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Practical by design
Spend 70% of the program on live demos, guided workflows and project work.
Live online course
AI for Data Professionals
Use GenAI, AI-assisted engineering and agentic AI to transform your work.
A practical, instructor-led program for data professionals who want practical AI capability, not just awareness. Learn to use AI in your current work, automate multi-step tasks and build dependable AI-assisted workflows.
Why join
Build AI capability around the work you already do.
The program is designed for working professionals. It connects AI to SQL, scripts, troubleshooting, documentation, analysis, engineering and architecture, with live demonstrations and a guided project.
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Built for data work
Apply AI to the tasks data professionals already perform, then extend and automate them.
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Vendor-neutral judgment
Learn how to choose models, tools and architectures for the problem in front of you.
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Beyond prompting
Progress from effective AI use to APIs, RAG, tools, agents and self-hosted models.
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Build with support
Turn a chosen problem into a working proof of concept through a guided build.
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Use AI responsibly
Practice validation, evaluation, guardrails, human review, security and access control.
Recommended for
DBAs
Database Engineers
Data Engineers
Data Analysts
Data Architects
Platform Engineers
Technical Professionals
Learning journey
From understanding AI to designing AI systems.
Each stage builds on the previous one so you can move from occasional AI use to designing a practical AI-assisted workflow for your work.
01 / Understand AI
Build the right mental models
Learn LLMs, context, model choice, responsible use, privacy and security.
02 / Use AI
Improve everyday data work
Apply GenAI to SQL, scripts, troubleshooting, documentation and analysis.
03 / Engineer with AI
Work across real codebases
Use AI coding tools for discovery, implementation, debugging, testing and review.
04 / Automate
Design controlled agents
Combine tools, planning, memory and guardrails for multi-step execution.
05 / Build with AI
Create practical applications
Use APIs, structured outputs, RAG, embeddings, tools and self-hosted models.
06 / Design systems
Think end to end
Bring together data, knowledge, tools, agents, evaluation and governance.
Program structure
A complete learning path for practical AI.
The program combines instructor-led learning, a guided project and dedicated doubt-clearing and support.
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AI Foundations for Data Professionals
LLMs, context, prompting, model choice, responsible use, privacy and security.
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AI Productivity for Data Work
SQL, Python and shell scripting, troubleshooting, root-cause analysis, documentation and data analysis.
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AI-Assisted Engineering
Claude Code, Codex, Cursor, repository context, debugging, testing, refactoring and review.
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Agentic AI
Agents, tools, planning, memory, state, guardrails, permissions, failure handling and human review.
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GenAI Applications and Self-Hosted LLMs
APIs, structured outputs, tool calling, embeddings, vector databases, RAG and model hosting.
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Enterprise AI Systems
Architecture, security, access control, evaluation, observability, reliability, latency and cost.
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Guided DIY Project
Choose a problem, design the workflow, build a first version, add intelligence, test, refine and demonstrate it.
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Doubt Clearing and Support
Bring a real problem for troubleshooting, architecture review, reliability review and next-step planning.
Outcomes
What you will be able to do.
Move from AI curiosity to practical, evidence-based AI capability.
- Use AI confidently with better context, prompting and tool selection.
- Work faster on SQL, scripting, analysis, troubleshooting and documentation.
- Engineer with AI while retaining technical judgment and review.
- Automate multi-step work using agents, tools and controlled actions.
- Build practical AI workflows and proofs of concept.
- Choose between APIs, RAG, tools, agents and self-hosted models.
- Validate outputs with tests, evidence, guardrails and human review.
- Think in AI systems: models, context, data, tools, actions and evaluation.
Instructor and contact
Learn with practical depth and real workflows.
The program is instructor-led, vendor-neutral and focused on building judgment: when to use AI, how to validate it and when not to use it.
Vinay C G
More than 18 years of experience across databases, data engineering, cloud platforms and AI.
I teach from first principles: start with the fundamentals, understand the architecture, then work upward to the tools.