Course Objectives
1. What will you learn in the MLOps – Machine Learning Operations course?
You will learn how to build, deploy, automate, monitor, and manage machine learning models in production using modern MLOps practices. The course covers the complete ML lifecycle, model versioning, CI/CD for ML, Docker, Kubernetes, MLflow, Kubeflow, cloud deployment, feature stores, model monitoring, automation, and scalable machine learning pipelines.
2. How does this course prepare you for real-world MLOps projects?
The course provides hands-on experience in deploying machine learning models, automating ML workflows, creating CI/CD pipelines, monitoring model performance, managing datasets, integrating cloud platforms, and implementing enterprise-grade MLOps solutions.
3. What practical skills will you develop during the training?
You will gain expertise in MLflow, Kubeflow, Docker, Kubernetes, Git, CI/CD pipelines, model deployment, model monitoring, feature engineering pipelines, cloud MLOps, experiment tracking, and automated machine learning workflows.
4. How will real-time projects improve your MLOps skills?
You will work on enterprise projects involving fraud detection deployment, recommendation systems, predictive maintenance, customer churn prediction, image classification, cloud-based ML pipelines, and automated model monitoring.
5. What career opportunities can you pursue after learning MLOps?
This course prepares you for roles such as MLOps Engineer, Machine Learning Engineer, AI Engineer, Data Engineer, DevOps Engineer for AI, ML Platform Engineer, Cloud AI Engineer, Data Science Engineer, AI Infrastructure Engineer, and Machine Learning Solutions Architect.
Popular Techniques Covered in This Course
Machine Learning Lifecycle Management
MLflow and Experiment Tracking
CI/CD Pipelines for Machine Learning
Docker and Kubernetes for ML Deployment
Kubeflow Pipeline Development
Model Versioning and Registry
Feature Stores and Data Pipelines
Cloud MLOps (AWS, Azure, and GCP)
Model Monitoring and Performance Optimization
Production Deployment and Automation
Get Hands-on Knowledge about Real-Time Projects
Project 1
Project 1: Customer Churn Prediction Pipeline
Build an automated machine learning pipeline that trains, deploys, monitors, and updates customer churn prediction models using MLflow and Kubernetes.
Project 2
Project 2: Fraud Detection Deployment
Deploy a fraud detection model using Docker, Kubernetes, CI/CD pipelines, and cloud infrastructure with automated monitoring and alerting.
Project 3
Project 3: Product Recommendation System
Develop an end-to-end recommendation engine with feature engineering, automated retraining, model registry, and production deployment.
Project 4
Project 4: Predictive Maintenance Platform
Create a machine learning solution for equipment failure prediction with automated data pipelines, model deployment, monitoring, and cloud integration.
Project 5
Project 5: Enterprise MLOps Platform
Design and deploy a complete MLOps platform integrating Git, MLflow, Kubeflow, Docker, Kubernetes, CI/CD, cloud deployment, monitoring dashboards, and automated model lifecycle management.
