Engineer AI Agents with Agent Development Kit (ADK)
Comprehensive certification in ADK agent runtime, multi-agent swarms, tool calling, memory management, and Cloud Run production deployments.
Certifications & Credentials
100% verified credentials in Google Cloud ADK & Multi-Agents, Stanford ML, Advanced RAG, and Applied Deep Learning.
Category:
Build Agent Skills with Google
Authoring 3-Tier agent skill architectures, self-correction scripts, Agents CLI lifecycle, and Enterprise Skill Registry integration.
Build Collaborative Multi-Agent Systems with ADK
Hierarchical multi-agent swarms, Agent-to-Agent (A2A) protocol, loop agents, sequential pipelines, and Two-Layer Shields.
Gemini for Application Developers
Multimodal reasoning, function calling, structured output with Pydantic, embeddings, and context window optimization.
Add Agent Capabilities With Tools
Deterministic Python custom tools, Model Context Protocol (MCP) integrations, API error handling, and guardrails.
Manage Agent Memory and State
Session state persistence, Firestore integration, multi-turn memory routing, and context variable safety.
Optimize Agent Behavior
Thinking budget configuration, automated regression evaluation with eval datasets, and prompt drift mitigation.
Build Agents with Agent Development Kit (ADK)
Core foundations of Google ADK, agent lifecycle, serverless deployment on Google Cloud Run, and IAM security.
Applied Machine Learning & Deep Learning
Deep Neural Networks, CNNs, Transformers, model evaluation metrics, feature engineering, and PyTorch implementations.
Supervised Machine Learning: Regression & Classification
Mathematical foundations of gradient descent, linear & logistic regression, regularization, decision boundaries, and loss optimization.
Python Basics & Core Algorithms
Algorithmic problem solving, data structures, OOP patterns, and clean code fundamentals.
Advanced RAG with Vector Databases & Retrievers
Hybrid search (Dense + Sparse), semantic chunking, re-ranking strategies, and pgvector / Chroma integration.
Vector Databases for RAG: An Introduction
Embeddings mathematical spaces, cosine similarity, HNSW indexing, and vector database clustering.
Build Multimodal Generative AI Applications
Processing audio, image, and text pipelines, vision-language models, and multimodal agent orchestration.
Build RAG Applications: Get Started
End-to-end document ingestion, embedding generation, context window injection, and hallucination reduction.
Develop Generative AI Applications: Get Started
LLM API integration, prompt engineering techniques, Few-Shot learning, and API shield architecture.
Advanced RAG System Implementation
Hands-on competency in building production-ready RAG retrievers, hybrid search, and vector databases.
Applied Generative AI Development & Strategy
Architectural design of enterprise GenAI workflows, fine-tuning considerations, and agentic workflows.
Foundational Programming & Software Architecture
Core software development patterns, data structures, algorithms, and clean code principles.
Machine Learning Foundations & Supervised Learning
Regression, classification, loss minimization, and model performance benchmarking.