MLOps Certification Course – Build & Deploy ML Pipelines
Master MLOps with our industry-ready certification course. Learn to build, deploy, and monitor machine learning models in production using CI/CD, Docker, Kubernetes, MLflow, and Kubeflow. This MLOps course online is designed for data scientists, ML engineers, and DevOps professionals who want hands-on skills, cloud expertise, and placement support to accelerate their careers.
Why Choose Our MLOps Course?
End-to-End ML Lifecycle
Learn the full path from data ingestion to training, packaging, deployment, and monitoring—built the way real teams ship models.
Hands-on Toolchain
Work with MLflow, DVC, Git/GitHub, and Python. Track experiments, version data, and keep your repos production-ready.
Containerization & Orchestration
Package apps with Docker and run them on Kubernetes using Deployments, Services, Ingress, HPA, and Helm charts.
CI/CD for Models
Automate tests, image builds, and releases with GitHub Actions/Jenkins. Enable canary rollouts and safe rollbacks.
Serving at Scale
Expose fast, reliable inference with TorchServe, NVIDIA Triton, or Ray Serve. Support REST/gRPC and multi-model setups.
Monitoring & Alerts
Track latency, throughput, and resource usage with Prometheus and Grafana. Add health checks and on-call friendly alerts.
Data & Model Drift
Detect data quality issues and drift with Great Expectations and Evidently. Trigger retraining or rollback when needed.
Cloud-Ready Deployments
Build on AWS SageMaker, Azure ML, or Google Vertex AI. Use Terraform to provision repeatable, cost-aware infrastructure.
Capstone, Mentorship & Placement
Ship a portfolio-grade project with code reviews, resume help, mock interviews, and placement assistance.
Top Skills You’ll Gain in the MLOps Certification Course
MLOps Tools & Frameworks You’ll Master
MLflow
Experiment Tracking & Registry
Log runs and artifacts, compare experiments, and promote versions via the Model Registry for staging and production.
DVC
Data & Model Versioning
Track large datasets and ML artifacts with Git-friendly pipelines for reproducible training and deployment.
GitHub Actions / Jenkins
CI for ML Pipelines
Automate testing, packaging, image builds, and release steps for data, training, and serving workflows.
Docker
Containerization
Ship consistent environments with lean, secure images and compose-based developer stacks.
Kubernetes
Orchestration at Scale
Deploy and scale jobs, cronjobs, and services with HPA, ConfigMaps/Secrets, and rolling updates.
Helm
Release Management
Template Kubernetes manifests, manage values per environment, and enable safe rollbacks.
Apache Airflow
Workflow Orchestration
Build DAGs for ETL, validation, training, and batch inference with retries and alerting.
Kubeflow / KServe
Pipelines & Model Serving
Design ML pipelines, run Katib HPO, and serve multiple models with traffic-splitting and autoscaling.
TorchServe
PyTorch Model Serving
Expose REST endpoints for PyTorch models, manage versions, and collect metrics for production ops.
NVIDIA Triton
Multi-Framework Inference
Serve TensorFlow, PyTorch, ONNX models with dynamic batching and GPU acceleration.
Ray Serve
Distributed Serving
Scale model APIs horizontally, build DAGs of Python deployments, and balance throughput vs latency.
Prometheus & Grafana
Monitoring & Dashboards
Track latency, throughput, GPU/CPU, and custom model metrics with alerts and visual dashboards.
Evidently AI
Data & Model Drift
Detect drift, monitor data quality, and trigger retraining or rollback based on thresholds.
Great Expectations
Data Quality Testing
Validate schemas and distributions, embed checks in ETL, and fail fast in CI/CD.
Feast
Feature Store
Centralize offline/online features, ensure training–serving consistency, and track lineage.
FastAPI
Model API Gateway
Build lightweight, high-performance inference APIs with type-safe contracts and validation.
Terraform
Infrastructure as Code
Provision cloud compute, storage, and network for training/serving with versioned IaC.
