Machine Learning Operations

Operate reliable AI solutions with MLOps

Bringing your machine learning and AI solutions into production in a sustainable and scalable way comes with a wide range of challenges, we help you overcome them. With a strong focus on engineering, processes, and culture, our MLOps approach ensures that your ML initiatives deliver long-term value and operate reliably over time.

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By adopting MLOps, you unlock real business value from your AI initiatives.

Developing a machine learning model is one thing. Operating it in a reliable, scalable, and maintainable manner in a production environment is an entirely different challenge. In many organizations, models are created by data scientists and handed over to IT infrastructure teams without further education or instruction. This often leads to friction, errors, data drifts, and models gradually becoming outdated without anyone noticing.

Our MLOps services follow a structured approach to address these challenges. It defines how models are built, tested, deployed, monitored, and updated, through repeatable and automated processes.

Our MLOps Service Offering

Infrastructure Setup

We establish the technical environment in which models are trained, versioned, and deployed. This can include cloud-based platforms as well as on-premises infrastructure.

Pipeline Automation

We design automated workflows that prepare data, train and evaluate models, and deploy them to production, without requiring manual intervention for each iteration.

Monitoring

We implement systems that continuously verify whether a model is still performing as expected—for example, by detecting changes in input data (data drift) or identifying declines in prediction quality.

Retraining Processes

We automate the decision process leading to when and how a model should be retrained once its performance begins to decline.

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Discover our approaches to building, operating, and integrating machine learning systems and LLMs.

ML lifecycle and MLOps

Guide your machine learning models through their entire lifecycle, from initial concept and development to continuous improvement in production. With our MLOps offering, we help you design and implement robust pipelines for data ingestion, model training, validation, and deployment, ensuring repeatable and reliable outcomes.

Our engineers handle versioning, monitoring, and automated retraining, optimizing performance, resource usage, and scalability. From observability and automated scaling to governance and operational integration, we manage every stage of your ML models so they deliver consistent, production-ready results.

LLMOps for AI with Large Language Models

Large language models require modern operational approaches. With our LLMOps offering, we integrate these models efficiently and at scale into your existing infrastructure and operational processes.

Our engineers handle model deployment, versioning, and monitoring, ensuring reliability, low latency, and cost-effective performance. We design robust pipelines for prompt management, chaining, and fine-tuning, while implementing safeguards compliance. From observability and automated scaling to security and governance, we manage the full lifecycle of your LLMs so they deliver consistent, production-ready results.

Compliance and the EU AI Act

Deploying scalable AI solutions requires secure and compliant processes. With our compliance offering, we provide structured frameworks, process templates, and platforms that help you navigate strict regulatory requirements, such as the EU AI Act, while continuing to drive innovation.

Our experts support you in establishing governance, risk management, and audit-ready workflows, ensuring that AI models are transparent, accountable, and aligned with legal obligations. From policy implementation and reporting to operational monitoring, we help you maintain regulatory compliance without compromising agility or performance.

By leveraging MLOps, you gain:

Accelerated project ROI

Significantly shorten the time from development to production, enabling faster return on investment.

Reliable and scalable systems

Reduce downtime and ensure your AI systems are equipped to meet the complex demands of daily business operations.

Enhanced control and compliance

Respond proactively to regulatory requirements, such as the EU AI Act, and turn compliance into a competitive advantage.

Increased team productivity

Standardized processes and improved collaboration reduce manual work and unstable environments, allowing data science teams to focus on delivering models and shipping solutions faster.

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Efficiency, stability, and scalability across the entire ML lifecycle

MLOps unifies technical processes, collaboration, and governance within a single framework, reliably transitioning AI and machine learning projects into production.

Automated pipelines with clearly defined responsibilities create a stable and scalable lifecycle, shorten time-to-market, reduce operational risks, and ensure quality and regulatory compliance—through continuous monitoring and version control—such as adherence to the EU AI Act. This approach delivers sustainable business value from AI models.

ti&m combines Swissness, technical excellence, and human insight to successfully implement MLOps in your organization

Extensive experience

Technical expertise with a collaborative approach

With hands-on experience in AI, data science, and engineering, we understand the critical success factors for complex MLOps projects. Our team combines deep technical knowledge with an understanding of human and operational challenges.

Swissness

Reliability, precision, and tailored solutions

As a Swiss company, we stand for quality, precision, and customized solutions. Our technology delivers reliability and long-term stability.

Collaboration at eye level

A true partnership with your team

We work closely with you, from strategy to production, to develop sustainable solutions that integrate people, processes, and technology.

Head of AI & Digital Solutions

Lisa Kondratieva

Get in touch and start scaling your AI and ML initiatives today.