---
title: "MLOps & Model Lifecycle Management"
canonical: https://wavect.io/services/mlops/
language: en
description: "MLOps for versioned datasets, reproducible training, model releases and drift monitoring. Wavect connects the lifecycle to your engineering workflow."
image: "https://wavect.io/img/general/bak/open_graph_preview.jpg"
---

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.

THE ENGAGEMENT

## Trace the model from data to deployment.

1. **Reconstruct the path** Map data, features, experiments, artifacts and release decisions.
2. **Automate the controls** Repeatable jobs, evaluation gates and versioned deployments.
3. **Assign the response** Monitoring, rollback and retraining decisions with named owners.

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](/services/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](/services/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](/services/managed-cloud-operations/) arrangement has its own service boundary and contract.

## Questions before you start

### Do we need a large MLOps platform?

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.

### Will retraining happen automatically?

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.

### Can you work with models built by another team?

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.

### Is this the same as LLMOps?

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.

## Continue with the right scope

- [Build and evaluate a predictive model](/services/machine-learning/)
- [Evaluate LLM applications](/services/ai-evaluation-llmops/)
- [Secure the delivery pipeline](/services/devsecops/)

## Structured Data

```json
{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@id": "https://wavect.io/#organization",
      "@type": [
        "Organization",
        "ProfessionalService",
        "LocalBusiness"
      ],
      "employee": [
        {
          "@id": "https://wavect.io/team/kevin-riedl/#person",
          "@type": "Person",
          "jobTitle": "Managing Director",
          "name": "Kevin Riedl",
          "url": "https://wavect.io/team/kevin-riedl/",
          "worksFor": {
            "@id": "https://wavect.io/#organization",
            "@type": [
              "Organization",
              "ProfessionalService",
              "LocalBusiness"
            ]
          }
        },
        {
          "@id": "https://wavect.io/team/christof-jori/#person",
          "@type": "Person",
          "jobTitle": "Managing Director",
          "name": "Christof Jori",
          "url": "https://wavect.io/team/christof-jori/",
          "worksFor": {
            "@id": "https://wavect.io/#organization",
            "@type": [
              "Organization",
              "ProfessionalService",
              "LocalBusiness"
            ]
          }
        }
      ],
      "founder": [
        {
          "@id": "https://wavect.io/team/kevin-riedl/#person",
          "@type": "Person",
          "jobTitle": "Managing Director",
          "name": "Kevin Riedl",
          "url": "https://wavect.io/team/kevin-riedl/",
          "worksFor": {
            "@id": "https://wavect.io/#organization",
            "@type": [
              "Organization",
              "ProfessionalService",
              "LocalBusiness"
            ]
          }
        },
        {
          "@id": "https://wavect.io/team/christof-jori/#person",
          "@type": "Person",
          "jobTitle": "Managing Director",
          "name": "Christof Jori",
          "url": "https://wavect.io/team/christof-jori/",
          "worksFor": {
            "@id": "https://wavect.io/#organization",
            "@type": [
              "Organization",
              "ProfessionalService",
              "LocalBusiness"
            ]
          }
        }
      ],
      "legalRepresentative": [
        {
          "@id": "https://wavect.io/team/kevin-riedl/#person",
          "@type": "Person",
          "jobTitle": "Managing Director",
          "name": "Kevin Riedl",
          "url": "https://wavect.io/team/kevin-riedl/",
          "worksFor": {
            "@id": "https://wavect.io/#organization",
            "@type": [
              "Organization",
              "ProfessionalService",
              "LocalBusiness"
            ]
          }
        },
        {
          "@id": "https://wavect.io/team/christof-jori/#person",
          "@type": "Person",
          "jobTitle": "Managing Director",
          "name": "Christof Jori",
          "url": "https://wavect.io/team/christof-jori/",
          "worksFor": {
            "@id": "https://wavect.io/#organization",
            "@type": [
              "Organization",
              "ProfessionalService",
              "LocalBusiness"
            ]
          }
        }
      ],
      "name": "Wavect GmbH",
      "subjectOf": {
        "@id": "https://wavect.io/verified-claims.json#dataset",
        "@type": "Dataset",
        "creator": {
          "@id": "https://wavect.io/#organization",
          "@type": [
            "Organization",
            "ProfessionalService",
            "LocalBusiness"
          ]
        },
        "description": "A machine-readable registry of quantitative and qualitative claims published by Wavect, with review dates, localized page appearances and public third-party citations where available.",
