About Course
About MLOps Training
Pune's advantage is that it combines three economies most cities have only one of: automotive and manufacturing engineering around Chakan and Hinjawadi, a large IT and BFSI services base, and a research and education corridor. That combination is why AI work here tends to be applied and physical — line quality, predictive maintenance, connected-vehicle data, supply-chain forecasting — rather than purely conversational. The engineering culture in the city expects evidence that a model works under plant conditions, not only on a benchmark.
The services base adds a second route: firms here run AI and data programmes on behalf of global clients, so engineers need to work inside someone else's governance, security and audit requirements from day one. Graduates most often land in analytics, data platform or ML engineering roles inside those two ecosystems, because both are short of people who can operate a pipeline as well as build one.
This Pune page carries the same 12-week, 72-hour MLOps Training 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 MLOps training (72 hours) covering the ML lifecycle, reproducible pipelines, experiment tracking, model registries, CI/CD for ML, serving, drift monitoring and retraining. Classroom, Live Online and Hybrid.
Why Learn MLOps Training
Manufacturing around Chakan and Hinjawadi needs engineers who can put a model on the line and defend its readings, which rewards domain knowledge over model fashion.
IT and product engineering firms in the city deliver AI work under client governance, so experience with evaluation, lineage and audit travels well between accounts.
Insurance and BFSI operations in the region are consolidating analytics, creating roles that combine actuarial thinking with working knowledge of modern pipelines.
AI and data hiring sectors in Pune
- Automotive, engineering and manufacturing
- IT services and product engineering
- BFSI operations and insurance shared services
- Pharma, education and research institutions
Demand drivers behind Pune AI and data hiring
- Predictive maintenance and quality analytics demanded by plant operations
- Connected-vehicle and telematics data needing engineering rather than reporting
- Client governance and audit requirements applied to AI delivery in services firms
- Supply-chain volatility forcing forecast-driven planning in manufacturing
Who Should Join This Course
- Data Scientists — Owning the model all the way to production instead of handing it over
- Data Engineers — Building the pipelines that feed and refresh a model
- DevOps Engineers — Moving into machine learning delivery
- Platform Teams — Standing up an ML platform others build on
- Fresh Graduates — Targeting machine learning engineering without a research background
- QA and Release Staff — Testing a model, which behaves unlike software
Prerequisites
You should be comfortable in Python, able to read a training script and understand what a metric represents. Command line and Git familiarity is needed, and basic SQL helps. Prior exposure to a machine learning project is useful but the modelling itself is not taught here, so this suits engineers moving toward ML delivery as well as data scientists tired of handoffs.
Learning Objectives
- Package a training run so it is reproducible from its own record
- Build a pipeline that retrains on a schedule or on a trigger
- Track experiments and register a model with its lineage intact
- Add CI/CD for ML including data and model validation gates
- Serve a model behind an endpoint with versioning and a rollback
- Monitor for data drift, concept drift and performance decay
- Design a retraining decision that is based on evidence
- Document a model with the lineage a governance review needs
What the Syllabus Covers
Each module is assessed against published MLOps Engineer outcomes rather than on memorisation, covering Feature Store, Experiment Tracking, Model Registry, CI/CD for ML, Drift Monitoring, Model Serving together with orchestrated training pipelines and data validation, experiment tracking with a feature store, a model registry gated by CI, and drift monitoring wired to retraining on evidence rather than on a schedule.
Career Opportunities After This Programme
- MLOps Engineer
- Machine Learning Engineer
- Platform Engineer (ML)
- ML Platform Analyst
- Data Engineer (ML)
- Model Governance Analyst
Hands-on Practice and Capstone
Capstone: Package a training run so it reproduces from its own record, build an orchestrated pipeline with data validation and retries, track experiments and register the model with full lineage, add CI with a metric gate that can block a bad release, serve it behind a versioned endpoint with a working rollback, then wire drift monitoring and a documented retraining trigger.
Production scenario: A fraud model quietly stopped working six weeks after release because the upstream query changed and nobody was watching. The platform built here makes that visible: ingestion validates schema and distribution before training runs, the endpoint reports input drift against the training snapshot, and the retraining trigger fires on evidence with an approval step rather than on a calendar nobody reviews.
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. Certificate issued: SkilBrill Course Completion Certificate. Assessment is against published MLOps Engineer outcomes, and you finish with evidence you can show rather than a pass mark.
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 AI, data and platform engineers, every module produces portfolio evidence rather than a completion badge, and the placement cell connects eligible candidates with hiring companies. Fee: ₹45,000 – ₹75,000. Enquire on +91 8610964691.
Career and Job Support
- Resume assistance tailored to AI, data and platform engineering roles
- LinkedIn profile rewrite and recruiter outreach
- Mock interviews covering evaluation, pipeline design and system-review scenarios
- Capstone walkthrough and portfolio review preparation
- Placement assistance with 200+ hiring partners
- Typical salary band after completion: ₹7 – 18 LPA
Frequently Asked Questions
What does MLOps training at SkilBrill cover?
Twelve weeks and 72 hours across eight modules: the ML lifecycle and what breaks, pipelines and orchestration, experiment tracking and feature stores, the model registry and delivery, serving, monitoring and drift, applied practice, and a capstone that ships a monitored model.
Do I need to be a data scientist first?
No, but you should be able to read a training script and understand what a metric means. The course does not teach model building from scratch; it teaches getting a model into production and keeping it honest. Data scientists, data engineers and DevOps engineers all take it from different directions.
What programming and tooling is required?
Comfortable Python plus command line and Git. You will write pipeline steps, tests and serving code. Some exposure to containers helps in the serving module, and basic SQL is useful for the data validation material.
How is MLOps different from DevOps?
DevOps ships code, which either works or does not. MLOps ships a model whose quality depends on data you do not control, so the pipeline has to validate inputs, track lineage, monitor drift and decide when retraining is justified. Most of the course is that difference.
Is certification included?
The syllabus follows MLOps engineer objectives so it works as preparation. Vendor credentials are booked through their own programmes, and the SkilBrill completion certificate is issued by us on the capstone.
Which tools are used in the course?
The course is tool-agnostic in principle and concrete in practice: a mainstream workflow orchestrator, an experiment tracker, a model registry, a feature store and a serving layer, with at least two options compared in the modules where the choice is contentious.
What do I leave with?
A reproducible training run, an orchestrated pipeline with validation gates, a registered model with lineage, CI that can block a release, a versioned endpoint with rollback, and drift monitoring with a documented retraining trigger.
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.
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