About Course
About AWS AI Fundamentals
Delhi's Connaught Place and Nehru Place corridor mixes consulting firms, government-facing IT vendors and enterprise head offices, all of which carry cloud obligations. Public-sector and PSU work in particular requires vendors to evidence data residency and access control against whatever the tender names.
Working with public-sector clients means working to fixed documentation standards and procurement cycles. Graduates who can produce an architecture document that survives technical evaluation tend to do very well in this market, regardless of which console they learned it on.
This Delhi page carries the same 12-week, 72-hour AWS AI Fundamentals syllabus, faculty and assessment as the master programme, delivered live online and in hybrid format, with classroom batches at our Thoraipakkam centre.
Course Overview
12-week AWS AI Fundamentals training (72 hours) covering Amazon Bedrock, SageMaker, Rekognition, Comprehend and responsible AI practice. Live online and classroom.
Why Learn AWS AI Fundamentals
Public tenders name data residency and access-control requirements explicitly, so candidates who can evidence them against a named framework get contracted repeatedly.
Government-facing IT vendors carry an extra documentation burden that makes cloud people who can write design documents disproportionately valuable.
Connaught Place consulting firms staff multi-client cloud assessments, which is the fastest route to breadth of experience in North India.
AWS hiring sectors in Delhi
- Management and risk consulting
- Public-sector and government IT vendors
- Telecom and infrastructure operators
- Enterprise head offices
Demand drivers behind Delhi cloud hiring
- Data residency requirements named in public tenders
- Group compliance programmes of multinational parents
- DPDP Act obligations for consumer-facing businesses
- Telecom sector security obligations on cloud
Who Should Join This Course
- Business Analysts — Needing to judge which AI ideas are actually buildable
- Product Managers — Scoping AI features without over-promising to a client
- Support Engineers — Answering customer AI questions without guessing
- Fresh Graduates — Entering AI work without a research background
- Consultants — Requiring platform vocabulary for AI conversations
- Non-Technical Stakeholders — Reviewing AI spend, risk and feasibility
Prerequisites
No prior AWS experience required unless the course page states otherwise. Basic IT familiarity, comfort with a web console and command-line basics are enough to start; AWS itself, its vocabulary and its operating discipline are taught from first principles.
Learning Objectives
- Explain what each AWS AI service does and where it stops being the right answer
- Frame a business problem as an AI use case worth funding or killing
- Call Amazon Bedrock models and read latency, token and cost consequences
- Choose between a managed AI service and building a custom model
- Apply responsible-AI checks: bias, hallucination, refusal and red-teaming
- Estimate the running cost of an AI feature before it ships
- Read an AI system’s failure modes and describe them to a stakeholder
- Sit the AWS Certified AI Practitioner exam with a structured revision plan
AWS Services and Tools Covered
Amazon Bedrock, Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, Amazon Textract, Amazon Q together with responsible-AI practice, evaluation method, and the DPDP obligations that apply whenever customer data is used to ground a response.
Career Opportunities After This Programme
- AI Product Analyst
- Cloud AI Associate
- Prompt Engineer (entry)
- Solutions Consultant (AI)
- Technical Pre-Sales (AI)
- AI Support Analyst
Hands-on Practice and Capstone
Capstone: Take a written business brief, frame it as an AI use case, decide whether it is buildable at all, then deliver it as a working Amazon Bedrock feature with retrieval over an S3 document set, an installed guardrail set, an evaluation report showing measured accuracy and refusal rates, and a monthly cost model with a budget alert attached.
Production scenario: A product team wants to ship an AI feature that summarizes customer tickets, and the estimate on the table ignores token cost entirely. Frame the use case properly, prototype it on Bedrock, measure it against a written evaluation set, price it honestly per ticket, and either green-light it with guardrails or show the team why it should be cut.
Course Duration, Mode and Certification
The course runs for 12 weeks (72 hours), three sessions a week of two hours each. Modes: Classroom / Live Online / Hybrid. Certifications mapped: SkilBrill Course Completion Certificate, AWS Certified AI Practitioner (mapped, exam separate).
Is classroom training available in Delhi? Classroom batches run at our Thoraipakkam centre in Chennai. Learners in Delhi join the same live online or hybrid cohort taught by the same faculty, with the same assessment and placement support.
Why Train at SkilBrill
SkilBrill Training Institute in Thoraipakkam focuses on making you employable. Trainers are working cloud engineers and architects, every module produces a portfolio artefact rather than a completion badge, and the placement cell connects eligible candidates with hiring companies. Fee: ₹35,000 – ₹60,000. Enquire on +91 8610964691.
Career and Job Support
- Resume assistance tailored to cloud and platform roles
- LinkedIn profile rewrite and recruiter outreach
- Mock interviews covering architecture, operations and troubleshooting scenarios
- Capstone project walkthrough preparation
- Placement assistance with 200+ hiring partners
- Typical salary band after completion: ₹4.5 – 10 LPA
Frequently Asked Questions
What is AWS AI Fundamentals training at SkilBrill?
It is the programme for understanding and using the AWS AI services responsibly: what Bedrock, SageMaker, Rekognition and Comprehend each do, how to frame a use case, how to evaluate a result and how to price it. SkilBrill runs it over 12 weeks and 72 hours.
How is this different from the AWS Machine Learning course?
This course is about using managed AI services and judging whether an AI feature is worth building — it requires no model training. AWS Machine Learning is the engineering track: feature engineering, training, deployment and MLOps with SageMaker. Take this one first if you are not writing models.
What does the syllabus cover?
Eight modules: what AI and ML are, the AWS AI service catalogue, working with Amazon Bedrock, data for AI, evaluation and guardrails, cost and capacity, use-case selection, and an end-to-end AI feature capstone.
What is the duration and fee?
12 weeks, three sessions a week of two hours each, totalling 72 classroom hours. Fees are ₹35,000 – ₹60,000 depending on batch and mode, placing it in the entry band alongside the architecture and operations tracks.
Do I need to know Python to take this course?
No. The course is written for people who must scope, evaluate and operate AI features rather than train models. Where a short script makes a point clearer it is taught from scratch, and the console path is shown for everything.
What jobs can I get after AWS AI Fundamentals?
AI product analyst, cloud AI associate, entry prompt engineer, technical pre-sales for AI products and AI support analyst. In India these typically start at ₹4.5–10 LPA, with pre-sales and product-facing roles paying above the band once you can talk about cost and risk.
Is classroom training available in Delhi?
No. SkilBrill's classroom runs only in Chennai. Learners in Delhi join the same live online / hybrid cohort taught by the Chennai faculty, with proctored labs and the same placement support.
Which companies hire for these skills in Delhi?
Learners in Delhi typically target a mix of MNCs and startups such as HCL, TCS, IBM. The programme's placement support applies equally to Delhi learners.