        "inLanguage": "en",
        "isAccessibleForFree": true,
        "license": "https://creativecommons.org/licenses/by/4.0/",
        "name": "Wavect verified publication claims",
        "url": "https://wavect.io/verified-claims.json"
      },
      "url": "https://wavect.io/"
    },
    {
      "@id": "https://wavect.io/team/kevin-riedl/#person",
      "@type": "Person",
      "jobTitle": "Managing Director",
      "name": "Kevin Riedl",
      "sameAs": [
        "https://www.wikidata.org/wiki/Q139796365",
        "https://www.linkedin.com/in/wsdt",
        "https://github.com/wsdt"
      ],
      "url": "https://wavect.io/team/kevin-riedl/",
      "worksFor": {
        "@id": "https://wavect.io/#organization",
        "@type": [
          "Organization",
          "ProfessionalService",
          "LocalBusiness"
        ]
      }
    },
    {
      "@id": "https://wavect.io/team/christof-jori/#person",
      "@type": "Person",
      "jobTitle": "Managing Director",
      "name": "Christof Jori",
      "sameAs": [
        "https://www.wikidata.org/wiki/Q139796367",
        "https://www.linkedin.com/in/jocr77/",
        "https://github.com/jo-chris"
      ],
      "url": "https://wavect.io/team/christof-jori/",
      "worksFor": {
        "@id": "https://wavect.io/#organization",
        "@type": [
          "Organization",
          "ProfessionalService",
          "LocalBusiness"
        ]
      }
    },
    {
      "@id": "https://wavect.io/#website",
      "@type": "WebSite",
      "inLanguage": [
        "en",
        "de",
        "es",
        "zh"
      ],
      "name": "Wavect",
      "potentialAction": {
        "@type": "SearchAction",
        "query-input": "required name=search_term_string",
        "target": {
          "@type": "EntryPoint",
          "urlTemplate": "https://wavect.io/search/?q={search_term_string}"
        }
      },
      "publisher": {
        "@id": "https://wavect.io/#organization",
        "@type": [
          "Organization",
          "ProfessionalService",
          "LocalBusiness"
        ]
      },
      "url": "https://wavect.io/"
    }
  ]
}
```

```json
{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@id": "https://wavect.io/services/mlops/#service",
      "@type": "Service",
      "areaServed": [
        {
          "@type": "Country",
          "name": "Austria"
        },
        {
          "@type": "Place",
          "name": "Worldwide"
        }
      ],
      "category": "Artificial Intelligence",
      "dateModified": "2026-10-31",
      "description": "MLOps for versioned datasets, reproducible training, model releases and drift monitoring. Wavect connects the lifecycle to your engineering workflow.",
      "name": "MLOps & Model Lifecycle Management",
      "provider": {
        "@id": "https://wavect.io/#organization",
        "@type": [
          "Organization",
          "ProfessionalService",
          "LocalBusiness"
        ]
      },
      "serviceType": "MLOps",
      "url": "https://wavect.io/services/mlops/"
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "item": "https://wavect.io/",
          "name": "Home",
          "position": 1
        },
        {
          "@type": "ListItem",
          "item": "https://wavect.io/services/overview/",
          "name": "Services",
          "position": 2
        },
        {
          "@type": "ListItem",
          "item": "https://wavect.io/services/overview/#ai",
          "name": "Artificial Intelligence",
          "position": 3
        },
        {
          "@type": "ListItem",
          "item": "https://wavect.io/services/mlops/",
          "name": "MLOps",
          "position": 4
        }
      ]
    },
    {
      "@id": "https://wavect.io/services/mlops/#webpage",
      "@type": "WebPage",
      "dateModified": "2026-10-31",
      "description": "MLOps for versioned datasets, reproducible training, model releases and drift monitoring. Wavect connects the lifecycle to your engineering workflow.",
      "inLanguage": "en",
      "isPartOf": {
        "@id": "https://wavect.io/#website",
        "@type": "WebSite"
      },
      "mainEntity": {
        "@id": "https://wavect.io/services/mlops/#service"
      },
      "name": "MLOps & Model Lifecycle Management",
      "url": "https://wavect.io/services/mlops/"
    }
  ]
}
```

```json
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "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."
      },
      "name": "Do we need a large MLOps platform?"
    },
    {
      "@type": "Question",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "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."
      },
      "name": "Will retraining happen automatically?"
    },
    {
      "@type": "Question",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "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."
      },
      "name": "Can you work with models built by another team?"
    },
    {
      "@type": "Question",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "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."
      },
      "name": "Is this the same as LLMOps?"
    }
  ],
  "speakable": {
    "@type": "SpeakableSpecification",
    "cssSelector": [
      ".faq-question",
      ".faq-answer"
    ]
  }
}
```