Production
A production environment is the live operational setting in which software or other systems serve real users, workloads, or business processes.
Build Python Web Apps with Streamlit
A deployment environment is an isolated live or pre-live instance of an application with its own resources and configuration.
- How it is used
- Moves a locally developed Streamlit app into a deployed environment for its intended users.
- How deep it goes
- Explains dependency compatibility and walks through Elastic Beanstalk environment creation and redeployment, without detailing runtime internals.
- Examples
- Uses a to-do app’s requests version mismatch and a named Elastic Beanstalk production environment.
- What you can do after
- Operate/configure it with the EB CLI to create a production environment and deploy later application versions.
Read how Build Python Web Apps with Streamlit explains production →
Building Web APIs with ASP.NET Core
A production environment is a runtime deployment context in which an application serves real users or operational workloads.
- How it is used
- Deploys the board-game web API for real requests while separating runtime configuration, credentials, and exposed development tooling.
- How deep it goes
- Explains database preparation, configuration, publishing, endpoint checks, and security verification, but does not detail the entire deployment infrastructure.
- Examples
- Uses appsettings.Production.json, the deployed website, /error/test, /cod/test, and a nonce-based Content-Security-Policy example.
- What you can do after
- Operate/configure a published API with separate database credentials, production settings, direct endpoint tests, and tightened browser security.
Read how Building Web APIs with ASP.NET Core explains production →
LLMs in Production
An operational environment in which a software system or machine-learning model is live and available to perform real work.
- How it is used
- Turns an LLM from a trained artifact into a live service that performs real work for users.
- How deep it goes
- Explains deployment, testing, optimization, and proof-of-concept trade-offs but stops short of detailed infrastructure implementation.
- Examples
- Uses a Raspberry Pi, llama.cpp server, OpenAI-compatible client, quantized model, and customer-facing production tests.
- What you can do after
- Operate/configure a rapid LLM proof of concept, expose it through an API, and identify infrastructure needs before scaling.
Deep Learning with Python, Third Edition
- How it is used
- Deploys trained models to serve predictions in an application's inference environment.
- How deep it goes
- Explains export, serving options, and optimization, but stops short of detailed operational procedures.
- Examples
- Shows TensorFlow SavedModel and ONNX export-and-load snippets, plus mobile and embedded deployment contexts.
- What you can do after
- Apply it via Keras export() to save TensorFlow SavedModel or ONNX artifacts for inference.
Read how Deep Learning with Python, Third Edition explains production →
Bootstrapping Microservices, Second Edition: With Docker, Kubernetes, GitHub Actions, and Terraform
A production environment is a deployment environment that hosts an application for access by real customers or users.
- How it is used
- Hosts an application for real customers and provides the target for deployments, feedback, and operational workflows.
- How deep it goes
- Explains dependency selection, startup conventions, deployment, environment separation, automation, and blue-green switching; it stops short of detailed infrastructure implementation.
- Examples
- Uses Node.js installation commands, npm start, Kubernetes clusters, Terraform variables, and blue-green DNS switching.
- What you can do after
- Operate/configure production by selecting runtime dependencies, starting a Node.js service, deploying to Kubernetes, and switching environments safely.
Machine Learning Platform Engineering
The live execution environment in which a software system or machine-learning model serves real users or business workloads.
- How it is used
- Frames production as the operational phase that turns validated models into monitored services delivering business value.
- How deep it goes
- Explains automated pipelines, deployment targets, optimization, staging, monitoring, lineage, and rollback without detailing service implementation internals.
- Examples
- Uses REST APIs, edge devices, staging comparisons, optimized deployment artifacts, performance thresholds, and rollback as operational examples.
- What you can do after
- Operate/configure a production ML workflow that deploys, monitors, versions, evaluates, and rolls back models across environments.
Read how Machine Learning Platform Engineering explains production →
Experimentation for Engineers
An operational system serving real users or business processes rather than an offline or test environment.
- How it is used
- Anchors experimental optimization in systems serving real users and business processes.
- How deep it goes
- Distinguishes offline evaluation from online measurement on live systems, stopping short of implementation internals.
- Examples
- Uses search servers, data transformation timing, response latency, and bounce rate as operational examples.
- What you can do after
- Evaluate changes by screening them offline, measuring business metrics in production, and retaining improvements.
Read how Experimentation for Engineers explains production →
Snowflake Data Engineering
A production environment is the environment in which an approved system runs for its intended users and operational workloads.
- How it is used
- Runs approved data pipelines on live data and serves required structures to downstream consumers.
- How deep it goes
- Defines production responsibilities and environment progression, but stops short of detailed deployment commands or configuration procedures.
- Examples
- Uses a bakery pipeline ingesting operational data and JSON orders, with testing based on representative volumes, integrity, and governance needs.
- What you can do after
- Operate pipelines for live workloads while separating development, testing, acceptance, and release activities.
Related concepts
- staging environmentcontrasts with · A staging environment is a pre-production computing environment used to validate software or operational changes under conditions that approximate production.
- Kubernetesuses · Kubernetes is an open-source platform for orchestrating and automating the deployment, scaling, and management of containerized workloads across computing clusters.
- execution environmentcontrasts with · An execution environment is the computing context, including hardware, software, configuration, and supporting resources, in which programs run.
- environmentkind of · An environment is the external context, including its entities and conditions, in which a system, process, or agent exists or operates.
- test environmentcontrasts with · A test environment is a configured computing context, including relevant software, infrastructure, data, and settings, in which software is tested.
- continuous deploymentused by · Continuous deployment is a software delivery practice in which validated changes are automatically released to a target environment, typically production, without manual approval.
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.