MLOps Fundamentals for Beginners – Learn DevOps for ML

Discover core mlops concepts at the intersection of machine learning, devops, and data engineering, and learn to develop, deploy, monitor, and maintain ml models in production.

Master DevOps to MLOps through continuous integration and delivery, detailing git-based code management, build pipelines with Jenkins, GitHub actions, and GitLab CI CD, and deployment to dev, staging, and production.

Explore MLOps tools like Jenkins, Kubernetes, Git, Docker, Terraform, and Kubeflow, and learn how CI/CD, containerization, and orchestration automate training and deployment of ML models.

Explore cloud platforms for ML ops across AWS, GCP, and Azure, covering SageMaker, Vertex AI, and Azure ML, plus data ingestion with Glue, and deployment via endpoints and GKE.

Train an iris dataset model and deploy an end-to-end ML workflow on AWS, containerized with Docker, exposing a Python UI on port 5000 for predictions.

Train a machine learning model in Google Colab by loading data, performing preprocessing and EDA, splitting data, training a random forest, evaluating with accuracy, and saving the model with pickle.

Explore model artifacts in MLOps, including weights, checkpoints, configurations in json and yaml, and serialized forms like pickle and joblib, plus onnx and tflite models.

Define your problem, map data types, and select the right ML model—from structured data with XGBoost to unstructured data with CNN or Transformers.

Learn how to train ML models, tune hyperparameters, and evaluate performance using regression, classification, and clustering metrics, with experiment tracking tools like MLflow, Azure ML, and TensorBoard.

Learn how to deploy a trained ML model with FastAPI, expose real-time predictions via an API, and containerize the service with Docker for production-ready inference.

Explore MLflow for experiment tracking, metrics, hyperparameters, and model registry. Learn to deploy MLflow on Kubernetes with helm charts using Postgres and Minio as backend and artifact stores.

Learn to set up MLflow for experiment tracking, configure a storage backend (MinIO or SQL databases), start the MLflow server, and prepare for logging experiments with Docker or Kubernetes.

Log experiments with MLflow by running a Python script to capture parameters, estimators, and MSE, manage artifacts, and register champion and challenger models with production or staging aliases for inference.

Understand model drift, including concept drift, data drift, label drift, and feature drift, and how they impact predictions. Learn detection, monitoring, and mitigation strategies including PSI and Earth Mover's distance.

Bridge model development and deployment by pairing data scientists with DevOps engineers in MLOps, building pipelines, monitoring, and retraining when drift is detected.

Explore future trends in MLOps and AI, featuring automated operations, model training and monitoring, foundation model governance, neural architecture search, and no-code tools.

Acquire essential MLOps skills, including Python and SQL, PyTorch, TensorFlow, Kubernetes, cloud platforms, and NLP, covering governance, deployment, monitoring, drift detection, optimization, and collaboration.

Explore career pathways in mlops by building ml infrastructure with devops skills. Master python, git, docker, kubernetes, and ci/cd to train, test, deploy, and monitor models.