A 3-month MLOps course covering CI/CD for machine learning, Docker, Kubernetes, MLflow and model monitoring — taught live, with real-time projects and placement support.
Our MLOps training in Hyderabad is designed for machine learning engineers, data scientists, DevOps engineers and Python developers who can build a model but have never deployed one. You will work through the full lifecycle — data versioning, experiment tracking, packaging, deployment, monitoring and retraining — on the same tools used in production teams, with a trainer who has done this work in industry.
The PDF contains a proposed syllabus update. Confirm the final batch curriculum and course arrangements with the team.
| Course detail | Information |
|---|---|
| Course | MLOps |
| Duration | 3 months (weekday batches) |
| Learning modes | Classroom and live online |
| Curriculum | 8 modules with hands-on labs |
| Projects | 4 graded builds and a capstone |
| Batch schedule | Weekday and weekend batches |
| Trainer | Shaheda Tabassum |
Every module ends with something deployed, versioned and monitored — the artefacts you will show in interviews.
Docker, Kubernetes, MLflow, DVC, Airflow, GitHub Actions and cloud deployment on AWS.
Sessions are led by practitioners who have built and maintained ML systems in production.
Guidance on preparing for vendor exams alongside your Brolly AI course certificate.
Resume rewriting, LinkedIn positioning, referrals and interview scheduling.
Technical rounds on pipelines, containers and monitoring, scored the way hiring panels score them.
Supervised work on a live pipeline so your resume shows applied experience, not just coursework.
Doubt-clearing sessions scheduled outside class hours.
Rewatch any session, and get updates when the course content changes.
Built for working professionals — ask about switching batches if your shift changes.
See how you will learn.
Meet your trainer and find a batch that fits your schedule.
Eight modules covering the model lifecycle from Python and Git to cloud deployment. Working Python knowledge and comfort with the command line help you get the most from the labs; no prior DevOps experience is required.
Request the current classroom or online fee, what it includes, payment options and the applicable refund terms before enrolling.
Discuss current feesSee how you will learn.
Meet your trainer and find a batch that fits your schedule.
Placement assistance includes resume review, LinkedIn positioning, technical mock interviews with written feedback, referrals and interview scheduling. Support continues after the course ends; it is not a job guarantee.
Package models and services into reproducible images.
Deploy, scale and roll back inference services.
Track experiments and manage the model registry.
Version datasets and pipeline stages alongside code.
Schedule and orchestrate training pipelines.
Build CI/CD workflows for model releases.
Monitor latency, throughput and model health.
Expose models as production HTTP endpoints.
Run managed training jobs and hosted endpoints.
Provision infrastructure as versioned code.
See how you will learn.
Meet your trainer and find a batch that fits your schedule.
Four graded builds plus a capstone. Each ends with a running service, a dashboard and a README you can link from your resume.
Train a model, register it in MLflow, serve it through FastAPI on Kubernetes, and trigger retraining when drift crosses a threshold.
Build an Airflow DAG that ingests sales data, validates schema, retrains weekly and publishes forecasts to a warehouse table.
Containerise a vision model, deploy two versions behind an ingress, split traffic, compare live metrics and roll back safely.
Serve a language model, add token-level cost tracking, set latency alerts and evaluate output quality on a fixed test set.
Provision infrastructure with Terraform, run a SageMaker training pipeline, deploy through GitHub Actions and hand over full documentation.
Complete the graded projects and capstone review to receive a Brolly AI course completion certificate, a project portfolio certificate and a verification URL you can add to LinkedIn. Ask the team about current vendor-exam preparation. Third-party exams have their own fees, schedules and issuing organisations.
See how you will learn.
Meet your trainer and find a batch that fits your schedule.
Generative AI Specialist & LLM Architect
9+ years in Artificial Intelligence, Machine Learning and Generative AI
Core expertise: Large Language Models, Prompt Engineering, Retrieval-Augmented Generation, AI agents, NLP systems, Deep Learning, MLOps and cloud deployment on AWS.
Industry projects: LLM-based AI assistants, RAG-powered knowledge platforms, intelligent chatbots, fraud detection systems and predictive AI solutions for healthcare and manufacturing.
Teaching style: explains transformers, embeddings and AI agents in simple language, with hands-on coding and job-focused training.
Book a free demoPick the schedule you can keep. Discuss the syllabus and trainer arrangements for your preferred format.
Attend the MLOps course in Hyderabad at our Kukatpally centre, with lab machines, in-person doubt clearing and group project work.
Live interactive classes, not recordings. Join from anywhere in India with the same assignments and reviews.
Saturday and Sunday sessions for working professionals, with weekday doubt-clearing slots.
Customised programs for engineering teams, delivered on-site or remotely against your existing stack.
Full recorded curriculum with lab guides, for learners who prefer to work through material alone.
Compressed daily schedule for learners on a notice period or between jobs.
See how you will learn.
Meet your trainer and find a batch that fits your schedule.
| Batch | Days | Learning mode | Next session |
|---|---|---|---|
| Weekday batch | Weekdays | Classroom or live online | Confirm the next start date and class time |
| Weekend batch | Weekends | Classroom or live online | Confirm the next start date and class time |
Choose a weekday or weekend batch. Contact the team for the next start date, exact timings and available seats before enrolling.
