Building and Scaling ML Pipelines

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.