MLOps: Build & Deploy ML Systems

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.

This programme introduces MLOps practices for building, deploying, and maintaining reliable machine learning systems in real-world environments. You’ll begin by understanding how operational machine learning differs from traditional software development. This includes the challenges of managing changing datasets, experimental results, model versions, and performance after deployment. Next, you’ll explore techniques for recording experiments, controlling data changes, and reproducing training results. These practices improve collaboration, traceability, and consistency across the machine learning lifecycle. You’ll then learn how automated workflows coordinate data preparation, training, testing, validation, and release activities. This helps teams reduce manual effort, identify issues earlier, and deliver updates more efficiently. The later sections focus on managing approved models, packaging prediction services, and releasing them into production environments. You’ll also examine how continuous monitoring, drift detection, and retraining strategies help preserve accuracy and reliability over time. By the end of this course, you will be able to: • Explain the principles of MLOps and how they differ from traditional DevOps practices. • Track experiments, control data changes, and reproduce training results. • Build automated workflows for model development, testing, and delivery. • Manage model versions, approvals, and lifecycle stages. • Package and deploy models as scalable prediction services. • Monitor production performance and identify data or model drift. • Apply retraining strategies to maintain long-term model effectiveness. Designed for aspiring ML engineers, AI engineers, data scientists, and software developers, this programme provides the practical skills needed to move machine learning solutions from experimentation into dependable production use. To be successful, you should have a basic understanding of Python, machine learning concepts, and model training. Develop the operational knowledge required to create machine learning systems that are reproducible, scalable, and ready for production.