Gen AI Specialization
End-to-end GenAI engineering: Transformers → agents, multimodal RAG, diffusion, ViT, VLMs, eval & deployment.
MLOps Certification Course with Placement Support
Build & Deploy Production ML Pipelines • Instructor-Led • Live Projects
Live Instructor-LedPlacement SupportWeekdays / Weekend Batches
A production-focused MLOps program taught live by instructors. Build the skills for roles like MLOps Engineer, Platform Engineer, and ML Infrastructure Lead with hands-on projects and interview preparation.
Master reproducible pipelines with DVC, MLflow, Docker, and Kubernetes. Learn CI/CD for ML, model serving with TorchServe & Triton, monitoring, drift awareness, and incident response—aligned with real-world engineering roles at scale.
With dedicated placement support, capstone projects, and mock interviews, you'll go from fundamentals to production-ready execution.
- DVC → MLflow → Docker → K8s → CI/CD
- Model serving, monitoring & drift detection
- AWS SageMaker • Azure ML • Vertex AI
- 5-Month cohorts • Mentorship • Mock interviews
Book a Session
Inquire about our MLOps Course
Program Overview of MLOps Course
A production-focused program that teaches you how modern teams actually ship ML.
This MLOps course is built for people who want to run ML systems in production — not just build models. Over 5 months, you'll learn how modern teams ship ML: reliable pipelines, reproducibility, release discipline, monitoring, and real incident mindset.
It's a live MLOps course online (100% instructor-led). That means you learn by building: assignments, reviews, and guided implementation — so your output looks like real work, not a tutorial repo.
100% live, instructor-led training (online)
5-month deep program with structured assignments
Skill alignment: practice, review, improvement loop
Portfolio-grade projects + capstone (production style)
Resume + GitHub review + hiring pattern preparation
Optional cloud track after core foundations
Why Choose Our MLOps Course?
A truly practical MLOps certification course — this is a live, instructor-led MLOps course online designed to take you from “model training” to “production operations”.
End-to-End ML Lifecycle
You learn MLOps the way real teams ship ML: data → training → packaging → deployment → monitoring → iteration — not just notebooks.
100% Instructor-Led (Live)
Live teaching + live debugging. You don’t “watch content” — you build with mentors, get unblocked fast, and learn production habits.
5-Month Deep Program
This is not a crash course. We go deep enough for interviews + real work: pipelines, release discipline, monitoring, and reliability.
Assignments → Skill Alignment
Every module has graded assignments and guided practice so your skills match how companies evaluate MLOps engineers.
Release Discipline for ML
You learn how to ship ML like software: versioning, gates, CI/CD thinking, rollback mindset, and clean repo patterns.
Monitoring & Incident Mindset
We train you to operate systems: alerts, health checks, debugging, incident handling, and how teams keep production stable.
Drift & Data Quality Response
You’ll learn what to do when data changes: validation, drift detection, triage, and safe re-training / rollback workflows.
Cloud Track (Optional)
After you learn the core patterns, we map them to cloud services (AWS/Azure/Vertex) so you can work across stacks.
Portfolio + Resume + Referrals
You graduate with portfolio-grade projects, resume + GitHub polishing, hiring pattern guidance, and referral support where possible.
Who This MLOps Certification Course Is For
Built for professionals who want to run ML systems like software — with deployments, reliability, monitoring, and drift response (not just notebooks).
DevOps / Platform / SRE (5–10 yrs)
- Want to own ML deploys, reliability, and rollbacks — not just “run a notebook”
- Looking to build ML release discipline: CI/CD, environments, infra, observability
- Need an ML-ready production stack you can explain in interviews
ML Engineers (Production-track)
- You can train models — now you want reproducibility, registry, serving, drift response
- You need scalable deployment patterns with monitoring and incident mindset
- Want portfolio-grade MLOps projects, not toy demos
Software Engineers moving into AI Infra
- You build APIs/services — now want model serving + ML pipelines + monitoring
- Need real tooling: MLflow, DVC, Docker/K8s, orchestration, drift
- Want structured mentorship + assignments that align skills to hiring needs
Teams / Organizations
- Want consistent ML release process (quality gates, approvals, rollback)
- Need standardization across data, training, deployment, and monitoring
- Want hands-on training mapped to your stack (optional cloud track)
Prerequisites (kept practical)
Comfort with Python, Git, and basic ML concepts (train/validate). Docker/Kubernetes basics help, but we cover essentials before deeper orchestration.
Top Skills You'll Gain in the MLOps Certification Course
Skills interviewers actually test for MLOps & Platform roles — not just tool names.
