MLOps
Keep trained models reproducible after the first deployment.
A model file alone does not explain which data produced it, why it was released or when it should change. We build the lifecycle around trained models so your team can reproduce a result, review a replacement and recover from a poor release.
DIRECT ANSWER
MLOps organizes the lifecycle of trained machine-learning models: dataset and artifact versions, repeatable training, release checks, serving, monitoring and controlled retraining. It makes model changes inspectable and operationally owned.
Best for
- Teams with models that are deployed manually or cannot be reproduced
- Products that need a controlled path from new training data to a model release
Consider another approach
- Building the first predictive model before feasibility is established
- Evaluating prompts and retrieval in an existing LLM application
Choose one model path to make repeatable
We start with a representative model and map its source data, transformations, training environment, artifacts and deployment target. Hidden notebook steps and manual file copies become explicit dependencies. If the problem is still whether a useful model can be trained, begin with Machine Learning before investing in lifecycle infrastructure.
Record enough to reproduce a candidate
The workflow records dataset references, feature definitions, code revisions, parameters and evaluation outputs. It should be possible to connect a deployed artifact to the evidence behind its approval. Storage and access policies account for licensed or sensitive training data. We reuse your existing engineering tools where they meet the requirement.
Promote candidates through an evidence gate
A new model is evaluated against the accepted baseline and relevant segments before promotion. Deployment may use staged traffic, shadow predictions or a bounded batch, depending on the application. We define rollback prerequisites, compatible feature versions and who may approve a release. General repository and pipeline security remain part of DevSecOps.
Separate changed inputs from worse decisions
Data drift indicates changed inputs, not automatically reduced business performance. We define useful signals, availability of ground truth and the action each alert requires. Retraining can run on a schedule or trigger, but publication still follows the agreed evaluation policy. Alerts that nobody can interpret or act upon are removed from the design.
Illustrative acceptance: reject a weaker replacement
A candidate improves the overall metric but performs worse for an important customer segment. An example release gate blocks promotion and preserves the current artifact. The team can reproduce the comparison and inspect its dataset references. A separate rollback rehearsal verifies that serving can return to the prior compatible model and feature version.
Leave an operating routine your team can follow
The handover includes pipelines, artifact and dataset conventions, dashboards, release criteria and incident runbooks. Infrastructure, monitoring hours and response responsibilities are agreed explicitly. This engagement establishes model lifecycle tooling; any ongoing Managed Cloud Operations arrangement has its own service boundary and contract.
Questions before you start
Not necessarily. A reproducible job, artifact store and clear release record can be enough for a small model portfolio. We add orchestration or a registry when the workflow needs it.
It can, if data access and evaluation support it. Training a candidate and approving it for production are separate actions, with the release policy agreed in advance.
Yes, after checking code, data permissions and artifact access. Missing training history is documented; it cannot be reconstructed by assuming the deployed file contains it.
There is overlap in deployment and monitoring. Prompt versions, retrieval quality and generated-answer evaluations have their own scope under AI Evaluation & LLMOps; this page focuses on trained-model and dataset lifecycles.
Review your model lifecycle
Describe the models in use, how they are trained and deployed, and the part of the lifecycle that is difficult to repeat. We agree access to a representative path.