MLOps Course: Automate, Deploy & Monitor ML in Production
Become a skilled MLOps Engineer with BinnBash Academy's comprehensive course. Master Docker, Kubernetes, CI/CD for ML, MLflow, Kubeflow, and cloud MLOps platforms (AWS, Azure, GCP). Learn to automate, deploy, and monitor machine learning models reliably in production environments!
Automate Your ML Future!Who Should Enroll in this MLOps Course?
This course is ideal for professionals looking to bridge the gap between Machine Learning and Operations:
- Machine Learning Engineers seeking to productionize models.
- Data Scientists who want to understand deployment and monitoring.
- DevOps Engineers looking to specialize in ML infrastructure.
- Software Engineers interested in ML system design and automation.
- Data Engineers who want to extend their skills into ML deployment.
- Anyone with a foundational understanding of ML and programming, eager to build scalable ML systems.
MLOps Course Prerequisites
- Basic understanding of Machine Learning concepts and algorithms.
- Proficiency in Python programming.
- Familiarity with Git and version control.
- Basic knowledge of Linux command line.
- Understanding of cloud computing concepts (e.g., AWS, Azure, GCP) is a plus.
- Prior experience with Docker or Kubernetes is beneficial but not mandatory.
Key MLOps Tools & Technologies Covered
Hands-on practice building, deploying, and managing robust and scalable ML systems.
MLOps: Comprehensive Syllabus & Practical Contents
Module 1: Introduction to MLOps & Foundations
- What is MLOps? Bridging ML, DevOps & Data Engineering.
- The Machine Learning Lifecycle in Production.
- Challenges in Productionizing ML Models.
- MLOps Principles and Best Practices.
- Version Control for ML Code, Data & Models (Git, DVC concepts).
- Lab: Set up a version-controlled ML project, explore DVC.
Tools & Concepts:
- MLOps Concepts, Git, DVC.
Expected Outcomes:
- Understand MLOps principles.
- Manage ML project versions.
- Identify production challenges.
Module 2: Experiment Tracking & Model Registry
- Importance of Experiment Tracking.
- Using MLflow for Experiment Tracking.
- Logging Parameters, Metrics, and Artifacts.
- Model Registry: Managing Model Versions.
- Model Lifecycle Management (Staging, Production).
- Lab: Track ML experiments and manage model versions with MLflow.
Tools & Concepts:
- MLflow, Model Registry.
Expected Outcomes:
- Track ML experiments effectively.
- Manage model versions.
- Understand model lifecycle.
Module 3: CI/CD for Machine Learning
- Continuous Integration (CI) for ML Code.
- Continuous Delivery/Deployment (CD) for ML Models.
- Automated Testing for ML (unit, integration, data validation).
- Building CI/CD Pipelines with GitHub Actions / GitLab CI / Jenkins.
- Code Quality & Linting for ML Projects.
- Lab: Implement CI/CD pipelines for an ML project.
Tools & Concepts:
- CI/CD, GitHub Actions/GitLab CI, Testing.
Expected Outcomes:
- Automate ML code integration.
- Build robust deployment pipelines.
- Implement ML testing strategies.
Module 4: Model Deployment & Serving
- Containerization with Docker for ML Models.
- Orchestration with Kubernetes (Pods, Deployments, Services).
- Building REST APIs for Model Serving (Flask/FastAPI).
- Batch vs. Real-time Inference.
- Model Serving Patterns (Shadow Deployment, Canary Release).
- Lab: Containerize and deploy ML models to Kubernetes.
Tools & Concepts:
- Docker, Kubernetes, Flask/FastAPI.
Expected Outcomes:
- Deploy models using containers.
- Orchestrate ML services.
- Implement various serving patterns.
Module 5: ML Model Monitoring & Management
- Importance of Model Monitoring in Production.
- Data Drift & Concept Drift Detection.
- Model Performance Monitoring (Latency, Throughput, Error Rates).
- Monitoring Tools (Prometheus, Grafana).
- Alerting & Incident Management.
- Model Retraining Strategies.
- Lab: Set up monitoring for a deployed ML model, detect drift.
Tools & Concepts:
- Prometheus, Grafana, Drift Detection.
Expected Outcomes:
- Monitor ML model health.
- Detect model degradation.
- Implement retraining strategies.
Module 6: MLOps on Cloud & Advanced Topics
- MLOps on AWS (Sagemaker Pipelines, Feature Store, Model Monitor).
- MLOps on Azure (Azure ML Pipelines, Endpoints).
- MLOps on GCP (Vertex AI Pipelines, Endpoints, Workbench).
- Infrastructure as Code (Terraform basics for ML infrastructure).
- Ethical MLOps & Responsible AI.
- Building a Professional MLOps Portfolio.
- Final Project: End-to-end MLOps pipeline on a chosen cloud platform.
Tools & Concepts:
- AWS Sagemaker, Azure ML, GCP Vertex AI.
- Terraform, Ethical AI, Portfolio Building.
Expected Outcomes:
- Build cloud MLOps solutions.
- Automate infrastructure.
- Secure an MLOps Engineer job.