Release Discipline for MLReproducible Training RunsModel Registry WorkflowsProduction Serving PatternsMonitoring & Incident MindsetDrift & Data Quality ResponsePipeline OrchestrationStreaming Ingestion PatternsCI/CD for Machine LearningDocker & Kubernetes for MLCloud Platforms (AWS / Azure / GCP)Portfolio-Grade Capstone ProjectResume & Interview PreparationPlacement Support & Hiring Prep
MLOps Tools & Frameworks You'll Master
Tools are taught as systems — you’ll connect them end-to-end with assignments and production-style patterns.
Data Quality & Validation
Stop bad data before it breaks training and production
- Pandera
- Great Expectations
- Evidently (data checks)
Orchestration & Pipelines
Repeatable workflows with retries, scheduling, and observability
- Dagster
- Apache Airflow
- Kubernetes Jobs/CronJobs
Streaming & Event Ingestion
Real-time ingestion patterns for modern ML systems
- Kafka
- Redpanda
- CDC + event-driven patterns
Versioning & Reproducibility
Track data + models like software; reproduce runs anytime
- DVC
- Git/GitHub
- DagsHub (optional remote)
Experiment Tracking & Registry
Track metrics/artifacts and manage model promotion
- MLflow Tracking
- MLflow Model Registry
- Promotion workflows
Containerization & Kubernetes
Package once, deploy anywhere, scale reliably
- Docker
- Kubernetes
- Helm
Model Serving & Scaling
Latency, throughput, rollout control, and multi-model serving
- FastAPI
- TorchServe
- Triton
- KServe
- Ray Serve
Monitoring, Drift & Reliability
Know when things break — and why
- Prometheus
- Grafana
- Evidently (drift)
- Alerting patterns
Cloud Track (Optional)
Same MLOps patterns mapped to managed cloud services
- AWS SageMaker
- Azure ML
- Vertex AI
- Terraform (IaC)
Production workflow • Not tutorial theory
The Complete MLOps Lifecycle
Learn the production ML workflow used by engineering teams to ship reliable, monitored models at scale.
Production
MLOps
ship • monitor • improve
Data Management
Versioning & Quality
Version datasets, validate schemas, track lineage, and monitor data quality end-to-end.
1
Model Development
Experiment Tracking
Track experiments, params, metrics and artifacts with MLflow / W&B for reproducible iteration.
2
CI/CD Pipeline
Automated Testing
Automate training, tests, validation gates and releases with disciplined ML delivery workflows.
3
Model Registry
Version & Promote
Promote models across stages with approvals, metadata and rollback-ready version control.
4
Production Serving
Deploy & Scale
Deploy containers, scale services, run canaries, and ship safe rollouts with load balancing.
5
Monitoring & Ops
Drift & Reliability
Monitor latency, errors, model metrics, drift, and trigger retraining with incident response habits.
6
1
Data Management
Versioning & Quality
Version datasets, validate schemas, track lineage, and monitor data quality end-to-end.
2
Model Development
Experiment Tracking
Track experiments, params, metrics and artifacts with MLflow / W&B for reproducible iteration.
3
CI/CD Pipeline
Automated Testing
Automate training, tests, validation gates and releases with disciplined ML delivery workflows.
4
Model Registry
Version & Promote
Promote models across stages with approvals, metadata and rollback-ready version control.
5
Production Serving
Deploy & Scale
Deploy containers, scale services, run canaries, and ship safe rollouts with load balancing.
6
Monitoring & Ops
Drift & Reliability
Monitor latency, errors, model metrics, drift, and trigger retraining with incident response habits.
Master each phase through hands-on projects, code reviews, and production-focused assignments.
MLOps Course Roadmap
A 10-step MLOps roadmap covering versioning, CI/CD, Docker, Kubernetes, serving, monitoring, and cloud track.
MLOps Foundations
Build the base for production ML: • What/why of MLOps • Lifecycle: data → train → serve → monitor • Repo hygiene & environments • Tools: Python, venv, Git
Python & Git Essentials
Automate and collaborate: • Python scripting & argparse • Modules, packaging, debugging • Git basics, branching & PRs • GitHub/GitLab setup
Data & ETL Pipelines
Reliable data for ML: • Batch vs streaming ingestion • Cleaning & validation (Great Expectations) • DVC for dataset versioning • Airflow/Luigi orchestration
Experiment Tracking & Registry
Make results reproducible: • MLflow tracking & artifacts • Model Registry: stage/promote • Compare runs & metrics • Optional: W&B/Neptune
Training & Hyperparameter Tuning
Train at scale with confidence: • CV & evaluation metrics • Optuna/Ray Tune HPO • TF/PyTorch distributed • Multi-GPU, fault tolerance
Docker for ML
Ship the same environment everywhere: • Dockerfile best practices • Build/tag/push to registry • docker-compose for stacks • Image security & slim builds
Kubernetes for ML Workloads
Scale and operate in clusters: • Deployments, Services, Ingress • HPA, jobs/cronjobs, PV/PVC • ConfigMaps/Secrets • Helm charts for releases
CI/CD for ML
Automate the pipeline: • GitHub Actions/Jenkins flows • Continuous training triggers • Canary & rollback strategies • Helm + K8s release gates
Model Serving & Monitoring
Serve and keep it healthy: • TorchServe / Triton / Ray Serve • REST/gRPC endpoints • Prometheus/Grafana dashboards • Health checks & alerts
Cloud, Drift & Capstone + Placement
Put it all together: • SageMaker, Azure ML, Vertex AI • Drift detection (Evidently) • Fairness checks (Fairlearn) • Capstone, resume & mock interviews
MLOps Curriculum
A structured, industry-aligned learning path covering data pipelines, training workflows, scalable serving, and production-grade operations — using tools like MLflow, Ray, Kubeflow, Docker, and Kubernetes.
