Data & AI
MLOps Services
Get your machine learning models into production and keep them accurate there, with automated pipelines, monitoring and retraining, set up by a founder-led team and led by a senior engineer.
Talk to the foundersWhat you get with Authect's MLOps services
MLOps (machine learning operations) applies the discipline of DevOps to machine learning. It brings data science, data engineering and software delivery together, so models move from experiment to production quickly and keep performing once they're there.
We shorten the path from a promising model to a reliable service by automating the steps your team now does by hand: preparing data, training, testing, deploying and checking results. Fewer manual steps mean fewer mistakes and much less waiting between an idea and a release.
Because we combine DevOps and data science in one small team, your models can scale under real workloads on infrastructure sized for what they actually need. Every setup is led by a senior engineer, and the pipelines, registries and cloud accounts are yours from day one.
Service benefits
How MLOps benefits your business
- 01
Shorter model development cycles
CI/CD pipelines for code, data and models remove the hand-offs that slow teams down, so your data scientists can experiment quickly and ship improvements in days instead of months.
- 02
Reliable performance at scale
Automated validation checks every new model before it replaces the old one, and the serving infrastructure scales with demand, so performance stays consistent as usage grows.
- 03
Better return on machine learning
Models that reach production and stay accurate are the ones that pay off. MLOps closes the gap between a good experiment and a measurable business result.
- 04
Lower infrastructure costs
Right-sized training jobs, autoscaling inference and clear cost tracking per model mean you pay for the compute you use, not for idle GPUs.
- 05
Less technical debt
Versioned data, reproducible training and tested pipelines replace fragile scripts and one-off notebooks, so models are easier to maintain and hand over.
- 06
Governance you can show
Every model in production has a record of the data, code and parameters that produced it, which makes audits, reviews and rollbacks straightforward.
How we deliver
How we deliver end-to-end MLOps
Whether you have one model in a notebook or a portfolio already in production, we work in four stages, each with a clear outcome you can see.
Infrastructure analysis
We review your current infrastructure, data sources, models and team workflow alongside your business goals, then agree with you where automation will make the biggest difference first.
ML architecture design
Pipelines in production
Monitoring and optimisation
Ready to get your machine learning models working reliably in production?
Contact usWhat's included
What's included in our MLOps process
MLOps covers the whole life of a model, not just deployment. We work across three phases, and you can start with whichever one is holding you back today.
Each phase ends with something concrete: an agreed list of use cases, a tested model, or a pipeline that runs on its own.
Design
Requirements engineering
Turning business goals into measurable requirements for accuracy, speed, cost and explainability.
Use case prioritisation
Ranking candidate use cases by value and feasibility, so the first model in production is one that matters.
Data availability check
Confirming the data a model needs exists, is good enough and can be accessed reliably and legally.
Model development
Data engineering
Repeatable pipelines that collect, clean and version the data each model trains and predicts on.
Model engineering
Training code that is tested, reproducible and tracked, so any result can be recreated later.
Testing and validation
Checks on accuracy, fairness and edge cases against held-out data before a model is approved.
Operations
Model deployment
Serving models as APIs, batch jobs or embedded components, with versioning and safe roll-outs.
CI/CD pipelines
Automated build, test and release of code, data and models whenever something changes.
Monitoring and triggers
Alerts on drift and performance, and automatic retraining when agreed thresholds are crossed.
MLOps levels
The three levels of MLOps maturity
Most teams start at the first level, and that's fine. We help you move up only as far as your models need, because full automation is worth it for some models and overkill for others.
Why Authect
Why choose Authect as your MLOps partner
- 01
DevOps and data science in one team
Our engineers understand both the models and the infrastructure they run on, so nothing gets lost between the data scientists and the people who deploy their work.
- 02
Support across the whole lifecycle
From data preparation and model building to deployment and monitoring, we can take on any part of the machine learning lifecycle, or all of it.
- 03
Led by a senior engineer
A senior engineer designs and leads every MLOps setup, with the founders involved from the first conversation. You talk to the people building it, never a salesperson.
- 04
No lock-in
Pipelines, model registries and infrastructure as code live in your accounts from day one, built on widely used open tools your team can run without us.
Built in
What we put in place around your models
Whatever level you're aiming for, these are the building blocks we set up and document, so your models are reproducible, observable and safe to change.
Experiment tracking
Every training run recorded with its data, code, parameters and results, so the best model is easy to find and reproduce.
Model registry
One place for approved model versions, with stages, owners and a clear history of what is live.
Data versioning
Snapshots of training data linked to each model, so you can always answer which data a prediction came from.
Feature pipelines
Shared, tested feature logic used for both training and live predictions, avoiding subtle mismatches.
Automated testing
Checks on data quality, model accuracy and API behaviour on every change, before anything reaches users.
Safe roll-outs
Shadow, canary and A/B releases that compare a new model with the old one before it takes over.
Drift and performance monitoring
Dashboards and alerts on input drift, accuracy, latency and errors, so problems surface before they cost you.
Cost visibility
Training and inference costs tracked per model, with autoscaling that keeps spending in line with use.
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FAQs
MLOps FAQs
What is MLOps?
MLOps (machine learning operations) is a set of practices that brings the discipline of DevOps to machine learning. It combines data engineering, data science and software delivery so models can be trained, tested, deployed, monitored and retrained reliably and repeatably, instead of by hand.
How is MLOps different from DevOps?
DevOps automates building, testing and releasing code. MLOps does the same for code, data and models together, and adds what's unique to machine learning: tracking experiments, versioning training data, validating model accuracy and watching for drift once a model is live.
Do we need MLOps if we only have one or two models?
Often a light version is worth it. Even a single model in production benefits from reproducible training, automated tests and basic monitoring. We help you put in place what your models need today, and add more automation only when it starts to pay off.
Which tools do you use for MLOps?
We work with widely used, well-supported tools such as MLflow, Airflow, Docker and Kubernetes, alongside the machine learning services on AWS, Microsoft Azure and Google Cloud. Where you already have tools in place, we build on them.