Machine learning infrastructure
Machine learning infrastructure is the hardware, software, data, and services supporting the development, training, deployment, and operation of machine-learning systems.
Machine Learning Platform Engineering
An integrated set of infrastructure and services that supports the development, deployment, orchestration, and operation of machine learning workloads.
- How it is used
- Provides the operational foundation for developing, deploying, orchestrating, and maintaining production ML systems.
- How deep it goes
- Builds a platform step by step from Kubernetes through Kubeflow, orchestration, experiment tracking, model management, and feature management.
- Examples
- Uses Jupyter Notebooks, Kubeflow Pipelines, MLflow, Feast, OCR, recommender, and RAG documentation systems as implementation examples.
- What you can do after
- Build it from scratch, integrate its components, and operate repeatable ML workflows for production deployment.
Read how Machine Learning Platform Engineering explains machine learning infrastructure →
Designing Deep Learning Systems: A guide for software engineers
The software, hardware, and services that support machine learning data processing, training, deployment, and operation.
- How it is used
- Supports scalable data processing, model development, deployment, serving, monitoring, and maintenance across production workflows.
- How deep it goes
- Defines component responsibilities and organizational value, but stops short of implementation commands, configurations, or detailed system internals.
- Examples
- Names data warehouses, compute platforms, orchestration services, artifact stores, training services, serving services, and monitoring systems.
- What you can do after
- Operate a repeatable path for building, deploying, monitoring, and maintaining models with less cross-team coordination.
Related concepts
- Kubeflowincludes · Kubeflow is an open-source platform for developing, deploying, and orchestrating machine-learning workflows on Kubernetes.
- mlops engineerconfigured by · An MLOps engineer operates and automates the infrastructure and workflows supporting machine-learning systems throughout their lifecycle.
- Google Vertex AIincludes · Google Vertex AI is Google Cloud’s managed platform for developing, training, deploying, and managing machine-learning models and AI applications.
- libraryuses · A software library is a reusable collection of code or components that other programs can incorporate or invoke to provide functionality.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.