MLOps – AI Model Deployment and Operations
We help move AI and machine learning models from experiments into daily work, with automated deployment, monitoring and governance.
- A Vodafone Group company
- 20 years in Lithuania
- Reply within 1 business day
A Vodafone Group company, 20 years in LithuaniaWhat the MLOps service covers
Click a card to see exactly what we do.
AI foundation
Workspaces, data access and compute resources for data scientists on Azure or AWS.
Automated model pipeline
CI/CD for training, testing and deployment, from a code change to a running model.
Model and data versions
Lineage for every model, data set and result.
Monitoring
Accuracy, data drift, model decay and performance monitoring with alerts.
AI governance
Access rights, bias checks, explainability and audit documentation.
Deployment at the edge
Deploying models to IoT and edge devices when decisions must be made on site.
MLOps – AI Model Deployment and Operations
Trying out an AI model is easy. Running it so that it works reliably, gets updated and keeps its accuracy as data changes is much harder.
MLOps applies DevOps principles to AI models: version control, automated training and deployment, quality tests and continuous monitoring. We build these processes on Azure Machine Learning, Amazon SageMaker and Kubernetes.
We also take care of what is often missing: data and model lineage, drift and bias monitoring, access rights and audit documentation.
Why AI models need MLOps
Have an AI pilot you can’t get into production? Let’s assess what is missing.
From pilot to daily work
The pilot becomes a working solution instead of staying in a presentation.
Reliable results
Model quality is monitored and updated as data changes.
Traceability
You know which model made a decision, with which data and when.
How we implement MLOps
A clear process with no surprises: at every stage you know what is being done, who is responsible and when to expect results.
Assessment
Review of current models, data and team workflow
Platform setup
Workspaces, storage and access rights on Azure or AWS
Automation
CI/CD for training, testing and deployment, plus a model registry
Monitoring and governance
Quality and drift monitoring, alerts, documentation and support
Skaylink Group experience
The Skaylink Group AI team builds AI foundations, MLOps processes and AI governance frameworks, and deploys models to IoT and edge devices. On AWS the Group built a text analytics solution that processes millions of posts a day and finds trends.
In Lithuania MLOps goes together with AI transformation projects: we start with AI opportunities, and MLOps makes sure the solution keeps working.
What to know before you start
Answers to the questions DevOps and development teams ask us most often.
01How is MLOps different from DevOps?
DevOps automates building and deploying software. MLOps also manages data, model training and changes in model quality over time.
02Do we need MLOps if we only use ChatGPT or Copilot?
Usually not. MLOps matters when you build or fine-tune your own models, for example for forecasting, anomaly detection or document analysis.
03Which platforms do you use?
Azure Machine Learning, Amazon SageMaker, Kubernetes and open-source tools such as MLflow, depending on your environment.
04What is model drift?
It is when real data changes and the model starts making more mistakes. Monitoring detects it and signals that the model needs retraining.
05How does MLOps relate to AI transformation?
AI transformation helps find and implement AI solutions, and MLOps makes sure they run reliably and are maintained.
Related services
Often ordered together – one team, one contract.
Start with an AI model assessment
Tell us which AI models you build or use and what stops you from putting them into production. We will suggest where to start.
- You write to usA few sentences about your situation are enough.
- A 30-min callWe discuss your goals and current setup.
- A clear proposalYou get a plan, timeline and price.
