LIVE ONLINE • INSTRUCTOR-LED • 5-MONTH MLOPS PROGRAMME
MLOps Course in Pune
Build reproducible training pipelines, release models and monitor them after deployment. Learn through five connected builds, an AWS SageMaker capstone and mentor feedback on your implementation.
For data scientists, ML engineers, and software, data or DevOps professionals developing production ML skills.
FROM NOTEBOOK → PRODUCTION ML SYSTEM
5 months
Programme duration
Live online
Instructor-led
17 modules
Across five phases
5 builds + capstone
Connected practical work
₹60,000
Programme fee
What you build with SCAI
- Connected implementation. Five builds covering data validation, reproducibility, training, release and monitoring, followed by the AWS capstone.
- Distributed CPU/GPU training. Ray Train with checkpointing and recovery, plus MLflow experiment comparisons — you practise how training behaves across workers.
- Controlled model releases. Evaluation criteria, model registry, serving, CI/CD and rollback decisions, so you can decide whether a model is ready to release and recover from a failed deployment.
- Practical AWS deployment. SageMaker Training Jobs and Serverless Inference in the capstone, with IAM, logging, cleanup and cost analysis.
- Engineering feedback. Reviewed implementation work, repositories, evaluation reports and operational notes you can explain in project presentations and interviews.
Live Online Program
Live MLOps Training in Pune for ML and Platform Engineers
This is a live online, instructor-led programme. You join from Pune (or anywhere) and participate in real-time sessions, mentor reviews, and project builds. The programme runs for 5 months with weekday and weekend batch options.
Live Online MLOps Training for Pune Learners
The programme is fully live online. You join real-time sessions from Pune or anywhere in India. There is no in-person requirement — all sessions, reviews, and project work happen remotely with live mentor support.
Where MLOps Skills Fit in Pune Engineering Work
- · Teams working with risk or compliance models need traceable data and controlled releases
- · Engineers supporting prediction APIs need reliable serving and monitoring
- · DevOps practitioners moving into ML need to understand data/model versions and evaluation gates
- · Data and analytics teams moving from pilot ML to deployment and monitoring
Who Should Join
Who Should Take This MLOps Course in Pune?
This programme is open to beginners and professionals transitioning from other technical backgrounds — no prior programming or MLOps experience is required to join. It teaches the foundations first, then the full ML delivery lifecycle; whichever starting point you bring, the builds cover the same production engineering layer.
Data Scientists
You have notebook and modelling experience. You need reproducibility, release discipline, monitoring, and controlled retraining to move into production roles. Best fit if you can train models but cannot reliably deploy, monitor, or retrain them..
ML Engineers
You handle model engineering. You need lifecycle automation, distributed training, experiment tracking, and release control for production systems. Best fit if you build models but lack the infrastructure and release discipline for production..
Data Engineers
You build pipelines and data systems. You need training lifecycle, experiment tracking, and model delivery workflows to bridge into MLOps. Best fit if you understand pipelines but need ML-specific lifecycle and serving skills..
DevOps / Platform Engineers
You own CI/CD and infrastructure. You need ML-specific pipelines, model registry, serving patterns, and drift handling. Best fit if you know DevOps but need ML-specific deployment, registry, and monitoring..
AI Engineers
You develop AI systems. You need reliable model lifecycle and production ML operations to deploy and maintain AI systems at scale. Best fit if you build AI applications but need production lifecycle and operational discipline..
Recommended foundation
No prior programming or MLOps experience is required to join. Python, Git, Docker and Kubernetes foundations are taught within the programme — learners start with the foundations and progress through the full lifecycle.
You may need a foundation course first if
you are looking for a data science fundamentals course rather than production ML engineering.
This is not a data science theory course or a generic DevOps bootcamp. It is a production ML engineering programme that teaches the foundations before the lifecycle work.
MLOps Lifecycle
What You Actually Learn Across the MLOps Lifecycle
The MLOps lifecycle is not a tools list. It is a controlled production workflow where each stage has a responsibility, a failure mode, and an engineering decision that allows the system to move forward.
Data Orchestration and Validation
RESPONSIBILITY
Validate and orchestrate event-driven data inputs. Ensure upstream changes do not silently corrupt training or serving.
FAILURE MODE
Schema drift from upstream sources, missing fields, or silent data quality degradation.
ENGINEERING DECISION
Validation result and quarantine recovery path. Move forward only when data is validated and versioned.
TOOLS
Curriculum
MLOps Course Curriculum and 5-Month Learning Path for Pune
The curriculum follows the ML delivery lifecycle. Each month progressively adds capabilities, building from data foundations to a full production MLOps system.
DATA → VALIDATE → VERSION
Focus: MLOps foundations, Python essentials, ML foundations, Git, data pipelines, versioning (DVC)
Data Foundations and Reproducibility
Focus: MLOps foundations, Python essentials, ML foundations, Git, data pipelines, versioning (DVC)
Training and Experiment Tracking
Focus: Distributed training (Ray Train), experiment tracking (MLflow), hyperparameter tuning (Optuna)
Packaging, Registry and Release
Focus: Docker for ML, MLflow Model Registry, model serving (FastAPI), CI/CD for ML
Monitoring, Drift and Retraining
Focus: Prometheus/Grafana monitoring, drift detection (Evidently), controlled retraining pipeline
Managed Cloud Deployment and Capstone
Focus: AWS SageMaker deployment, capstone project, portfolio review, resume & mock interviews
Projects
MLOps Projects You Will Build
You build connected production systems across the ML delivery lifecycle — not isolated tool demos. Each project connects to the next, culminating in an AWS SageMaker capstone.
