Machine learning system
A machine learning system is a software system that uses models trained on data, together with associated data, computation, and operational components, to perform tasks.
Machine Learning System Design
A software system that incorporates machine learning models together with the data, infrastructure, and operational components required to produce behavior in an application or production context.
- How it is used
- Frames data, modeling, evaluation, and design decisions as parts of a system serving a final goal.
- How deep it goes
- Develops a checklist-like design and evaluation process rather than a deep tutorial on individual ML algorithms.
- Examples
- Uses a constant regression baseline and recommender-system feedback loops to show why simple starting points and production evaluation matter.
- What you can do after
- Build it from scratch by checking feasibility, starting with an operable baseline, and evaluating results against the system’s goal.
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Delivering Machine Learning Projects: From design to deployment
A machine learning system is a deployed software system whose behavior depends on one or more machine learning models and their surrounding data and infrastructure.
- How it is used
- Frames delivery as building and operating a system around models, data, business processes, and stakeholder requirements.
- How deep it goes
- Details production decisions, readiness checks, and ownership practices without descending into implementation code.
- Examples
- Uses a retailer’s sales and inventory systems and a production-responsibility inventory to ground delivery and ownership decisions.
- What you can do after
- Practice/perform production planning, release checks, and assignment of continuing system responsibilities.
Distributed Machine Learning Patterns
A software system that collects data, trains or serves machine learning models, and supports the surrounding operational workflow.
- How it is used
- Handles growing data, computation, prediction traffic, and operational complexity through a coordinated distributed pipeline.
- How deep it goes
- Builds from component roles through complete implementation of ingestion, training, serving, and workflow composition.
- Examples
- Uses Fashion-MNIST, TensorFlow, Python, Kubernetes, Kubeflow, Docker, Argo Workflows, replicated servers, and an end-to-end architecture.
- What you can do after
- Build an end-to-end distributed pipeline that ingests data, trains models, serves predictions, and coordinates operational steps.
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Machine Learning Platform Engineering
A software system that incorporates machine learning models and the data, computation, and operational components required to deliver their outputs.
- How it is used
- Structures the transition from ML experimentation to deployable, monitored, and maintainable production services.
- How deep it goes
- Connects platforms, pipelines, training, validation, serving, and monitoring while deferring implementation details to specialized workflows.
- Examples
- Uses an ID card detection system, YOLO object detection, and a movie recommendation system to illustrate varied production workflows.
- What you can do after
- Build it from scratch by assembling an ML platform, reproducible pipelines, validated models, serving infrastructure, and monitoring.
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Related concepts
- batchingused by · Batching is the grouping of multiple data items, requests, or operations so they can be processed together as a unit.
- data assetuses · A data asset is a collection of data maintained or controlled as a resource for analysis, model development, or system operation.
- data sourceuses · A data source is an origin, system, repository, or process from which data is obtained for use or processing.
- fraud detectionincludes · Fraud detection is the process of identifying transactions, activities, or other events suspected of involving intentional deception or unauthorized gain.
- demand forecastused by · A demand forecast is an estimate or prediction of the quantity of a product, service, or resource that will be needed during a specified future period.
- data ingestionhas part · Data ingestion is the process of acquiring or receiving data from one or more sources and bringing it into a system for storage or processing.
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