21nov
Karnataka Digital Economy Mission Recognition at BTS
MicroDegree receiving an award for its impactful work at the grassroots level.
Generative AI & Agentic AI Certification Program
4.7 Google Reviews
- Build projects and learn.
- Learn 10x Faster in Kannada.
- Get Trained by Experts from OpenAI, Amazon, Google, NVIDIA & more.
- Master Generative AI, LLMs, Prompt Engineering, RAG, and Agentic AI Systems
Key Highlights
Duration: 3 - 4 months
Instructor led Live online classes
Morning & Evening batches focused for IT professionals
The only platform to teach in kannada
Community support for doubt clarification
Course Completion / Internship Certificate
Career training for resume building & mock interviews
Assured interview opportunities*
4.7
Google Reviews
4.8
Glassdoor
4.8
Shiksha
ಕನ್ನಡದ No.1 Tech-Skilling Platform
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Our Mission: Skill 5 lakh Learners by 2027
Meet Our Trainers & Experts
Gaurav Kamath
Co-Founder | Tech Education Expert | Ex-Infosys, Accenture, Tracxn
With 10+ years of industry experience, Gaurav has worked with leading IT firms like Infosys and Accenture. An engineer from Nitte, he now focuses on building practical Cloud and DevOps learning programs for modern tech careers.
Rakesh Kothari
Co-Founder | Cloud & Automation Mentor | Ex-TCS, Bank of America
Rakesh brings 15+ years of IT expertise across TCS and Bank of America. An engineer from BMS College, he specializes in Cloud, Automation, and AI-driven solutions that help professionals upskill faster.
Manikanta Nair
Co-Founder | AI & Full Stack Mentor | Ex-Industry Professional
With 7+ years of experience, Manikanta has developed scalable tech solutions and mentored learners in AI and Cloud technologies. An engineer from PACE, he focuses on simplifying next-gen tech concepts through practical, hands-on learning.
What are the top job roles in Generative AI?
AI Developer / Gen AI Engineer
Designs and builds Gen AI based apps using LLMs and APIs (OpenAI, Bedrock, Gemini).
LLM Application Developer
Integrates AI APIs and frameworks like LangChain, CrewAI, and LangGraph for business applications.
Agentic AI Engineer
Builds multi-agent systems capable of reasoning, collaboration, and autonomous execution.
AI Product Builder
Develops AI-based copilots and assistants that solve domain-specific problems.
AI Solutions Architect
Designs scalable and secure AI workflows integrating LLMOps, RAG, and orchestration frameworks.
Data & Cloud Professional (AI Track)
Upskills into Generative AI to deploy AI applications on AWS, Azure, and GCP.
MicroDegree vs Competition — Feature Comparison
Feature
MicroDegree
Typical Competitors
Language advantage
MicroDegree
Learn 10x faster in Kannada
Typical Competitors
Mostly English only delivery
Language advantage
Learn 10x faster in Kannada
Mostly English only delivery
Batch flexibility
MicroDegree
Switch any batches within a year
Typical Competitors
Fixed batches, limited flexibility
Batch flexibility
Switch any batches within a year
Fixed batches, limited flexibility
Trainer quality
MicroDegree
Active IT professionals & industry experts
Typical Competitors
Professors with limited real world exposure
Trainer quality
Active IT professionals & industry experts
Professors with limited real world exposure
Course pricing
MicroDegree
Affordable & ROI-driven pricing
Typical Competitors
Often charge ₹1 - 3+ lakhs
Course pricing
Affordable & ROI-driven pricing
Often charge ₹1 - 3+ lakhs
Target audience
MicroDegree
Designed for mid & senior IT professionals
Typical Competitors
Cater to everyone → mixed cohort quality
Target audience
Designed for mid & senior IT professionals
Cater to everyone → mixed cohort quality
Tools Used
OpenAI
Bedrock
MCP
CrewAI
Dotenv
Chroma
Gradio
Pinecone
Pydantic
Jsonschema
Docker
StreamlitGenerative AI Course Curriculum
9 modules
Module 1 – AI Foundations & Prompting for IT Professionals
- Program orientation and how LLMs really work: tokens, context window, training vs inference, why models hallucinate. Each learner states the one job task they will improve by the capstone.
- The prompt pattern Task → Context → Input → Constraints → Output → Check; managing context across a conversation; AI as a tutor for any new tool or technology.
- Validating output and handling data responsibly: what may and may not be pasted into an AI tool, company policy, citing sources.
Module 2 – Claude Environment: Chat, Projects & Artifacts
- Chat versus workspace: setting up a Project with instructions and reference files; what the AI sees and what it does not.