Own the pipelines that take a model from notebook to production, and keep it healthy after release.
Build and optimise models, then package and serve them with the deployment skills this course covers.
Bring existing CI/CD and Kubernetes experience to machine learning workloads.
Monitor deployed models, manage drift and retraining, and enforce governance.
Build the internal tooling other data scientists use to train and ship models.
Design the ingestion and feature pipelines that production models depend on.
See how you will learn.
Meet your trainer and find a batch that fits your schedule.
Read learner feedback across Brolly AI programmes and ask the team about experiences from your chosen course.
Read Brolly AI reviews on GoogleMLOps training teaches you to take machine learning models from a notebook into reliable production systems. It covers data and model versioning, containerisation, CI/CD pipelines, deployment on cloud or Kubernetes, and monitoring for drift and performance after release.
A complete MLOps course covers eight areas: Python and Git foundations, data pipelines, experiment tracking with MLflow, Docker containerisation, Kubernetes serving, CI/CD automation, production monitoring, and cloud deployment. Our syllabus ends with a capstone where you deploy and monitor a full pipeline yourself.
Machine learning engineers, data scientists, DevOps and cloud engineers, Python developers, data engineers and final-year students with programming basics. If you can write Python and understand what a trained model is, you can follow the course from module one.
Working Python knowledge and comfort with the command line. Basic machine learning concepts help but are covered in module one. No prior Docker, Kubernetes or cloud experience is required — those start from fundamentals.
Three months for weekday batches, around four months for weekend batches and six weeks for fast-track. Ask the team for total instruction and project hours for your chosen batch. Recordings remain available for revision.
Request the current classroom or online fee, what it includes, payment options and the applicable refund terms before enrolling.
Complete the graded projects and capstone review to receive a Brolly AI course completion certificate, a project portfolio certificate and a verification URL you can add to LinkedIn. Ask the team about current vendor-exam preparation. Third-party exams have their own fees, schedules and issuing organisations.
A certificate documents your training. Deployed projects give you practical work to discuss in an interview; hiring decisions also depend on your background, skills and the role.
Yes. Online batches are live and interactive, not pre-recorded, with the same trainer, assignments and reviews as classroom batches. You get a remote lab environment for Docker and Kubernetes exercises, and lifetime access to session recordings.
Yes. Placement assistance covers resume rewriting, LinkedIn positioning, technical mock interviews with written feedback, referrals and interview scheduling, and it continues after the course ends. It is structured support, not a guaranteed job offer.
Docker, Kubernetes, MLflow, DVC, Apache Airflow, GitHub Actions, Prometheus, Grafana, FastAPI, Terraform and AWS SageMaker. Each tool appears in at least one graded lab, so you use it rather than just hear about it.
MLOps engineer, ML engineer, ML platform engineer, DevOps engineer on ML workloads, machine learning operations specialist and data engineer roles focused on ML pipelines. Hyderabad's product companies and global capability centres hire actively for all of these.
Basic familiarity helps but is not mandatory. Module one covers the ML lifecycle from scratch. What matters more is Python fluency, because every lab involves writing and packaging code rather than tuning model accuracy.
Brolly AI is at 65, National Highway, Jai Bharat Nagar, Hyder Nagar, Vasantha Nagar, Kukatpally, Hyderabad, Telangana 500085 — close to KPHB and easily reached from Miyapur, Madhapur and Gachibowli. Call +91 90521 44555 to schedule a centre visit.
No. The course introduces containers and delivery progressively. Kubernetes and cloud deployment are extensions after the local core. Prior certification is not a recommended prerequisite; the final admission policy remains for Brolly AI to confirm.
They overlap but serve different tasks. The core MLOps path operates predictive models. The LLMOps extension handles prompts, retrieval and model APIs. AIOps applies AI to IT operations and is not presented as an interchangeable name for this syllabus.
No. Drift is a signal to investigate. Check input quality, sample size, changes in the business process and actual model performance when labels are available. Retraining produces a candidate that still needs validation.
The proposal uses a small predictive model with consistent preprocessing, a batch or API interface and a container package. Hosted deployment depends on the final environment and cost agreement; a local demonstration does not establish production capacity.
Show reproducibility, data and model gates, serving contracts, release evidence, monitoring and a tested rollback. Make it possible to identify which code, data and model produced an observed prediction.
Yes. Select Download syllabus (PDF) at the top of this page or after the curriculum. The ungated PDF includes all proposed modules, readiness guidance, four project briefs and assessment criteria. Business details marked in the PDF still need confirmation.
Use the bottom Enquire button, call or message the team on WhatsApp. Share your learning goal and confirm the schedule, location or online access, fees and tool requirements before enrolling.
See how you will learn.
Meet your trainer and find a batch that fits your schedule.
Visit the Kukatpally training centre or discuss live online options. Call ahead to arrange a centre visit and confirm current timings.
Brolly AI, 65, National Highway, Jai Bharat Nagar, Hyder Nagar, Vasantha Nagar, Kukatpally, Hyderabad, Telangana 500085See how you will learn.
Meet your trainer and find a batch that fits your schedule.
Tell us what users need to do, what is slowing the business down and what a successful outcome should look like. We will help you identify the appropriate AI, software, product or training pathway.
