Certifications

Master the fundamentals of AI and LLMs — foundation models, prompt engineering, RAG, AI APIs, and AI-powered development tools. Free course; a free account is required for full access.

Go deeper with cloud AI platforms — Azure AI Foundry, Google Vertex AI, AWS Bedrock, Hugging Face, and LLM evaluation and fine-tuning techniques.

Build production-grade AI systems — agentic frameworks (ADK, LangGraph), Claude SDK, AI observability, self-hosted model deployment, and AI safety.

Validate knowledge of AI, machine learning, and generative AI concepts and AWS AI services.

Understand AWS Cloud concepts, core services, security, architecture, pricing, and support — the essential foundation for any cloud career.

Ingest, transform, store, and secure data pipelines on AWS using Glue, Kinesis, Redshift, and Lake Formation.

Build, deploy, and debug cloud-native applications on AWS using Lambda, DynamoDB, API Gateway, and CI/CD pipelines.

Build, operationalize, deploy, and maintain machine learning solutions and pipelines on AWS.

Design, implement, deploy, and maintain ML solutions on AWS using SageMaker, data engineering, and MLOps best practices.

Design secure, resilient, high-performing, cost-optimized architectures on AWS.

Design and evolve complex, cost-optimized, and resilient enterprise architectures across AWS.

Describe AI concepts and responsible AI, then build your first apps and agents with Microsoft Foundry.

Manage Azure identities, governance, storage, compute, and virtual networks.

Demonstrate core data concepts and how they're implemented with Microsoft Azure data services.

Prove foundational knowledge of cloud concepts, Azure services, and Azure management and governance.

Design identity, governance, monitoring, data storage, business continuity, and infrastructure solutions on Azure.

Deploy applications, monitor operations, and manage enterprise solutions on Google Cloud.

Design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud.

Design, build, and operationalize data processing systems on Google Cloud.

Architect, build, scale, and monitor ML solutions on Google Cloud.

Build, integrate, and manage advanced, scalable AI agents using Microsoft Copilot Studio, Power Platform, Foundry, and MCP for enterprise-grade solutions.

Architect enterprise agentic-first AI solutions across Copilot Studio, Microsoft Foundry, Power Platform, and Dynamics 365 — from planning and design through deployment, ALM, and responsible AI governance.

Build generative AI apps and agents on Microsoft Foundry, plus vision, text analysis, and information extraction solutions.

Implement data engineering solutions on Azure Databricks with Unity Catalog and Lakeflow.

Design, build, optimize, and deploy scalable production-ready multi-agent AI systems and workflows across Azure, Microsoft Foundry, Model Context Protocol (MCP), and orchestration frameworks.

Build AI-enabled database solutions across SQL Server, Azure SQL, and Fabric with vectors and RAG.

Design, build, and manage enterprise analytics solutions with Microsoft Fabric.

Build data engineering solutions with Microsoft Fabric: ingestion, transformation, and optimization.

Design, implement and operate identity and access management with Microsoft Entra: user and hybrid identities, authentication, Conditional Access, workload identities and identity governance.

Run MLOps and GenAIOps on Azure Machine Learning and Microsoft Foundry.

Prepare, model, visualize, and analyze data with Microsoft Power BI.

Build ELT pipelines and production workflows on the Databricks Lakehouse.

Demonstrate advanced data engineering on Databricks: production code in Python and SQL, ingestion, transformation and quality, data sharing, monitoring, cost/performance optimization, security, governance, and deployment.

Design, build, and deploy LLM-powered applications on Databricks.

Use Databricks Machine Learning and MLflow to build, develop, and deploy machine learning models on the Lakehouse.

Operationalize machine learning at scale on Databricks: advanced MLflow, model lifecycle and deployment, MLOps automation, and Lakehouse Monitoring.

Architect enterprise-scale Snowflake solutions covering multi-cluster warehouses, data sharing, and performance tuning.

Build and optimize production data pipelines in Snowflake with Streams, Tasks, Dynamic Tables, and Snowpark.

Prove working knowledge of the Snowflake AI Data Cloud: architecture, accounts, loading, transformation, performance and collaboration.

Build generative AI on Snowflake: Cortex LLM functions, Cortex Search and Analyst for RAG, governance, and document parsing.

Develop, test, document, and deploy dbt models like a professional analytics engineer.

Prove fundamental Terraform Community Edition and HCP Terraform concepts and skills, tested against Terraform 1.12.

Author, maintain, and consume GitHub Actions workflows and custom actions to automate CI/CD pipelines and developer workflows.

Operate, integrate, supervise, and govern AI agents in production SDLC workflows — tool use and MCP, memory and state, evaluation and tuning, multi-agent orchestration, and guardrails, with GitHub as the control plane.

Demonstrate knowledge of GitHub Copilot features, responsible AI principles, and practical developer use cases across IDE, CLI, and code review.

Demonstrate foundational knowledge of Git and GitHub — repositories, branches, commits, pull requests, Issues, Actions, and project administration.

Build, test, and monitor event-streaming applications on Apache Kafka and Kafka Streams.

Demonstrate core Apache Airflow knowledge: architecture, DAG authoring, scheduling, and the UI/CLI.

Prove foundational Tableau Desktop skills: connecting to and preparing data, exploring, analyzing, and sharing insights.