MLOps and LLMOps
Release and improve AI applications with a defined process.
A successful experiment leaves practical questions about how models, data and configurations will work together in everyday use. We develop processes to deploy, evaluate, monitor and change your AI application. Model quality, technical reliability and costs are assessed together, giving your team a documented basis for releases and ongoing operations.
Your options
Services that move your project forward
Map the lifecycle and responsibilities
We review data, models, prompts, the application and existing deployment. Ownership of development, approval and operations is explicitly assigned.
All important components are given a defined path through the life cycle.
Keep versions and artifacts traceable
Models or model references, dataset versions, prompts, configurations and test results are versioned appropriately. Their relationship to each release is recorded.
A result can be traced to the components and versions used.
Integrate evaluation into development
Representative test cases and business quality criteria are combined with automated checks. The model's task, data and intended use determine appropriate evaluation methods.
Changes can be assessed on the basis of comparable results.
Design deployment and release
Environments, deployment, approvals and fallback procedures are integrated into your development process. Experience with Git, CI/CD, Helm and Argo CD supports controlled releases.
Releases become repeatable and traceable.
Monitor quality, runtime and consumption
Technical errors, response times, resource usage and suitable quality indicators are monitored. Subject specialists assess changes to input data and results where required.
Relevant changes become visible and follow a defined investigation process.
Establish operations and ongoing improvement
Runbooks, responsibilities, model changes and regular evaluations are agreed. Prometheus, Grafana and other suitable tools from the SYNEDAT PLATFORM can support the operational overview.
Your team can maintain and improve the deployed version systematically.
Where to start
MLOps and LLMOps Use cases
Three example situations show how we can help.
An AI prototype needs to enter regular operations
We add versioning, release controls, monitoring and the necessary operating documentation.
Model or prompt changes have unexpected consequences
Comparable test cases and documented artifacts make differences assessable before release.
Technically successful requests increasingly produce poor answers
We combine technical signals with suitable quality assessments and a clear investigation process.
From requirements to results
A clear process with agreed milestones
Review the current setup and operational goals
Components, teams, risks and existing development processes are recorded.
Design versioning and evaluation
Artifacts, criteria, test sets, and responsibilities are determined.
Integrate deployment and monitoring
A defined release path is implemented with tests, signals and fallback procedures.
Test and hand over procedures
Release, error handling and change scenarios are tested and documented.
Your benefit
What you receive
- A coordinated lifecycle for the agreed models and applications.
- Versioned artifacts and comprehensible evaluation results.
- An implemented release process with approvals and tested fallback procedures.
- Appropriate monitoring, operating instructions and responsibilities.
Ways to work with us
Choose a starting point that fits your needs. We agree the scope and required effort in a tailored proposal.
Assess readiness for AI operations
For orientation: lifecycle, versioning, evaluation and operational tasks.
Request a quote: Assess readiness for AI operationsImplement release and evaluation processes
For a defined scope: artifacts, checks, deployment and monitoring.
Request a quote: Implement release and evaluation processesImprove AI operations
For ongoing changes: additional models, better evaluation and agreed support.
Request a quote: Improve AI operationsSYNEDAT PLATFORM
Platform experience for your project
We use these selected tools in SYNEDAT PLATFORM or its delivery processes. We adapt suitable practices to your project and align their integration with your existing systems.
Identities, secrets and policies
Keycloak · OpenBao · External Secrets · Kyverno
Sign-in, technical secrets and platform policies serve different purposes. We connect them with roles, limited permissions and documented exceptions. The selected tools form part of a common access and operating model.
Controlled access and more consistent platform policies.
Observability and operations
Prometheus · Grafana · Alloy · Loki · Tempo
Metrics, logs and traces provide different views of applications and platforms. We organize data sources, dashboards and alert paths around specific operating questions. Retention, sensitive data and costs are considered when planning data collection.
Better incident diagnosis and informed operating decisions.
Data, search and messaging
MySQL · Redis · RabbitMQ · Solr · OpenSearch
Data storage, caching, messaging and search have different consistency, access and recovery requirements. We select and connect these components around your business processes. Ownership and maintenance are part of the integration.
Technical components that fit your data and application processes.
Questions before you get started
What is the difference between MLOps and LLMOps?
MLOps organizes the lifecycle of machine learning models and their applications. In LLM applications, prompts, context sources, external model versions and special evaluation procedures are often added. The processes are tailored to the components actually used.
Do we need a new platform?
Not always. Existing repositories, pipelines, registries and monitoring can already cover important requirements. We identify gaps and add appropriate components. New products are considered where specific needs justify them.
Which components should be versioned?
Relevant models or model references, code, prompts, configurations, test cases and dataset versions should be traceable to a release. The implementation depends on the product and data storage arrangements. Sensitive data does not automatically belong in a code repository.
How should we evaluate LLM results?
A representative test set combines typical tasks, edge cases and business quality criteria. Automated checks can be supplemented by human evaluation. A model used as an evaluator must also be assessed before it can serve as a reliable reference.
Does monitoring detect every decline in quality?
Technical signals reveal issues such as errors, response times and resource usage. Content quality needs suitable criteria, sampling or evaluated outcomes. We define what monitoring covers and which additional checks are necessary.
Can a model change be made without retesting?
A change can change behavior, costs and results. Therefore, suitable comparative tests and approvals are provided. In the case of external services, it is checked which versioning and change control is actually available.
How does rollback to a previous version work?
The application, configuration and related artifacts need to be considered together. Data changes or external model availability can limit rollback options. We therefore design and test a suitable fallback procedure for the specific environment.
What costs are incurred in ongoing IT operations?
Among other things, model calls or computing capacity, storage, evaluation, data processing and support are relevant. The measurement should cover the entire use case. Infrastructure consumption and ongoing services are shown separately in the offer.
Discuss your next step
Which AI application needs a reliable path into production?
Describe your current status and the most important quality issues. We define the next steps for controlled releases and traceable results.
MLOps and LLMOps
Your next step
Tell us what you need. We will route your enquiry to the right team and discuss the next steps with you.
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