Define data and evaluation, then build the training, deployment and change-validation process
Connect data, code and model versions in a reproducible training and evaluation path. Define release criteria, performance monitoring, model replacement and recovery.
Before listing features, we define the inputs, decisions and next actions in a real working situation.
An illustrative scenario. Specific processing rules are agreed for the project.
A new model is compared with the current version.
Deploy the verified version with a path to restore the previous one
When a model needs to move from demonstration into a working service
Specific features, integrations and operating support are agreed in the project proposal
See the scope, role and supplied interfaces for each project
Original planning & project documents
Supplied original images
Details for technical reviewers. Architecture and acceptance criteria are agreed for the actual environment and project scope.
Register trained artifacts with evaluation evidence and route approved versions to serving. Monitoring feeds reevaluation and retraining, with distinct paths for promoting a candidate and restoring a previous version.
Concrete design decisions and acceptance scenarios to agree for the project.
ImplementationData, code, environment, parameter lineage
ValidationTrace an artifact to its evaluation conditions
ImplementationSlice quality, serving contract, resource budget
ValidationReject a candidate, stop a canary, restore an alias
ImplementationDrift, observed quality, label delay, approval
ValidationKeep alerts separate from automatic promotion
Technology choices and acceptance criteria depend on discovery, integrations, and agreed scope. We define reproducible acceptance scenarios and handover evidence for the operating environment.
From visible features to failure conditions,
we work through the decisions before launch.
Link data snapshots, feature transformations, code commits, environment images, and hyperparameters through a run ID. Retain models and evaluation artifacts and validate schema, missingness, ranges, and distributions before training. Record library and hardware effects; a fixed random seed alone does not establish reproducibility.
Use a registry such as MLflow to associate versions with input signatures, examples, and evaluation evidence. Update operational aliases such as champion only after review. Release gates cover relevant data slices, latency, memory, and policy constraints; producing a model artifact must not automatically replace a production endpoint.
Align feature construction and input schemas across training, batch scoring, and online serving. Distinguish shadow evaluation from a canary that affects limited production traffic, with separate stop and rollback criteria. Specify compatibility between models and feature schemas, timeouts, and fallback behavior when a prediction is unavailable.
Observe input drift, missingness, prediction shifts, and performance from delayed labels separately. Retraining requires sufficient data, cost controls, evaluation, and approval; a worse candidate can be recorded without promotion. Use lineage and run history to investigate whether data, features, code, or deployment caused a regression.
Understand your goals, workflows, and operating environment.
Define features, priorities, and integration requirements.
Build the agreed scope and review progress together.
Prepare testing, deployment, and operational handover.
Tell us what you want to build or improve
Our team will review your request and reply by email