MLflow
Experiment Tracking & Registry
Log runs and artifacts, compare experiments, and promote versions via the Model Registry for staging and production.
DVC
Data & Model Versioning
Track large datasets and ML artifacts with Git-friendly pipelines for reproducible training and deployment.
GitHub Actions / Jenkins
CI for ML Pipelines
Automate testing, packaging, image builds, and release steps for data, training, and serving workflows.
Docker
Containerization
Ship consistent environments with lean, secure images and compose-based developer stacks.
Kubernetes
Orchestration at Scale
Deploy and scale jobs, cronjobs, and services with HPA, ConfigMaps/Secrets, and rolling updates.
Helm
Release Management
Template Kubernetes manifests, manage values per environment, and enable safe rollbacks.
Apache Airflow
Workflow Orchestration
Build DAGs for ETL, validation, training, and batch inference with retries and alerting.
Kubeflow / KServe
Pipelines & Model Serving
Design ML pipelines, run Katib HPO, and serve multiple models with traffic-splitting and autoscaling.
TorchServe
PyTorch Model Serving
Expose REST endpoints for PyTorch models, manage versions, and collect metrics for production ops.
NVIDIA Triton
Multi-Framework Inference
Serve TensorFlow, PyTorch, ONNX models with dynamic batching and GPU acceleration.
Ray Serve
Distributed Serving
Scale model APIs horizontally, build DAGs of Python deployments, and balance throughput vs latency.
Prometheus & Grafana
Monitoring & Dashboards
Track latency, throughput, GPU/CPU, and custom model metrics with alerts and visual dashboards.
Evidently AI
Data & Model Drift
Detect drift, monitor data quality, and trigger retraining or rollback based on thresholds.
Great Expectations
Data Quality Testing
Validate schemas and distributions, embed checks in ETL, and fail fast in CI/CD.
Feast
Feature Store
Centralize offline/online features, ensure training–serving consistency, and track lineage.
FastAPI
Model API Gateway
Build lightweight, high-performance inference APIs with type-safe contracts and validation.
Terraform
Infrastructure as Code
Provision cloud compute, storage, and network for training/serving with versioned IaC.
MLOps Course Roadmap — Python to Production
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
CI/CD for ML
Automate the pipeline: • GitHub Actions/Jenkins flows • Continuous training triggers • Canary & rollback strategies • Helm + K8s release gates
Kubernetes for ML Workloads
Scale and operate in clusters: • Deployments, Services, Ingress • HPA, jobs/cronjobs, PV/PVC • ConfigMaps/Secrets • Helm charts for releases
Docker for ML
Ship the same environment everywhere: • Dockerfile best practices • Build/tag/push to registry • docker-compose for stacks • Image security & slim builds
Training & Hyperparameter Tuning
Train at scale with confidence: • CV & evaluation metrics • Optuna/Ray Tune HPO • TF/PyTorch distributed • Multi-GPU, fault tolerance
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 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
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.
CERTIFICATE
Has successfully completed the MLOps Certification Course and demonstrated the ability to manage machine learning models in production environments.
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 |
Which AI Infrastructure Track Fits You?
- MLOps Course: Master end-to-end ML workflows — from versioning and CI/CD to scalable model serving with Docker, Kubernetes, and MLflow.
- LLMOps Course: Specialize in LLM deployment — covering quantization, vLLM, LangServe, LangSmith, distributed inference, and cost optimization.
- AIOps Course: The all-in-one track — covering MLOps, LLMOps, and AgentOps. Dive deep into drift detection, PromptOps, RAG pipelines, and secure agent deployment.
MLOps Course Fees
Included Benefits:
- Live mentorship from industry MLOps engineers.
- Capstone projects using MLflow, Kubeflow, Docker, and Kubernetes.
- Placement prep: mock interviews, resume building, and referral support.
- Lifetime access to course recordings, toolkits, and future updates.
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
Note: ranges are indicative and vary by company, domain, and location.
What Our Learners Say
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Frequently Asked Questions
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