MLOps Services

Ordinary software behaves the same until someone changes the code. A model also depends on data, and data keeps changing. Without MLOps services, teams meet the same problems repeatedly:

• Nobody can reproduce the model that is running, because the data, code and settings were not recorded.
• Deployment is a manual handoff that takes weeks and breaks easily.
• Performance falls and the business learns about it from complaints.
• Retraining depends on one person who remembers the steps.
• Auditors or customers ask how a decision was made, and there is no trail.

MLOps is not a product to buy. It is a set of practices and tooling that can be introduced gradually.

The Google Cloud guide on MLOps: continuous delivery and automation pipelines in machine learning (opens in a new tab) describes levels of automation, from manual processes to automated pipelines for training and delivery. It is a useful way to decide how far to go.

Match the level to the number of models and the cost of failure. A single low-risk model rarely needs a full platform, so start with versioning and monitoring.

• Data versioning and validation. Record which data trained each model, and check new data for missing values, schema changes and odd ranges before training.
• Experiment tracking and a model registry. Tools such as those in the MLflow documentation (opens in a new tab) log parameters, metrics and artifacts, and store approved versions with their stage and lineage.
• Automated pipelines. Repeatable steps for features, training, evaluation and packaging, run by CI/CD, see CI/CD implementation.
• Serving. Real-time endpoints, batch jobs or edge packaging, with load, latency and cost considered.
• Feature consistency. The same transformations used in training and in production, to avoid training-serving skew.
• Infrastructure as code. Environments created from versioned definitions, see infrastructure as code.

Framework docs such as the TensorFlow guide (opens in a new tab) and scikit-learn documentation (opens in a new tab) cover model packaging for their ecosystems, and cloud providers offer managed equivalents.

Monitoring a model has two layers. The first is ordinary service health: uptime, latency, errors and cost. The second is model health, which needs its own signals.

• Data drift. The inputs now look different from the training data, for instance a new customer segment or a changed form.
• Concept drift. The relationship between inputs and outcomes has changed, so past patterns no longer predict well.
• Outcome tracking. Where true results arrive later, such as a payment default, compare predictions to reality and record the delay.
• Proxy signals. When truth is slow, watch prediction distributions, confidence and human override rates.
• Segment checks. Look at results by region, product or customer group, since averages hide local failures.

Retraining can be scheduled, triggered by drift, or done when enough new labeled data arrives. Whichever you choose, a new model should earn its place.

1. Train on a recorded data snapshot with a recorded code version.
2. Evaluate against a fixed held-out set and against the current production model.
3. Check fairness and segment results, not only the overall score.
4. Release gradually, for example to a share of traffic or in shadow mode first.
5. Keep the previous model ready so rollback is quick.

Large language model applications add evaluation sets, prompt versions and retrieval content to this list. See LLM fine-tuning and RAG development for how those are tested.

Governance is what makes models defensible to customers, auditors and your own board. MLOps tooling produces the evidence automatically, if it is set up for that.

• Lineage and audit trail. Data, code, parameters, approver and release time for every model version.
• Risk management. The NIST AI Risk Management Framework (opens in a new tab) describes governing, mapping, measuring and managing AI risks across the lifecycle.
• Access control. Limit who can change data, approve a model or deploy it, and separate environments.
• Privacy. Minimize personal data in training sets, record legal basis and retention, and handle deletion requests. See the ICO guidance on AI and data protection (opens in a new tab).
• Supply chain. Scan dependencies, pin versions, and review third-party and open models before use.
• Human oversight. Define which decisions require human review and how overrides are recorded.

Cloud providers publish related principles, such as Google’s responsible AI practices (opens in a new tab), which can inform an internal policy.

Illustrative scenarios, not Kramate results.

• Retail. A demand forecast is retrained as seasons and promotions change, with a rollback if a release performs worse.
• Lending and insurance. Risk models carry full lineage and approval records, and monitoring checks results across customer segments.
• Manufacturing. Predictive maintenance models on edge devices report health back so retraining uses fresh sensor data.
• Healthcare operations. A no-show prediction model is monitored for drift after a clinic changes its booking process.

• USA. Sector regulators and state laws shape expectations for model risk and privacy. Financial institutions commonly follow supervisory model risk management guidance.
• UK. UK GDPR applies to personal data in training and inference, with ICO guidance on accountability for AI.
• Australia. The Privacy Act 1988 and the Australian Privacy Principles apply, with additional prudential expectations in regulated sectors.
• Singapore. The Personal Data Protection Act applies, and national AI governance guidance is available to organizations.

If you serve EU customers, check the EU AI Act (opens in a new tab) for high-risk system duties on documentation and monitoring. Confirm with legal counsel. For data residency, choose hosting regions for training data and model artifacts deliberately.

• Number of models, their update frequency and their business criticality.
• Current state: existing notebooks, data quality and documentation.
• Tooling choice: open-source components, managed cloud services or a mix.
• Compute for training and serving, including accelerators.
• Monitoring depth and the speed of ground-truth feedback.
• Governance and audit requirements.

Start by reproducing and monitoring your most valuable model, then automate pipelines, then standardize across teams. Fixed-price suits a defined pipeline build, a dedicated team suits a growing platform, and hourly work suits audits and reviews.

1. Reviews your current models before proposing a platform.
2. Right-sizes the solution to your maturity and avoids unneeded complexity.
3. Builds on open standards so you are not locked in.
4. Includes monitoring and response plans, not only deployment.
5. Produces governance evidence as a by-product of the pipeline.
6. Trains your team and hands over code, runbooks and ownership.

Kramate pairs you with a dedicated project manager and shares daily updates on Slack or Teams. Each two-week round ends with a demo, such as a pipeline run or a monitoring view. We work under an NDA from the first detailed discussion, use encrypted devices and least-privilege access, review code in pairs, and transfer all intellectual property to you. See our delivery approach.

• MLOps: Continuous delivery and automation pipelines in machine learning (opens in a new tab)Google Cloud
• MLflow documentation (opens in a new tab)MLflow
• TensorFlow Guide (opens in a new tab)Google
• scikit-learn documentation (opens in a new tab)scikit-learn
• AI Risk Management Framework (opens in a new tab)NIST
• Guidance on AI and data protection (opens in a new tab)ICO UK
• Google AI Responsible AI practices (opens in a new tab)Google
• EU AI Act (opens in a new tab)Future of Life Institute