Here's what we do everyday, our bread-and-butter
Putting ML models into production
Making it easier to get ML models into production repeatably
Creating streamlined and consistent ways for Data Scientists to create new ML models
Enabling performance monitoring of ML models in production at scale
Building automated re-training frameworks for production ML models
Enabling Cloud Partner Solutions
Training and upskilling
Machine learning engineering at Mantel Group
ML Engineering (also known as AI Engineering, ML Eng) is the practice of ensuring Machine Learning models integrate successfully into the real world. It combines the capability of Software Engineering, DevOps, Machine Learning, and Cloud Engineering.
At Mantel Group, our ML Engineers work closely with Data Scientists to ensure that the characteristics of a model during development are maintained as it is deployed and scaled.
Our ML Engineers also work closely with Data Engineers and Cloud Engineers to ensure that your platforms and processes support the unique requirements of machine learning software and enable Data Scientists to do what they do best.
What we can help you with
ML Engineering covers two core sub-domains, ML Operations (MLOps), which includes everything that’s required to ensure a model becomes and remains operational, and Technical ML or Environment Optimisation, which involves deep diving into specific environments to ensure models are performing optimally.
We also have a range of other services that support your operation throughout the AI development lifecycle.
Our ML Engineers specialise in ML Ops, a term which encompasses the additional components required to put ML models into operation.
An ML model alone forms only part of the primary inference pipeline, there is additional work required to integrate source and destination systems, and to ensure the models operate securely, consistently, and understandably.
Beyond inference, we design and build the components required to monitor data drift and model performance, track experiments, version models, and, where appropriate, deploy automatically.
We take a holistic approach to MLOps, engaging your team and updating processes to ensure these components are part of your ML journey from day dot, and not a last minute blow out to your budget – or showstopper to getting your model into production.
Our ML Engineering team brings a wealth of experience across a variety of common (-and more exotic) tech stacks. We are able to dive deep into your production code, identifying and analysing quick wins to improve the latency and throughput of your models.
Depending on the environment, implementing effective caching, minimising in-memory copying, or overhauling algorithms to use more efficient database queries are just some of the approaches we apply.
Our deep expertise with ML models and runtimes uniquely positions us to optimise your model within its environment.
Enabling Cloud Partner Solutions
We work closely with the major cloud providers, leveraging cloud platforms and products to create industry and function-specific insights and intelligence for businesses.
Cloud providers can support a wide range of ML initiatives, from leveraging common deployment patterns and pipelines, to bespoke containerised CI/CD pipelines and platforms.
We harness the latest cloud solutions to accelerate your AI time-to-market.
Upskilling and training
We work closely with your in-house engineers to ensure your team is comfortable with taking over the solution.
Where necessary, we also provide custom training and knowledge sharing sessions to set the team up for success.
”"We were able to half the number of images sent to manual review"Stuart NicholsChief Data Officer, IDP Education