaming and Security Testing

Hands-on practice

Build a defense-in-depth test environment for prompt injection against RAG and agent applications.

Module 29LLMOps, Observability & Monitoring16 topics
  1. MLOps vs LLMOps
  2. AI Application Lifecycle Management
  3. Prompt, Model, and Dataset Versioning
  4. Experiment Tracking
  5. LangSmith, Langfuse, and Phoenix Concepts
  6. Distributed Tracing for LLM Applications
  7. OpenTelemetry Integration
  8. Model Invocation and Tool Call Traces
  9. Token and Cost Observability
  10. Latency Percentiles and SLOs
  11. Quality and Safety Monitoring
  12. Evaluation Dashboards
  13. Feedback Collection
  14. Model Drift and Regression Detection
  15. Alerts, Incident Response, and Rollbacks
  16. Production AI Operations Runbooks
Module 30Scalable AI System Design16 topics
  1. Designing an AI System for High Traffic
  2. AI Gateway and Model Router Architecture
  3. Stateless APIs and Stateful Workflows
  4. API Rate Limits and Admission Control
  5. Asynchronous Inference Queues
  6. Load Shedding and Backpressure
  7. Request Batching and Concurrency Control
  8. Prompt and Semantic Caching
  9. GPU Resource Scheduling
  10. Distributed Vector Search
  11. Multi-Region AI Infrastructure
  12. Tenant Isolation and Resource Quotas
  13. AI Service Resilience Patterns
  14. End-to-End Capacity Estimation
  15. Cost per Request and Unit Economics
  16. Designing for 100K+ Daily Active Users
Module 31AI Deployment, Cloud & Infrastructure16 topics
  1. Dockerizing AI Services
  2. Kubernetes Fundamentals for AI Workloads
  3. CPU and GPU Workload Scheduling
  4. Model Artifact Storage
  5. Model Registry and Deployment Versioning
  6. CI/CD for AI Applications
  7. Infrastructure as Code
  8. Managed AI Platforms vs Self-Hosting
  9. AWS, Azure, and GCP AI Infrastructure
  10. Kubernetes Autoscaling
  11. Secrets and Configuration Management
  12. Blue-Green and Canary Deployments
  13. Model Rollbacks
  14. Data Pipeline Scheduling
  15. Disaster Recovery and Business Continuity
  16. Production Release Checklist
Module 32Advanced AI Architecture & Emerging Patterns16 topics
  1. AI-Native Software Architecture
  2. Compound AI Systems
  3. Model and Tool Routing Architectures
  4. Agent Skills and Reusable Capabilities
  5. Sandboxed Code Execution for Agents
  6. Computer-Use Agents
  7. Browser and UI Automation Agents
  8. Deep Research Workflows
  9. Long-Running Autonomous Systems
  10. Agent Communication Protocols
  11. Knowledge Graphs and Neuro-Symbolic Approaches
  12. Synthetic Data Generation
  13. Small Specialized Model Architectures
  14. On-Device and Edge AI
  15. Evaluating New AI Technologies
  16. AI Architecture Decision Records

Hands-on practice

Build a production AI platform with monitoring, security, evaluation, distributed infrastructure and deployment.

A portfolio of 12 AI projects

  • Machine Learning Prediction Service
    Model training, feature engineering, evaluation and FastAPI deployment.
  • Multi-Model AI Chat Platform
    LLM gateway, streaming, persistent sessions, model routing and usage tracking.
  • Intelligent Document Assistant
    PDF processing, embeddings, pgvector, grounded answers and citations.
  • Enterprise RAG Search Engine
    Hybrid retrieval, reranking, tenant isolation, evaluation and incremental indexing.
  • Custom MCP Integration Platform
    MCP server, tool registry, schema validation, secure execution and audit logging.
  • Personal AI Assistant with Memory
    Agent state, long-term memory, personalized context and external actions.
  • Multi-Agent Research System
    Supervisor, researcher, evaluator, report writer, handoffs and agent traces.
  • Realtime Voice AI Assistant
    Speech streaming, interruption handling, tool calls and low-latency responses.
  • Domain-Specific Fine-Tuned LLM
    LoRA adapters, training datasets, evaluation and optimized local inference.
  • Intelligent Document Processing Pipeline
    OCR, VLM, structured extraction, confidence checks, review and durable workflows.
  • AI Evaluation & Observability Platform
    Golden datasets, prompt experiments, traces, regression testing and cost dashboards.
  • Production AI Engineering Capstone
    Multi-tenant AI SaaS with RAG, agents, MCP, Temporal, evaluation, security and scalable deployment.
FINAL CAPSTONE

AI Enterprise Workspace

Build a multi-tenant platform with knowledge search, personal memory, MCP integrations, specialized agents, voice commands, approval workflows, monitoring and usage billing.

  • Reproducible evaluation dataset
  • Permission and tenant isolation tests
  • Fault-injection and recovery scenarios
  • Audit trail and deployment pipeline
  • Predefined latency, cost and quality SLOs

Proposed assessment

Theory quizzes
15%
Hands-on labs
25%
Module projects
25%
Architecture challenges
10%
Final capstone
25%

The proposed pass threshold is 75%, with 90% as an advanced achievement target. This is a course assessment model, not an industry certification.