This course provides hands-on expertise to make you a proficient and job-ready MLOps Engineer!
MLOps Engineer Roles and Responsibilities in Real-Time Scenarios & Live Projects
Gain hands-on experience by working on live projects, understanding the real-time responsibilities of an MLOps Engineer in leading global companies. Our curriculum is designed to align with industry best practices for scalable and reliable ML systems.
Automated ML Pipelines
Design and implement automated CI/CD pipelines for machine learning models, ensuring continuous integration, delivery, and deployment, as done at Google.
Model Deployment & Serving
Deploy and manage ML models in production environments using containerization (Docker) and orchestration (Kubernetes) tools, ensuring high availability and low latency, similar to work at Amazon.
Model Monitoring & Alerting
Set up robust monitoring systems (e.g., Prometheus, Grafana) to track model performance, data drift, concept drift, and system health, and configure alerts for proactive issue detection, common at Netflix.
Version Control & Experiment Tracking
Implement best practices for versioning ML code, data, and models, and utilize tools like MLflow or DVC for comprehensive experiment tracking and reproducibility.
ML Infrastructure Management
Manage and optimize the underlying infrastructure for ML workloads, including cloud resources (AWS, Azure, GCP), compute, and storage, often using Infrastructure as Code (e.g., Terraform).
Collaboration with Data Scientists/DevOps
Work closely with Data Scientists to transition models from experimentation to production, and collaborate with DevOps teams to integrate ML pipelines into broader software delivery processes.
ML Model Governance & Security
Ensure compliance, security, and ethical considerations are integrated into the ML lifecycle, including access controls, data privacy, and bias detection in models.
Automation & Optimization
Identify opportunities for automation across the ML lifecycle, from data ingestion to model retraining, and continuously optimize ML workflows for efficiency and cost-effectiveness.
Our Alumni Works Here!
Akash Sharma
MLOps Engineer
Priya Singh
ML Platform Engineer
Rahul Gupta
Production ML Engineer
Sneha Reddy
DevOps Engineer (ML)
Vikram Joshi
MLOps Specialist
Divya Kumar
Associate MLOps Eng.
Karan Desai
MLOps Intern
Meena Patel
Cloud MLOps Eng.
Siddharth Rao
Data Science Ops Lead
Neha Sharma
ML Systems Engineer
Akash Sharma
MLOps Engineer
Priya Singh
ML Platform Engineer
Rahul Gupta
Production ML Engineer
Sneha Reddy
DevOps Engineer (ML)
Vikram Joshi
MLOps Specialist
Divya Kumar
Associate MLOps Eng.
Karan Desai
MLOps Intern
Meena Patel
Cloud MLOps Eng.
Siddharth Rao
Data Science Ops Lead
Neha Sharma
ML Systems Engineer
What Our MLOps Students Say
"This MLOps course is a game-changer! I learned how to productionize ML models efficiently using Docker and Kubernetes."
"The CI/CD for ML module was incredibly insightful. Automating deployments with GitHub Actions has streamlined our workflow."
"MLflow and Kubeflow hands-on practice gave me the skills to track experiments and manage models effectively in production."
"BinnBash Academy's focus on cloud MLOps platforms like AWS Sagemaker was exactly what I needed to advance my career."
"The instructors are industry experts who provide practical knowledge and real-world scenarios, making complex MLOps concepts understandable."
"I highly recommend this course for any data scientist or ML engineer looking to bridge the gap between development and production."
"From model monitoring to retraining strategies, every aspect of MLOps was covered comprehensively. I feel fully prepared for the industry."
"The emphasis on ethical MLOps and responsible AI was a crucial addition, highlighting the importance of building fair and transparent systems."
"Learning to automate and monitor ML pipelines has significantly improved my team's efficiency and model reliability."
"The practical approach to learning, combined with industry-relevant tools, made this course stand out from others."
"This MLOps course is a game-changer! I learned how to productionize ML models efficiently using Docker and Kubernetes."
"The CI/CD for ML module was incredibly insightful. Automating deployments with GitHub Actions has streamlined our workflow."
"MLflow and Kubeflow hands-on practice gave me the skills to track experiments and manage models effectively in production."
"BinnBash Academy's focus on cloud MLOps platforms like AWS Sagemaker was exactly what I needed to advance my career."
"The instructors are industry experts who provide practical knowledge and real-world scenarios, making complex MLOps concepts understandable."
"I highly recommend this course for any data scientist or ML engineer looking to bridge the gap between development and production."
"From model monitoring to retraining strategies, every aspect of MLOps was covered comprehensively. I feel fully prepared for the industry."
"The emphasis on ethical MLOps and responsible AI was a crucial addition, highlighting the importance of building fair and transparent systems."
"Learning to automate and monitor ML pipelines has significantly improved my team's efficiency and model reliability."
"The practical approach to learning, combined with industry-relevant tools, made this course stand out from others."
MLOps Engineer Job Roles After This Course
MLOps Engineer
ML Platform Engineer
Production ML Engineer
DevOps Engineer (ML Focus)
ML Infrastructure Engineer
ML Systems Engineer
AI Operations Specialist
MLOps Consultant