- Document analysis: long documents, comparing versions, extracting tables, answering with citations.
- Building a role assistant: instructions, a prompt library, a voice and limits; quality checklists and sharing it with a team.
- Artifacts: editable documents, tables, Mermaid diagrams and simple interactive pages.
- Professional documents and training content: proposals and policies in house style; turning an SOP into a course outline, job aid, quiz and slide deck as Word and PowerPoint files.
Module 3 – Data Views & Reports with AI
- Analysing data with AI: cleaning an export, grouping by week, month, quarter and year, and checking the totals.
- Charts, dashboards and summaries: one chart and one paragraph per period, written for a manager.
Module 4 – Claude Skills, Connectors & Cowork
- Claude Skills: packaging a repeatable procedure so anyone can run it.
- Connectors and their risks: drive, email, calendar, Jira, Confluence; read versus write access, least privilege, and an everyday introduction to prompt injection.
- Claude Cowork and Claude in Chrome: multi-step tasks over files, apps and web pages, where they fail, where a human must approve.
Module 5 – Workflow Automation with Claude Cowork
- The pattern: trigger → AI transformation → approval → destination, and the six workflow patterns built on it. Building them with Cowork, Connectors and a Skill.
- AI steps that do real work: classification, extraction, summarisation into fixed fields.
- Ticket-to-Jira with human approval; documentation automation into Confluence; turning the flow into a scheduled task.
- Testing workflows: edge cases, failures, usage limits and reliability; what Cowork cannot do yet, and where Part 2 code takes over.
Module 6 – No-Code AI Agents & Prototyping
- Agents versus workflows; giving Cowork two or three connected tools, an approval step and a schedule, so it drafts a business report from the 1.3 views.
- Prototyping without deep coding: interactive and AI-powered prototypes in Artifacts, screens and clickable mock-ups in Claude Design, and a quick user test.
Module 7 – Role-Based AI Playbooks
- Role pods. Each pod works through its playbook with an instructor or senior peer.
- Build your AI Work Kit for one real task: an SOP, a short presentation and a process diagram.
- Cross-role showcase: each pod demos to the others. PMs see how QA uses AI, developers see how operations does, and so on.
Module 8 – Leading with AI
- AI strategy, governance and adoption for leaders: use-case portfolio, business case, team AI policy, data and risk basics, and presenting AI results to leadership.
Module 9 – AI Operating System & Capstone
- Your personal AI operating system and capstone clinic: prompt library, assistants, automations and a weekly routine; measuring impact; instructor review of each capstone plan.
- Capstone demos.
15 modules
Module 1 – AI Foundations & Prompting for IT Professionals
- Program orientation and how LLMs really work: tokens, context window, training vs inference, why models hallucinate. Each learner states the one job task they will improve by the capstone.
- The prompt pattern Task → Context → Input → Constraints → Output → Check; managing context across a conversation; AI as a tutor for any new tool or technology.
- Validating output and handling data responsibly: what may and may not be pasted into an AI tool, company policy, citing sources.
Module 2 – Claude Environment: Chat, Projects & Artifacts
- Chat versus workspace: setting up a Project with instructions and reference files; what the AI sees and what it does not.
- Document analysis: long documents, comparing versions, extracting tables, answering with citations.
- Building a role assistant: instructions, a prompt library, a voice and limits; quality checklists and sharing it with a team.
- Artifacts: editable documents, tables, Mermaid diagrams and simple interactive pages.
- Professional documents and training content: proposals and policies in house style; turning an SOP into a course outline, job aid, quiz and slide deck as Word and PowerPoint files.
Module 3 – Data Views & Reports with AI
- Analysing data with AI: cleaning an export, grouping by week, month, quarter and year, and checking the totals.
- Charts, dashboards and summaries: one chart and one paragraph per period, written for a manager.
Module 4 – Claude Skills, Connectors & Cowork
- Claude Skills: packaging a repeatable procedure so anyone can run it.
- Connectors and their risks: drive, email, calendar, Jira, Confluence; read versus write access, least privilege, and an everyday introduction to prompt injection.
- Claude Cowork and Claude in Chrome: multi-step tasks over files, apps and web pages, where they fail, where a human must approve.
Module 5 – Workflow Automation with Claude Cowork
- The pattern: trigger → AI transformation → approval → destination, and the six workflow patterns built on it. Building them with Cowork, Connectors and a Skill.
- AI steps that do real work: classification, extraction, summarisation into fixed fields.
- Ticket-to-Jira with human approval; documentation automation into Confluence; turning the flow into a scheduled task.
- Testing workflows: edge cases, failures, usage limits and reliability; what Cowork cannot do yet, and where Part 2 code takes over.