MLOps Jobs & Salaries — India vs Global
Demand for MLOps talent is rising across fintech, healthcare, SaaS, and consumer AI. Here’s what compensation typically looks like.
India (₹)
- Typical range (25th–75th): ₹8.25 L – ₹22.0 L / year
- High (90th percentile): up to ₹31.5 L / year
- Bands vary by city (Bengaluru, Hyderabad), cloud skills, and prod experience.
Global / U.S. ($)
- Typical range (25th–75th): $132k – $199k / year
- High (90th percentile): up to ~$240k / year
- Comp varies by sector (Big Tech, hedge funds, startups) and location.
Hot Job Titles
- MLOps Engineer / ML Platform Engineer
- ML Engineer (Production)
- Data / ML Infrastructure Engineer
- Model Reliability / Model Ops Engineer
Skills That Matter
- CI/CD for ML, model registry (MLflow)
- Docker, Kubernetes, cloud (AWS/GCP/Azure)
- Monitoring & drift detection (Prometheus/Evidently)
- Serving (TorchServe/Triton), feature stores, data contracts
Why Now
- Companies productize AI → need reliable pipelines
- Compliance & cost control push for strong Ops
- Upskilling wave among engineers in India
Talk to our Career Team
Note: ranges are indicative and vary by company, domain, and location.
Why Choose Our MLOps Course Vs Free Tutorials
| Feature | Our MLOps Course | Free / Other Courses |
|---|---|---|
| End-to-End ML Lifecycle | ✔ Covers data pipelines, CI/CD, training, deployment, and monitoring | ✘ Limited to theory or isolated topics |
| Tools & Platforms | ✔ MLflow, DVC, Docker, Kubernetes, Kubeflow, SageMaker, Azure ML, Vertex AI | ✘ Focus on notebooks, missing real infra tools |
| Deployment & Monitoring | ✔ Production-grade with FastAPI, TorchServe, Prometheus, Grafana | ✘ Usually stops at model training only |
| Live Projects | ✔ Real projects with CI/CD, drift detection, and retraining workflows | ✘ Demo-level exercises without pipelines |
| Placement Support | ✔ Resume building, interview prep, and job assistance until placed | ✘ No structured career support |
| Certification Value | ✔ Industry-recognized certification with portfolio-grade projects | ✘ Limited recognition outside the platform |
Explore AI Infrastructure Tracks
Built for DevOps / Platform / SRE engineers who care about reliability, release discipline, monitoring, rollback, and cost — not just notebooks. Pick the track that matches the systems you operate.
MLOps
Best for
Platform/DevOps/SRE + ML Engineers shipping classical ML to production
Core focus
Release discipline for ML: pipelines, CI/CD, K8s, serving, monitoring, drift response
You’ll walk away with
- Production-grade ML pipeline (data → train → registry → deploy → monitor)
- Kubernetes deploy + rollout + rollback patterns
- Monitoring, drift, and incident-friendly operations
mlops course • mlops certification course • ml ops training • mlops course online
LLMOps
Best for
Engineers owning LLM deployments: RAG, agents, inference cost/latency, evaluation
Core focus
LLM production ops: inference serving, RAGOps, AgentOps, eval/observability, governance & cost control
You’ll walk away with
- LLM serving stack (latency, throughput, GPU utilization mindset)
- RAG + eval pipelines + guardrails (hallucination control)
- Observability for prompts/retrieval + reliability patterns
llmops course • llm deployment • ragops • agentops • llm observability
AIOps
Best for
Teams operating end-to-end AI: ML + DL + GenAI monitoring, drift, automation, reliability
Core focus
Full AI operations: data/model/prompt drift, monitoring, retraining automation, governance & production reliability
You’ll walk away with
- Unified ops view across ML + GenAI systems
- Drift + monitoring + automated response workflows
- Production-grade reliability across the AI lifecycle
aiops course • aiops training • mlops + llmops • monitoring & drift
Quick pick (for Platform/SRE mindset)
Pick MLOps if…
- You operate ML pipelines + deployments in K8s
- You want CI/CD, rollback, drift response, monitoring
- You want portfolio-ready “production ML stack” proof
Pick LLMOps if…
- You run LLM inference + RAG + agentic workflows
- Latency/cost/GPU utilization is your daily pain
- You need eval + guardrails + LLM observability
Pick AIOps if…
- You want full lifecycle ops across ML + GenAI
- You want unified monitoring + automated response
- You’re building org-wide AI reliability standards
Industry-Recognized MLOps Certificate
On completing the MLOps Certification Course, you’ll receive an industry-grade certificate that validates your expertise in CI/CD, Docker, Kubernetes, MLflow, Kubeflow, and cloud platforms. This certification proves you can design, deploy, and monitor machine learning models at scale.
