When AI is Supercharged

All Aboard the Hype Train!

Artificial intelligence (AI) has been all the rage in the tech world, with companies claiming to have the best AI-powered solutions. But let’s be real, most are just riding the AI hype train. So what makes TurinTech’s evoML different? I’m sure many are aware that some companies add blockchain and AI to their slides in order to spice things up a little when it comes to boardroom bingo (you may recall when the Long Island tea company pivoted to Blockchain technology adding 200% to their stock price).

While other companies might be powered by just one model and call it a day — does only using Logistic Regression make you an AI-driven Deep-Tech company? Probably not! — evoML takes it to the next level by using AI on top of AI. That’s right – we’ve essentially created a Frankenstein’s monster of AI, and it’s terrifyingly efficient. Okay, but what does it mean, and why should you care?

The Achilles Heel of AutoML

To understand this, let’s talk about AutoML, automated machine learning. AutoML is a process that uses machine learning algorithms to automate the selection and fine-tuning of a model for a particular problem. It’s a useful tool, but let’s face it – it’s not exactly reinventing the wheel – it has been around and evolving for many years. So you may ask “how are you different and isn’t evoML yet another AutoML company?”. The answer is “Yes and No!”. evoML goes beyond AutoML. The best way to think of evoML is to imagine an incubator that generates optimised boilerplate ML model pipelines ready for all possible customisations.

Yes, AutoML is a little “bastardized”, by data scientists wanting to retain complete control and build the models themselves (and why wouldn’t they), or perhaps by its inability to fully optimise the models, or stay flexible in meeting the practitioners’ requirements. In other words, the code produced by AutoML may have been a little poor. This is where evoML shines – it brings all the necessary fixes to optimise, package and deliver production-grade quality code and models.

Why Does evoML Succeed?

There are things we hear on our travels, “We do it in-house, we do it manually”. While there is nothing wrong with that, history shows automation beats manual labour. There is not much that is handmade anymore and certainly not for scale. Automation, according to some estimates, suggests there are 25x gains in efficiency when it comes to AI-related processes. To those companies trying to build from the ground up, in-house because of data sensitivity, we are able to deploy evoML in an isolated environment or on a cloud.

There are many great firms out there providing platforms, and even the tech giants have an offering, but do they really understand what the end user needs and do they really solve a desperate workflow problem for a sophisticated practitioner? Aren’t they more into advocating and democratising data science instead of filling the void that our current data-science practitioners need?

Tech giant solutions, while being great, are not transparent. Neither do they give you production-grade and optimised code, nor are they incentivised to provide a highly performant model – the more data they crunch, the more customers pay in consumption fees.

As data literacy evolves at a rapid rate, it is certain that more tools will be used by practitioners and those tools will help with various stages of any AI workflow.

How do we differ from the competition? We give you the code, production-grade, optimised and ready-to-go. When we say optimised code, what do we actually mean by that? The optimised AI codebase includes, among others:

  • hyperparameter-optimised models that fit to your data
  • the most efficient data structures
  • models that train fast
  • models that predict fast
  • code and model that use the least memory (if your device has constraints)
  • the least amount of code
  • less computing power (fewer carbon emissions)

With evoML, you are driving on the Autobahn while without it’s a bit like being stuck behind a tractor on a B road.

But Why Should You Care?

The band-aid approach to building technology can often unravel fast, as Southwest Airlines recently experienced. If you have scrappy code, it can accumulate over time and lead to technical debt. Technical debt is the accumulation of problems and inefficiencies in your code that can be costly and time-consuming to fix later on. By using optimized code, you can minimize your future technical debt and make sure your AI models are running as smoothly as possible.

Think of it like this: if you have a mill and you don’t maintain it properly, it will eventually break down. On the other hand, if you take the time to keep it clean and well-maintained, it will keep running smoothly. Optimised code helps to ensure that your AI models will continue to perform well over time.

But how can you quantify the effect of code optimisation? One way is through Environmental, Social, and Governance (ESG) standards. By using optimised code, you can show your commitment to sustainability and social responsibility, which is a strategic focus for investors and stakeholders. Plus, it’s like a win-win: you’re doing good for the planet and your AI models are Greta Thunberg-friendly.

Another way to quantify the benefits of code optimisation is through a new practice called “Continuous Code Optimisation” (CCO). This is similar to Continuous Integration (CI), a software engineering practice where code changes are automatically built, tested, and deployed to production. CCO involves continuously improving and optimising the code used in your AI pipelines to ensure they’re performing at their best.

For example, say you have an AI model that predicts stock ticker direction. If your code isn’t optimised, the model may lag behind, leading to poor investment decisions. On the other hand, if you continuously optimise the code, your model will be faster both in training time and predicting market movements, so your investment decisions will be better informed.

Talk To Us

evoML sets itself apart from the competition by using AI to optimise the code used in its models. This leads to better performance. Adding evoML to your team is like adding a supercharger to your engine.

Feel free to talk to us even if you are not sure about what you can do with a supercharged AI. We can direct you – the possibilities are endless!

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