Using ML Engineering To Supercharge Your AI Models!
(because all great partnerships start with a call)
You’re not alone.
Failing ML Models Are More
Common Than You Think
Machine Learning isn’t some plug-and-play solution. It’s crazy stuff and often leaves data scientists playing the roles of sorcerers with mounting pressure to produce “magically accurate ML models”. Getting this right is hard. And we see our clients struggling with 3 key challenges:
Cost factors associated with building and deploying accurate models.
Obstacles in ensuring consistency within datasets and ML model fine-tuning.
Finding the right specialists with the expertise to solve ML challenges.
Fast, Effective ML Model Deployment & Optimization
Machine Learning is still a new field. But it has the power to revolutionize your business and redefine how you compete. To get this right means implementing leading-edge ML Engineering practices to boost model performance and create intelligent AI-powered customer journeys.
This is built on 3 key requirements that we know are important to you:
ASSETS
Introducing architectures for reusable assets through ML feature stores.
SPEED
Taking ML Models from inception to production in days (not months).
FREEDOM
Creating space and capacity for Data Scientists to focus on what’s important.
The Success Model to ML models
Our ML Engineering teams build out,
improve and sharpen your ability to deploy high-accuracy ML models at scale. While working closely with your data scientists, they provide fluid approaches, specialised expertise and world-class best practices.
This is how we ensure your ML model is
productionised at scale:
Establish a suitable toolchain that supports your hyperscale environment.
Set up ML feature stores with seamless access to the relevant resources.
Architecture an effective deployment pipeline and evaluation loop.
Our ML Experts Produce Results In Record Time!
Your data scientists are under immense pressure. Your ML models don’t perform the way you had envisioned. Your big data is difficult to access. And you don’t even know where to begin. We understand your pain and we’re here to help. We give you access to the manpower and resources for highly effective model optimisation.
Now you can rest easy:
Our team of experts have extensive and specialised knowledge in ML engineering across all industries and platforms.
We have a proven track record in successfully deploying highly-effective ML models at scale.
By using world-class best practices, we implement and optimise ML technology in a fraction of the time.
The Broken ML Model Problem
(and why we’re so obsessed with fixing it)
Businesses are quickly beginning to understand the true potential of AI and the far-reaching impact of ML. The benefit? Higher revenues, rapid innovation, organisational resilience, greater retention… Believe us, the list goes on.
In fact, only 53% of models actually make it into production. That’s only for companies that have a data-driven culture and some involvement with AI. Otherwise, 87% of companies see repeated failure due to slow development in carving out a modern tech-friendly environment.
In fact, only 53% of models actually make it into production. That’s only for companies that have a data-driven culture and some involvement with AI. Otherwise, 87% of companies see repeated failure due to slow development in carving out a modern tech-friendly environment.
But why, you ask?
ML tends to fail for a number of reasons:
It’s unsupported
A lack of understanding and support from the top. Unrealistic expectations and red tape create limitations to data accessibility and progress.
A lack of understanding and support from the top. Unrealistic expectations and red tape create limitations to data accessibility and progress.
It’s dirty
Unclean and inaccessible data. Unclean and disparate data creates further bottlenecks to accurate model development.
Unclean and inaccessible data. Unclean and disparate data creates further bottlenecks to accurate model development.
It’s complicated
Model deployment and maintenance require specialised practices. ML only works as a well-oiled machine and without the necessary components, it fails.
Model deployment and maintenance require specialised practices. ML only works as a well-oiled machine and without the necessary components, it fails.
How to fix it if it’s broken
Your ability to compete in a data-dependent world relies on clean, accessible data; clearly aligned ML business goals and KPIs; and a cultural shift from top to bottom. Making it work requires support, trust, understanding and backing from leadership. Throw in new ways of working, modern project delivery approaches, accessibility through every channel and you’ll have ML doing exactly what you want it to do.
Customers Who Are Making AI Work,
Right Now!
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Download our free report that shows how you can put the right disaster recovery mechanisms set in place to protect and manage your customer journey.
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Download our free report that shows how you can put the right disaster recovery mechanisms set in place to protect and manage your customer journey.
Our Culture - A Key to Success
Peter Drucker’s comment “culture eats strategy for breakfast” is spot on.
It’s why we spend a huge amount of time working on our culture. Which is built around putting our staff first.
And yes, while the idea is attractive; articulating, growing and managing culture is far harder than any strategy process or plan. Culture is subtle, messy and made up of conflicting personal beliefs and behaviours.
Rather than break it down on an entire page, we have put together a Way of Working deck to better represent what we believe in.
It’s why we spend a huge amount of time working on our culture. Which is built around putting our staff first.
And yes, while the idea is attractive; articulating, growing and managing culture is far harder than any strategy process or plan. Culture is subtle, messy and made up of conflicting personal beliefs and behaviours.
Rather than break it down on an entire page, we have put together a Way of Working deck to better represent what we believe in.
