About Course
About AWS Machine Learning
Pune's Hinjawadi and Kharadi parks house IT services alongside a large automotive and engineering supplier base, and both are mid-migration. Suppliers moving up the value chain find their buyers asking for information-security governance on cloud, while the shared-services centres in Kharadi consolidate reporting into Redshift.
The automotive angle matters more here than anywhere else in the programme. Plant systems, supplier portals and quality data sit on-premise because of latency and safety, so hybrid connectivity through Direct Connect and Site-to-Site VPN is a daily design problem rather than an edge case.
This Pune page carries the same 12-week, 72-hour AWS Machine Learning 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 Machine Learning training (72 hours) covering SageMaker, feature engineering, training, deployment, drift monitoring and MLOps pipelines. Live online and classroom.
Why Learn AWS Machine Learning
Automotive suppliers connecting plant systems through Direct Connect need people who can design the hybrid network, and there is very little local supply of that skill.
Kharadi shared-services centres are consolidating reporting into Redshift, which puts warehouse modelling alongside general cloud knowledge in the same role.
US-listed parents run SOX testing from Pune, creating recurring seasonal demand for people who can evidence access controls on cloud.
AWS hiring sectors in Pune
- IT services and shared-services centres
- Automotive and component suppliers
- Manufacturing and industrial engineering
- Pharma and contract research
Demand drivers behind Pune cloud hiring
- Hybrid connectivity between plant systems and cloud platforms
- Redshift consolidation of shared-services reporting
- Buyer security questionnaires requiring cloud governance evidence
- SOX testing for US-listed parent companies
Who Should Join This Course
- Data Analysts — Moving from dashboards into predictive modelling
- Software Engineers — Owning models in production rather than notebooks
- Data Scientists — Needing deployment, monitoring and MLOps discipline
- Fresh Graduates — Targeting ML engineering rather than research roles
- Backend Developers — Taking on model-serving responsibility
- Tech Leads — Deciding what ML work is safe to put into production
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
- Frame a business problem as a tractable ML problem with a measurable target
- Prepare and validate training data with leakage and imbalance checks
- Train and tune models in SageMaker and compare against a baseline
- Deploy to a monitored endpoint with traffic splitting and rollback
- Detect data and concept drift and trigger retraining on evidence
- Operate MLOps pipelines with a model registry and lineage
- Apply security, governance and risk checks to a deployed model
- Explain a model’s behaviour and its limits to a non-engineer
AWS Services and Tools Covered
Amazon SageMaker, SageMaker Pipelines, Amazon Redshift ML, AWS Glue, Amazon S3, MLflow on AWS together with MLOps practice, model risk and evaluation method, and the ISO 27001 and DPDP obligations that apply to training data and model documentation.
Career Opportunities After This Programme
- Machine Learning Engineer
- MLOps Engineer
- ML Engineer (SageMaker)
- Data Scientist (AWS)
- Applied Scientist (Engineering)
- ML Platform Engineer
Hands-on Practice and Capstone
Capstone: Frame a business problem as an ML task, prepare and validate the training data with leakage and imbalance checks, train a SageMaker model that beats the stated baseline, deploy it to a monitored endpoint with traffic splitting and a rehearsed rollback, build the retraining pipeline with lineage recorded in the model registry, then present the model alongside its limits and its risk position.
Production scenario: A deployed model is degrading silently: accuracy has fallen while latency and cost hold steady. Detect it from the monitoring signal, establish whether the cause is data drift or concept drift, retrain on the refreshed distribution, and roll the new version out safely without taking the endpoint down.
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.
Is classroom training available in Pune? Classroom batches run at our Thoraipakkam centre in Chennai. Learners in Pune 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: ₹55,000 – ₹85,000. 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: ₹7 – 22 LPA
Frequently Asked Questions
What is AWS Machine Learning training at SkilBrill?
It is the engineering programme for taking a model from raw data through training, deployment and monitoring on AWS: SageMaker, feature engineering, drift detection and MLOps pipelines. SkilBrill runs it over 12 weeks and 72 hours and finishes with a system you can operate, not a notebook.
How is this different from AWS AI Fundamentals?
AI Fundamentals teaches you to use managed AI services and judge whether a use case is worth building, with no model training. This is the engineering track: you train models, deploy them, monitor drift and operate retraining pipelines. AI Fundamentals is the sensible first course if you are not writing models.
How is this different from AWS Generative AI Development?
This course covers predictive machine learning — classification, regression, forecasting and the MLOps discipline around it. Generative AI Development covers building applications on foundation models: retrieval, agents, evaluation of open-ended output and token economics. Different problems, different evaluation methods.
What does the syllabus cover?
Eight modules: problem framing, data preparation and feature engineering, SageMaker training, deployment and inference, monitoring and drift, MLOps pipelines and model registry, security and model risk, and an end-to-end capstone system.
What is the duration and fee?
12 weeks, three sessions a week of two hours each, totalling 72 classroom hours. Fees are ₹55,000 – ₹85,000 depending on batch and mode, placing it in the premium band alongside advanced architecture and generative AI.
What jobs can I get after AWS Machine Learning?
Machine learning engineer, MLOps engineer, ML platform engineer and applied scientist in engineering teams. In India these typically start at ₹7–22 LPA, with production MLOps and platform roles at the higher end.
Is classroom training available in Pune?
No. SkilBrill's classroom runs only in Chennai. Learners in Pune 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 Pune?
Learners in Pune typically target a mix of MNCs and startups such as Infosys, Wipro, TCS, Capgemini. The programme's placement support applies equally to Pune learners.