SCHOOLOFCOREAI
OF ACHIEVEMENT
This certificate is presented to
Shweta Sharma
Has successfully completed the MLOps Certification Course and demonstrated the ability to manage machine learning models in production environments.
Aishwarya Pandey
Founder & CEO
DD/MM/YY
SCAI-MLOPS-000123
MLOps Program Details — Duration, Format & Batch Schedule
Duration
5 Months
Format
100% Live • Instructor-Led (Online)
Mentorship
Live guidance + unblock support during labs
Career Support
Resume + project positioning + hiring pattern prep
Certificate
Industry-recognized certificate on completion
Upcoming Batch
Limited seats • Early bird pricing available
🚀
Next cohort starting soon!
Batch dates update frequently. Submit the form to get the next start date + schedule in 1 message.
MLOps Course Fees
One-time fee for our MLOps certification course with live mentorship, projects, and placement preparation.
One-time Fee
₹60,000
5 months • Live ILT • Projects
5 months duration
Live mentorship
Capstone projects
Certificate included
Transparent pricing — no hidden charges. Built for engineers targeting real MLOps roles.
What you get with this MLOps course fee
- Live mentorship + review cycles on your pipelines and deployments.
- Capstone projects aligned to production tooling (MLflow, Docker, Kubernetes, CI/CD).
- Placement prep: resume + LinkedIn review, mock interviews, and referral guidance.
- Recordings + reusable templates, checklists, and future course updates.
What Our Learners Say
Real journeys: from notebooks → production-grade MLOps
MLOps Engineer • Moved from notebooks → production
"I finally understood what “production ML” actually means. The course didn’t just teach tools — it made me build a deployable pipeline end-to-end."
- Built MLflow tracking + model registry workflow
- Containerized inference service with Docker
- Deployed on Kubernetes with rollout basics
Ankit M.
MLOps Engineer
Data Scientist • Wanted CI/CD + deployment clarity
"CI/CD for ML was confusing until I implemented it step-by-step. The live mentorship made the engineering side feel practical."
- Added automated checks to pipeline steps
- Structured training → versioning → deploy flow
- Understood monitoring + drift response patterns
Ritika S.
Data Scientist
ML Engineer • Transitioning to MLOps responsibilities
"The best part was the “real pipeline thinking”. Versioning, reproducibility, and monitoring were treated as defaults — not extras."
- Used DVC-style versioning for reproducible runs
- Added metrics + dashboards mindset
- Improved portfolio project quality for interviews
Saurabh J.
ML Engineer
AI Platform Engineer • Automating manual deployments
"I used to deploy models manually. After this, I could design repeatable deploy steps and make the system more reliable for the team."
- Built API-based serving workflow (FastAPI pattern)
- Deployment workflow + environment consistency
- Reduced repeated manual steps in delivery
Priya V.
AI Platform Engineer
DevOps / Cloud Engineer • Adding ML deployment skills
"This helped me connect DevOps concepts to ML workloads. I now understand how ML serving differs from typical web services."
- Learned ML-specific deployment constraints
- Understood scaling + rollout tradeoffs for inference
- Explored optional cloud track directionally
Rohit M.
DevOps / Cloud Engineer
Entry-level Engineer • Started with basics, built confidence
"I was worried it would be too advanced, but the learning flow was structured. Once the basics clicked, the production part felt achievable."
- Improved Python + Git workflow for projects
- Built a portfolio-ready end-to-end assignment
- Got resume + project positioning support
Neha K.
Entry-level Engineer
Explore Our Core AI Tracks
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🎁 Special: Bundle any 2 courses & save 20%
Frequently Asked Questions
Quick answers on eligibility, tools, projects, certification, and placement support.
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