Overview

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This course provides an intermediate-level exploration of MLOps, focusing on how machine learning systems are scaled, productionized, and managed across feature engineering, training, orchestration, serving, and deployment. You will examine how modern ML solutions use feature stores, Kubernetes, Kubeflow, distributed training, advanced serving frameworks, progressive release strategies, and inference optimization techniques. Through hands-on demonstrations and practical exercises, you will gain experience building reliable and scalable workflows with industry-standard technologies such as Feast, Great Expectations, Kubernetes, Kubeflow, Optuna, Ray, MLflow, BentoML, KServe, ONNX, and autoscaling. By the end of this course, you will be able to: - Build and validate production-ready feature engineering pipelines - Manage batch, streaming, online, and offline features using Feast - Run scalable training workflows with Kubernetes and Kubeflow Pipelines - Serve models using BentoML, Seldon, and KServe - Apply canary releases and A/B testing to deploy new model versions safely This course is designed for ML engineers, AI engineers, data scientists, DevOps professionals, and software developers who want to scale machine learning operations and deliver dependable models in cloud-native environments. A foundational understanding of MLOps, Python, machine learning, Docker, and deployment concepts is recommended.

Syllabus

Feature Engineering Pipelines and Feature Stores

Build a strong foundation in feature engineering pipelines and feature stores by learning how features are created, transformed, validated, stored, and reused across ML workflows. Explore how pipelines improve training-inference consistency, reduce duplication, and support scalable production systems. Apply these concepts through hands-on exercises to design reliable feature workflows for governance, reuse, and real-time serving.

Scalable Training and Workflow Orchestration on Kubernetes

Build practical skills in scalable training and workflow orchestration on Kubernetes by learning how ML workloads are scheduled, managed, and scaled across distributed infrastructure. Explore how Kubernetes supports containerized training jobs, resource allocation, workflow automation, and reliable execution of ML pipelines. Apply these concepts through hands-on activities to orchestrate training workflows, manage compute resources efficiently, and support scalable production-ready ML operations.

Advanced Model Serving and Deployment Patterns

Develop practical expertise in advanced model serving and deployment patterns for production ML systems. Learn how serving frameworks, deployment strategies, and inference optimization techniques support reliable model delivery at scale. Apply these concepts to package models, deploy services on Kubernetes, run controlled releases, optimize inference performance, and configure autoscaling for production-ready ML and LLM services.

Course Wrap-Up and Assessment

This module consolidates learning through a hands-on vision project and final assessment. Learners demonstrate their ability to design, train, and evaluate complete their MLOps journey.