MLOps Training — Machine Learning Operations
Master the complete MLOps lifecycle: from experiment tracking to production deployment, model monitoring, and automated retraining. The most hands-on MLOps training in India.
MLOps (Machine Learning Operations) is the discipline of deploying, managing, and monitoring machine learning models in production environments. As companies move from experimental ML to production AI, MLOps Engineers are in massive demand. This comprehensive training by Rajinikanth Vadla teaches you everything from ML pipeline design to production model serving, monitoring for data drift, automated retraining, and multi-cloud ML deployment. You'll work with industry-standard tools like MLflow, Kubeflow, DVC, Feast, Apache Airflow, and cloud-native ML services (AWS SageMaker, Azure ML, GCP Vertex AI). Every concept is backed by hands-on labs using real enterprise patterns.
What You'll Learn
- Design and build end-to-end ML pipelines for production
- Track experiments with MLflow and Weights & Biases
- Version data, code, and models with DVC and Git
- Deploy ML models as REST APIs with FastAPI and Flask
- Build feature stores with Feast for real-time serving
- Orchestrate ML workflows with Kubeflow and Airflow
- Implement CI/CD pipelines specific to ML workloads
- Monitor models for data drift and concept drift
- Set up A/B testing for ML model evaluation
- Deploy on AWS SageMaker, Azure ML, GCP Vertex AI
- Containerize ML models with Docker and Kubernetes
- Implement automated retraining triggers and pipelines
Course Curriculum
1MLOps Fundamentals
- → ML lifecycle and MLOps maturity model
- → MLOps vs DevOps vs DataOps
- → Industry best practices and patterns
- → Common production ML challenges
- → MLOps team structures and roles
2Data Engineering for ML
- → Data ingestion and validation pipelines
- → Feature engineering at scale
- → Feature stores with Feast
- → Data quality monitoring
- → Apache Airflow for data orchestration
3Experiment Tracking & Management
- → MLflow for experiment tracking
- → Weights & Biases integration
- → Hyperparameter optimization (Optuna)
- → Reproducible ML experiments
- → Experiment comparison and analysis
4Model Versioning & Registry
- → Code versioning with Git for ML
- → Data versioning with DVC
- → Model registry with MLflow
- → Artifact management strategies
- → Reproducibility best practices
5Model Deployment & Serving
- → Model serialization (ONNX, TorchScript, SavedModel)
- → REST API deployment with FastAPI
- → Batch inference systems design
- → Real-time vs batch serving architectures
- → Model serving with TensorFlow Serving, TorchServe
6ML CI/CD Pipelines
- → Automated testing for ML (unit, integration)
- → Continuous training pipelines
- → Continuous deployment for models
- → GitHub Actions / Jenkins for ML CI/CD
- → Pipeline orchestration patterns
7Model Monitoring & Observability
- → Performance metrics monitoring
- → Data drift detection (Evidently, Alibi)
- → Concept drift detection
- → A/B testing for ML models
- → Alerting and automated remediation
8ML Orchestration Platforms
- → Kubeflow Pipelines deep dive
- → AWS SageMaker Pipelines
- → Azure ML Pipelines
- → GCP Vertex AI Pipelines
- → Comparison and when to use each
9Cloud ML Deployment
- → AWS SageMaker: training and endpoints
- → Azure ML: workspaces and deployments
- → GCP Vertex AI: pipelines and serving
- → Multi-cloud ML strategy
- → Cost optimization for ML workloads
10Capstone: Production ML System
- → End-to-end MLOps pipeline design
- → Automated training with CI/CD
- → Production deployment on Kubernetes
- → Monitoring dashboard with Grafana
- → Documentation for your portfolio
Frequently Asked Questions
What is the best MLOps training in India?
Rajinikanth Vadla's MLOps training is rated #1 in India with 4.9/5 rating. It covers the complete MLOps lifecycle with MLflow, Kubeflow, Docker, Kubernetes, and multi-cloud deployment. 500+ engineers trained with 95% placement rate.
What is MLOps and why is it important?
MLOps (Machine Learning Operations) bridges the gap between ML experimentation and production deployment. It automates ML pipelines, ensures model reliability, and enables continuous improvement. Companies need MLOps to scale their AI initiatives.
What is the salary of an MLOps Engineer?
MLOps Engineers earn ₹12-40 LPA in India, $120K-$200K+ in USA, €70K-€130K+ in Europe. Our students see an average 60% salary increase after completing this training.
Do I need ML/Data Science background?
Basic Python and some familiarity with ML concepts helps, but we teach the operational side from scratch. DevOps engineers transitioning to MLOps find this course particularly valuable.
What tools will I learn?
MLflow, Kubeflow, DVC, Feast, Apache Airflow, Docker, Kubernetes, FastAPI, AWS SageMaker, Azure ML, GCP Vertex AI, Evidently, Grafana, Prometheus, Jenkins, GitHub Actions.
Is there placement assistance?
Yes! Resume optimization, mock interviews, LinkedIn review, and active placement support. Our students have a 95% job placement rate in MLOps and ML Engineering roles.
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