Key Features
Edubrights offers MLOps – Machine Learning Operations Training in virtual mode with expert trainers. Here are the key features,
40 Hours Course Duration
100% Job Oriented Training
Industry Expert Faculties
Free Demo Class Available
Completed 500+ Batches
Certification Guidance
Curriculum
Module 1: Introduction to MLOps
- What is MLOps and why it matters for production ML
- MLOps vs DevOps: key differences and similarities
- The ML lifecycle: from experimentation to production
- MLOps maturity levels and organisational readiness
Module 2: Experiment Tracking and Model Management
- Introduction to MLflow: tracking experiments and metrics
- Logging parameters, metrics, and artefacts with MLflow
- MLflow Model Registry: versioning and stage transitions
- Comparing experiments and selecting the best model
Module 3: Data Versioning and Pipeline Management
- Data versioning with DVC (Data Version Control)
- Building reproducible ML pipelines with DVC stages
- Managing datasets, features, and model artefacts
- Integrating DVC with Git for full pipeline reproducibility
Module 4: CI/CD for Machine Learning
- Applying CI/CD principles to ML workflows
- Automated testing for ML: data validation and model tests
- Building ML pipelines with GitHub Actions
- Continuous training and model retraining pipelines
Module 5: Model Serving and Deployment
- Model serving patterns: batch, real-time, and streaming
- Deploying models with FastAPI and Docker
- ML model serving with MLflow, TorchServe, and TF Serving
- Deploying to cloud: AWS SageMaker, Azure ML, and GCP Vertex AI
Module 6: Feature Stores
- What is a feature store and why it is critical for MLOps
- Introduction to Feast and Tecton feature stores
- Offline vs online feature serving
- Building and registering features for production ML
Module 7: Model Monitoring and Observability
- Types of ML model drift: data, concept, and prediction drift
- Monitoring model performance in production
- Tools: Evidently AI, WhyLabs, and Arize
- Alerting, retraining triggers, and incident response
Module 8: LLMOps and Capstone Project
- MLOps for large language models: LLMOps overview
- Managing LLM fine-tuning pipelines and model registries
- Prompt versioning and evaluation pipelines
- Hands-on capstone: end-to-end MLOps pipeline project
Receive Training From Our Skilled and Effective Trainers
Experience in the Industry Gain expertise from MLOps engineers who have designed and operated production ML pipelines, model registries, and monitoring systems for large-scale AI deployments.
Backgrounds at the Top Our MLOps trainers have implemented ML platforms at leading e-commerce, fintech, and cloud-native technology companies using tools like MLflow, Kubeflow, and AWS SageMaker.
Clear & Effective Teaching ML lifecycle management, CI/CD for ML, model monitoring, and drift detection are taught with practical toolchain examples and real pipeline demonstrations.
Hands-On Learning Focus Students build end-to-end MLOps pipelines using MLflow, DVC, GitHub Actions, and cloud ML platforms through comprehensive project-based labs.
Up-to-Date Knowledge Trainers continuously update content with the latest MLOps tooling, LLMOps practices for generative AI, and cloud provider ML platform updates.
Professional MLOps – Machine Learning Operations Certification
The Professional MLOps – Machine Learning Operations Certification validates your expertise in deploying, managing, monitoring, and automating machine learning models in production.
Course FAQs
1. What is MLOps?
Answer: MLOps (Machine Learning Operations) is the practice of automating, deploying, monitoring, and managing machine learning models throughout their production lifecycle.
2. Why is MLOps important?
Answer: MLOps improves collaboration between data scientists and DevOps teams while ensuring scalable, reliable, secure, and automated machine learning deployments.
3. What is MLflow?
Answer: MLflow is an open-source platform used for experiment tracking, model management, model registry, deployment, and machine learning lifecycle management.
4. What is Kubeflow?
Answer: Kubeflow is an open-source platform that simplifies deploying, orchestrating, and managing machine learning workflows on Kubernetes.
5. Why is Docker used in MLOps?
Answer: Docker packages machine learning applications into portable containers, making deployment consistent across development, testing, and production environments.
6. Which industries use MLOps?
Answer: Banking, healthcare, retail, manufacturing, finance, telecommunications, insurance, logistics, e-commerce, automotive, and technology companies use MLOps.
7. Does the course include practical projects?
Answer: Yes. The course includes AI deployment, ML automation, cloud pipelines, monitoring, fraud detection, recommendation systems, and predictive analytics projects.
8. Is this course beginner-friendly?
Answer: Basic Python programming, machine learning, and cloud fundamentals are recommended before learning advanced MLOps concepts.
9. Can MLOps work with cloud platforms?
Answer: Yes. MLOps integrates with AWS, Microsoft Azure, Google Cloud Platform, Kubernetes, and various cloud-native machine learning services.
10. Why should professionals learn MLOps?
Answer: MLOps is one of the fastest-growing AI disciplines, enabling organizations to deploy scalable machine learning solutions efficiently while creating excellent career opportunities.
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