Module 6 – No-Code AI Agents & Prototyping
- Agents versus workflows; giving Cowork two or three connected tools, an approval step and a schedule, so it drafts a business report from the 1.3 views.
- Prototyping without deep coding: interactive and AI-powered prototypes in Artifacts, screens and clickable mock-ups in Claude Design, and a quick user test.
Module 7 – Role-Based AI Playbooks
- Role pods. Each pod works through its playbook with an instructor or senior peer.
- Build your AI Work Kit for one real task: an SOP, a short presentation and a process diagram.
- Cross-role showcase: each pod demos to the others. PMs see how QA uses AI, developers see how operations does, and so on.
Module 8 – Leading with AI
- AI strategy, governance and adoption for leaders: use-case portfolio, business case, team AI policy, data and risk basics, and presenting AI results to leadership.
Module 9 – AI Operating System & Capstone
- Your personal AI operating system and capstone clinic: prompt library, assistants, automations and a weekly routine; measuring impact; instructor review of each capstone plan.
- Capstone demos.
Module 10 – Python for AI Engineering
- Setup: Python, Colab/Jupyter, VS Code; working with an AI coding assistant: asking for code, reading it, running it.
- Python essentials in context: variables, strings, lists, dictionaries and loops, read and modified with AI help.
- Functions, modules, virtual environments and pip; debugging with the assistant.
- Files, JSON and CSV; classes and dataclasses.
- Error handling and logging; reviewing AI-generated code for silent failures.
- HTTP and REST with requests/httpx; authentication and API keys.
- Environment variables, python-dotenv and secret hygiene; checking AI-written code for leaked keys.
- Pydantic models and validation.
- Structured outputs: parsing and validating JSON returned by an LLM.
- Async basics with asyncio and httpx for concurrent calls.
- Boto3 and Amazon Bedrock basics; packaging a reusable LLM helper with light pytest tests.
- Mini-project.
21 modules
Module 1 – AI Foundations & Prompting for IT Professionals
- Program orientation and how LLMs really work: tokens, context window, training vs inference, why models hallucinate. Each learner states the one job task they will improve by the capstone.
- The prompt pattern Task → Context → Input → Constraints → Output → Check; managing context across a conversation; AI as a tutor for any new tool or technology.
- Validating output and handling data responsibly: what may and may not be pasted into an AI tool, company policy, citing sources.
Module 2 – Claude Environment: Chat, Projects & Artifacts
- Chat versus workspace: setting up a Project with instructions and reference files; what the AI sees and what it does not.
- Document analysis: long documents, comparing versions, extracting tables, answering with citations.
- Building a role assistant: instructions, a prompt library, a voice and limits; quality checklists and sharing it with a team.
- Artifacts: editable documents, tables, Mermaid diagrams and simple interactive pages.
- Professional documents and training content: proposals and policies in house style; turning an SOP into a course outline, job aid, quiz and slide deck as Word and PowerPoint files.
Module 3 – Data Views & Reports with AI
- Analysing data with AI: cleaning an export, grouping by week, month, quarter and year, and checking the totals.
- Charts, dashboards and summaries: one chart and one paragraph per period, written for a manager.
Module 4 – Claude Skills, Connectors & Cowork
- Claude Skills: packaging a repeatable procedure so anyone can run it.
- Connectors and their risks: drive, email, calendar, Jira, Confluence; read versus write access, least privilege, and an everyday introduction to prompt injection.
- Claude Cowork and Claude in Chrome: multi-step tasks over files, apps and web pages, where they fail, where a human must approve.
Module 5 – Workflow Automation with Claude Cowork
- The pattern: trigger → AI transformation → approval → destination, and the six workflow patterns built on it. Building them with Cowork, Connectors and a Skill.
- AI steps that do real work: classification, extraction, summarisation into fixed fields.
- Ticket-to-Jira with human approval; documentation automation into Confluence; turning the flow into a scheduled task.
- Testing workflows: edge cases, failures, usage limits and reliability; what Cowork cannot do yet, and where Part 2 code takes over.
Module 6 – No-Code AI Agents & Prototyping
- Agents versus workflows; giving Cowork two or three connected tools, an approval step and a schedule, so it drafts a business report from the 1.3 views.
- Prototyping without deep coding: interactive and AI-powered prototypes in Artifacts, screens and clickable mock-ups in Claude Design, and a quick user test.
Module 7 – Role-Based AI Playbooks
- Role pods. Each pod works through its playbook with an instructor or senior peer.
- Build your AI Work Kit for one real task: an SOP, a short presentation and a process diagram.
