AI Engineering & MLOps
Build machine-learning models that solve a defined business problem, and run them reliably in production with the monitoring, retraining and controls they need.
Is this for you?
You might need this if…
Data scientists have built promising models, but they live in notebooks and never reach production.
Models in production have quietly lost accuracy, and nobody noticed until the business complained.
You want to forecast demand, predict equipment failures or inspect quality automatically, but lack an in-house team.
AI use is growing across departments, each with its own tools, and costs and risks are hard to oversee.
What we deliver
What it covers
Machine-learning model development
We frame the business problem, prepare the data and build models for classification, prediction or ranking, validated against a clear baseline. You get a documented model that solves a defined problem and is ready to deploy.
Computer vision and image analysis
We build models that inspect products, read gauges, count items or detect defects and safety risks from cameras and images. They can run in the cloud or on edge devices close to the production line.
Forecasting, predictive maintenance and optimisation
We build models that forecast demand, predict equipment failures and optimise planning, routing or stock levels. Planners get decisions backed by data instead of spreadsheets and gut feel.
MLOps and LLMOps platforms
We set up the pipelines, model registry, deployment and monitoring that take models and language-model applications from development to production. Releases become repeatable, traceable and fast.
AI at scale
We run and maintain your AI solutions over time: monitoring performance and cost, retraining models and handling incidents. Shared platforms and standards let new use cases build on what already exists.
AI evaluation, testing and quality assurance
We test models and AI applications for accuracy, robustness, bias and security, including prompt injection for language models. You get evidence that the AI works as intended, which also supports EU AI Act documentation.
Our approach
How we work
01
Frame
We define the business problem, the success metric and the baseline, and check what data is available.
02
Build
Models developed and validated iteratively, with experiments tracked and results shared with your team.
03
Deploy
Automated pipelines take the model to production in the cloud, on-premises or at the edge.
04
Operate
Monitoring, retraining and incident handling keep the model accurate and its costs under control.
Best practices
What we bring to every engagement
Beat a simple baseline first
A model earns its place only if it clearly outperforms a business rule or a simple statistical method.
Version everything
Data, code, models and prompts are versioned so every result can be reproduced and audited.
Monitor drift, not just uptime
We track input data and prediction quality, because a model can be running and still be wrong.
Automate the path to production
CI/CD for models removes manual hand-overs and makes retraining routine.
Run where the data lives
Models are deployed in a public cloud, your own data centre or at the edge, depending on data sensitivity and latency.
Track cost per prediction
GPU and inference costs are monitored and optimised from the start, not after the first invoice.
Outcomes
What you get
- Production models with documented performance
- Automated pipelines from training to deployment
- A model registry with versioning and an audit trail
- Monitoring dashboards for drift, quality and cost
- Test and evaluation reports, including bias and robustness
- A runbook for retraining and incident handling
AI-powered
Unleash the power of AI
We offer the possibility of using AI throughout this work: ready-to-use AI tools, or a customised version built for your organisation that can run inside your own infrastructure. In AI engineering, AI coding assistants speed up data preparation, feature engineering and test writing, while automated evaluation compares every new model version against the last. Whichever option you choose, training data and models can stay inside your own environment.
Starter offer
MLOps Readiness Check
A fixed-scope, four-week review of your models, pipelines and platform that shows what it takes to run AI reliably in production and where to start.
Week 1
Inventory
Review of models, data pipelines, tools and how models are deployed and monitored today.
Week 2
Assess
Assessment of reproducibility, deployment, monitoring, security and cost against sound MLOps practice.
Week 3
Design
Target platform and process design, including deployment options in the cloud or on your own infrastructure.
Week 4
Plan
A prioritised improvement plan presented, with a first model selected to bring into production.
You receive
- A maturity assessment of your machine-learning lifecycle
- A list of risks in models currently in production
- A target MLOps architecture
- A prioritised improvement plan with quick wins
FAQ
Frequently asked questions
How long does it take to get a model into production?
For a well-defined problem with available data, a first production model typically takes two to four months. Setting up an MLOps platform alongside it usually adds a few weeks, but it makes every later model faster to deliver.
Which platforms do you work with?
We work with the major cloud platforms such as Azure, AWS and Google Cloud, with Databricks, and with open-source tools such as MLflow and Kubernetes, as well as on-premises infrastructure. Where it makes sense, we build on what you already have.
Can models run on our own servers?
Yes. Models can be trained and served in your own data centre, a private cloud or on edge devices, so sensitive data never leaves your environment. We help you size the hardware and compare it with cloud options.
Can you take over models someone else built?
Yes. We review the models and code, document them, add monitoring and bring them under a managed process. Altechy stays your single point of contact, with ML engineers and data scientists from our partner network as needed.
Related services
Generative & Agentic AI Data & Analytics DevOps & Platform Engineering IoT & Connected Systems AIOps & Observability Cloud Operations & FinOps
Let’s get your models into production
Book a free 60-minute idea session. We explore your challenges and opportunities with you, and suggest where to start — with no obligation.