What will you learn in Automation Testing using TestComplete 11.0 Course
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Grasp the end-to-end MLOps lifecycle, from data preparation to model monitoring.
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Containerize machine learning models using Docker and manage them with Kubernetes.
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Build automated ML pipelines with tools like Airflow and Jenkins.
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Implement CI/CD practices tailored for data and model versioning.
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Monitor model performance in production and handle drift using Prometheus and Grafana.
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Leverage feature stores and experiment tracking with MLflow or similar platforms.
Program Overview
Module 1: Introduction to MLOps & Architecture
2 hours
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Topics: MLOps principles, differences from DevOps, MLOps components.
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Hands-on: Sketch an MLOps reference architecture and identify toolchain components.
Module 2: Version Control & Experiment Tracking
2.5 hours
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Topics: Git for code, DVC for data and model versioning, MLflow overview.
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Hands-on: Track an experiment end-to-end with DVC and MLflow.
Module 3: Containerization with Docker
3 hours
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Topics: Docker images, Dockerfiles, best practices for ML workloads.
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Hands-on: Containerize a trained model and run inference in a Docker container.
Module 4: Orchestration with Kubernetes
3.5 hours
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Topics: Kubernetes basics, pods, deployments, services, ConfigMaps and Secrets.
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Hands-on: Deploy your Dockerized model on a local Kubernetes cluster.
Module 5: Building Automated Pipelines
3 hours
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Topics: Airflow DAGs, pipeline scheduling, retries, and monitoring.
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Hands-on: Create an Airflow pipeline that ingests data, trains a model, and registers it.
Module 6: CI/CD for ML
3 hours
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Topics: Jenkins/GitHub Actions pipelines, automated testing of data and code.
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Hands-on: Set up a CI/CD workflow that runs unit tests, trains, and deploys a model.
Module 7: Model Serving & APIs
2.5 hours
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Topics: Model serving frameworks (FastAPI, TensorFlow Serving), load balancing.
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Hands-on: Expose your model as a REST API with Docker and test it with sample requests.
Module 8: Monitoring & Observability
2.5 hours
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Topics: Metrics collection, Prometheus exporters, Grafana dashboards.
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Hands-on: Instrument your serving endpoint, collect metrics, and visualize them.
Module 9: Feature Store & Data Management
2 hours
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Topics: Concepts of feature stores, online vs. offline features, data cataloging.
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Hands-on: Set up a simple feature store and retrieve features for inference.
Module 10: Capstone Project – End-to-End MLOps Pipeline
4 hours
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Topics: Combine all components to automate training, deployment, and monitoring.
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Hands-on: Deliver a fully automated pipeline that retrains on new data and updates production.
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Job Outlook
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MLOps engineers are among the fastest-growing roles in AI, with salaries ranging $110K–$160K+.
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Expertise in CI/CD, container orchestration, and model monitoring is highly sought by tech companies and enterprises embarking on AI projects.
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Skills translate into roles such as MLOps Engineer, ML Platform Engineer, and AI Infrastructure Architect.
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Growing freelance opportunities exist to build robust ML workflows for startups and consultancies.
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