Event-Driven Data Orchestration and Validation
ML pipelines break when upstream data changes.
Main submission: Orchestrated pipeline definition, validation suite, quarantine logic, and a data-quality runbook.
Tools, workflow and review criteria
Review: Pipeline correctness, validation coverage, orchestration reliability, and quarantine explanation.
Reproducible Data and Artifact Versioning
Training results cannot be reproduced when data, code, and environment are not versioned together.
Main submission: Versioned repository, DVC remote configuration, reproduction report, and lineage documentation.
Tools, workflow and review criteria
Review: Reproducibility, repository structure, version management, and lineage explanation.
Distributed CPU/GPU Training and Experiment Tracking
Training on a single machine is slow and does not reflect production scale.
Main submission: Distributed training script, checkpoint strategy, MLflow experiment comparison, and a scaling note.
Tools, workflow and review criteria
Review: Training correctness, distribution strategy, checkpoint reliability, and experiment comparison logic.
Governed Model Registry, Release and Serving
Models are promoted without documented evaluation criteria.
Main submission: Evaluation report, registered model, Docker image, CI/CD workflow, and a release/rollback note.
Tools, workflow and review criteria
Review: Evaluation logic, promotion criteria, CI/CD correctness, serving health checks, and rollback explanation.
Drift Detection and Controlled Retraining
Production models degrade silently.
Main submission: Drift report, alert rules, retraining pipeline, validation gate, and a response runbook.
Tools, workflow and review criteria
Review: Drift analysis accuracy, threshold rationale, retraining logic, and response decision documentation.
AWS SageMaker Deployment Capstone
Self-managed MLOps workflows are complex to operate at scale.
Main submission: SageMaker implementation with Training Jobs and Serverless Inference, IAM configuration, cost analysis, and a comparison note.
Tools, workflow and review criteria
Review: Component mapping correctness, IAM and logging, cost awareness, and comparison between self-managed and managed approaches.
Scope Comparison
MLOps vs ML Engineering, LLMOps and AIOps
MLOps, ML Engineering, LLMOps, and AIOps are related but distinct. Understanding the scope of each helps you choose the right path.
Data pipelines, reproducible training, model release, serving, monitoring and controlled retraining.
How the Program Is Taught
How live teaching and engineering feedback work
Live sessions introduce the concepts and implementation approach for each stage. You then apply them in the connected builds, submit the work for review, address feedback and explain your engineering decisions.
Reviews focus on engineering decisions
Review criteria are specific: whether a training run can be reproduced, whether validation coverage is adequate, how checkpoint recovery works, why a release should proceed, and how a rollback would be executed.
Build a portfolio you can explain
Keep code, configurations, experiment results and operational notes together so you can walk through the system, explain a failure and defend the choices made during implementation.
Certification & Career
MLOps Certification and Career Support
SCAI MLOps Course Completion Certificate
Issued after you complete the required assignments and the capstone project. The certificate records completion of SCAI's programme; the SageMaker capstone provides practical AWS experience but is not an AWS certification.
Portfolio evidence
Versioned repositories, experiment records, release workflows, monitoring logic and operational notes give you concrete work to explain in project presentations and interviews.
Career preparation
Résumé and project-presentation review, GitHub presentation, mock interviews, interview preparation and job-search guidance. Referrals may be available where possible; placement is not guaranteed.
Course Fee and Cohort
MLOps Course Fee, Format and Next Cohort in Pune
The published programme fee is ₹60,000 for the full five-month live online programme. Included: live instructor-led teaching, five connected builds and the AWS SageMaker capstone, mentor support and engineering reviews, career preparation, and the SCAI MLOps Course Completion Certificate.
FEE
₹60,000
DURATION
5 months
FORMAT
Live online
LEVEL
MLOps Specialization
COHORT
Admissions Open
MENTORSHIP
Included
What the fee covers
- · 5 months live instructor-led programme
- · Hands-on assignments and 6 production projects
- · Mentor support and engineering reviews
- · Career preparation: resume, GitHub, mock interviews
- · MLOps Course Completion Certificate
Practical enrolment summary
Starting knowledge: No prior programming or MLOps experience is required to join — Python, Git and Docker are taught within the programme, starting from the foundations.
Duration and batches: Five months, with weekday and weekend batch options. The course team confirms available start dates and session timings.
How mentor questions are handled: Mentor support and engineering reviews are included throughout the programme; ask during sessions and project reviews.
Fee inclusions: Live teaching, the five builds and capstone, mentor reviews, career preparation and the SCAI completion certificate.
FAQ
MLOps Course in Pune — FAQs
Is this MLOps course in Pune online or classroom?
Do I need experience before joining?
How long is the MLOps course, and what batches are available?
What will I build during the programme?
Is this an AWS certification?
How do mentor reviews and project feedback work?
What is included in the ₹60,000 fee?
Can I take the MLOps course alongside a full-time job?
Does the course include placement support?
Who can take this course from Pune?
Next Step
Discuss your MLOps learning goals
Share your current experience, the systems you want to learn to build and your preferred batch. The course team can help you assess how the programme's foundation-first structure fits your background, project expectations and schedule before you enrol.
Discuss Course Fit