- Cross-role showcase: each pod demos to the others. PMs see how QA uses AI, developers see how operations does, and so on.
Module 8 – Leading with AI
- AI strategy, governance and adoption for leaders: use-case portfolio, business case, team AI policy, data and risk basics, and presenting AI results to leadership.
Module 9 – AI Operating System & Capstone
- Your personal AI operating system and capstone clinic: prompt library, assistants, automations and a weekly routine; measuring impact; instructor review of each capstone plan.
- Capstone demos.
Module 10 – Python for AI Engineering
- Setup: Python, Colab/Jupyter, VS Code; working with an AI coding assistant: asking for code, reading it, running it.
- Python essentials in context: variables, strings, lists, dictionaries and loops, read and modified with AI help.
- Functions, modules, virtual environments and pip; debugging with the assistant.
- Files, JSON and CSV; classes and dataclasses.
- Error handling and logging; reviewing AI-generated code for silent failures.
- HTTP and REST with requests/httpx; authentication and API keys.
- Environment variables, python-dotenv and secret hygiene; checking AI-written code for leaked keys.
- Pydantic models and validation.
- Structured outputs: parsing and validating JSON returned by an LLM.
- Async basics with asyncio and httpx for concurrent calls.
- Boto3 and Amazon Bedrock basics; packaging a reusable LLM helper with light pytest tests.
- Mini-project.
Download Complete Curriculum for more details.
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- Step-by-step roadmap to build a Gen AI career
- What are LLMs, RAG & AI Agents (in simple terms)
- What is Agentic AI & why it’s important.
- Real-world Gen AI use cases and career paths
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Awards & Recognition
MicroDegree is proud to be acknowledged by some prominent publications and organizations
20mar
Winning Startup Elevate 2020
MicroDegree chosen as one of the top 100 startups by Govt. of Karnataka.
21feb
Awarded by IEEE
MicroDegree impacted 3000+ rural talent imparting tech sessions in Kannada.
16jan
NIDHI Seed Support Program
MicroDegree featured among 75 promising startups in the Azadi Ka Amrit Mahotsav compendium
Learners Review
I recently completed the AWS course, and I must say it was an excellent learning experience! The course content was well-structured, up-to-date, and covered all the essential AWS services. The instructors were knowledgeable, supportive, and explained complex concepts in an easy-to-understand manner.
malatesh
I took the AWS DevOps course and it was really good. They taught me a lot about AWS and how to do DevOps stuff. The hands-on practice helped me learn by doing. The teachers were helpful, and because of this course, I got a job! If you want to work in cloud computing, this course is worth it. Also The teachers were helpful, and the staff were very responsive when I had questions.
Arunkumar D
AWE Class was very informative and well structured, providing overview of AWS Services and hands on experience with Labs. Chitharanjan Sir was very knowledgeable and provided best explanations for complex concepts. Topics covered by Sir and explanation given which was easy to grasp and took Quizes on everyday to understand topics much more clear and also supportive and calm during all the time while clearing doubts. Thank you Team for the AWS course and would recommend to others who looking forward to learn AWS.
Darshan N.Kini
I recently completed my training in DevOps class taught by Sumeet Kulkarni sir, the training was excellent with good interaction, knowledge sharing is good and recording facility is excellent for revising. Course was practically and informative. Sumeet Sir was enthusiastic and really aware of what he is explaining and most supportive at all the time and making complex concepts much easier for students to understand and apply them in real-world scenarios. Overall, I would highly recommend Sumeet Kulkarni sir as a DevOps teacher to anyone looking to deepen their understanding of DevOps practices and principles.
Nagraj
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Frequently Asked Questions
What is the duration of dashboard access?
All live sessions are recorded and uploaded daily. You will have access to these recordings for 1 year from your enrollment date.
Is there flexibility in choosing batches?
Yes! You can join any of our morning or evening batches. Within 1 year of your enrollment, you may switch batches and complete your program as per your convenience.
What is the duration of the program?
We offer multiple learning plans. Depending on the plan you choose, the program duration can range from 3 months to 6 months.
How does career assistance work?
- We organize a Placement Drive every 2nd & 4th Saturday.
- During the drive, we assess your hands-on skills.
- Our team will line up interview opportunities for you within 6 months of clearing the placement drive.
What is your refund policy?
We follow a session-based + time-based refund system to ensure fairness for students and protect against misuse.
Refund Timeline:
- 100% Refund – Request within 24 hours of payment
- 50% Refund – Request within 4 days of batch start OR before attending up to 2 sessions (whichever is earlier)
- 25% Refund – Request within 7 days of batch start OR before attending up to 5 sessions (whichever is earlier)