Data Engineering, Applied AI & MLOps Specialists
Ops 4 ML
Create a Value Delivery System from your Inference Project
Have your boffins come up with a breakthrough ML model? Does the model still only run from a Notebook? Has your Ops team got the skills to deploy ML?
Infer Systems will create a production data engineering and inference operational environment to wrap around your hard work. Our experience will enable your business to leverage the research that you own and want to operate, specifically:
- inference speed/resource management,
- scaling to keep outputs time relevant,
- applying CI/CD techniques to data led products,
- event/API driven systems architectures, and
- efficient integration with organisational resources.
The OPS 4 ML product will deliver exactly what you need to take one or more models into production.
ML 4 OPS
Use Inference Technology to Better Deliver your Business Outcomes
Have you got valuable raw data? Are your competitors demonstrating automation capabilities that may eat into your bottom line? Are you unsure about how to engage with a Data Science development project?
Infer Systems will analyse your data for opportunities to apply well-known battle-hardened ML models to reap increasingly sophisticated insight and emerging automation savings. Our team will use their experience to implement continuous in-line inference into the heart of your business system landscape, and in particular create: validation, feedback, self-check and safety controls to take the path of least risk and clearest return for making the most from your data.
The ML 4 OPS product will deliver a comestible data inference layer to enhance your business outcomes.
No Hype Guarantee!
A pragmatic approach to engineering learning based systems
The Infer Systems team have been delivering achievable results with Machine Learning and Artificial Intelligence in production environments since 2016. Learning and inference systems are often touted as promising high levels of accuracy and/or very cost effective operational delivery, but unfortunately most implementations never fulfil this billing. Infer Systems have a philosophy of making systems that deliver value as early and cleanly as can well engineered.
Infer Systems will endeavour to:
- deliver the simplest possible system that meets your needs
- be honest and transparent about the capabilities and limitations of the ML deployments
- stick to solutions that are available now and not rely on "nearly ready" models
- be clear about the lifespan, training flaws and data entropy issues affecting the systems
- explain, quantify and relate the benefits of AI/ML to the wider business
The best AI/ML systems start small, aim for tangible ROI and deliver results early, providing a platform for further success.
What are your Data Engineering needs?
Here is how you might get started:
- Introduce yourself and your issues by sending an email to email@example.com;
- We will then set up a free 2 hour interactive video workshop to explore the problem and solution space around your needs;
- Following that workshop we will propose a suitable working relationship and mutually agree a high level strategy to tackle your issues;
- When you are ready we will then work towards delivering a series of steps towards your required inference systems by:
- selecting a tactical objective,
- measuring current accuracy and modelling data flow,
- designing, building, and integrating the layers of solution,
- tracking success, and evaluating performance;
- And then we will stick around to train your team and provide ongoing support for as long as you need to make the most of your inference based systems.
So, do you need ML 4 OPS or OPS 4 ML?