Scaling AI means 4S: Scale, Speed, Scope and Sustainability.

This week, we spotlight on “SCOPE”.

Scaling AI means the pervasive use of AI by different teams, departments and for different use cases.

EvoML enables both tech and business users to generate accurate & efficient AI models instantly. With a high degree of explainability, people at all levels can trust AI as a decision-making partner to take optimal actions.

With EvoML:

• Data Scientists can build complex models faster with more flexibility and transparency
• Business Analysts can supercharge business insights through an end-to-end, code-free AI process
• Software Engineers can build production-ready AI…

Scaling AI means 4S: Scale, Speed, Scope and Sustainability.

This week, we spotlight on Vol.2 SPEED.

Scaling AI requires organisations to build, run and adapt AI at speed.

With EvoML, businesses can:

1. Automatically build hundreds of models in parallel, in hours or days
2. Optimise models to run faster with our #CodeOptimisation feature
3. Quickly adapt to the evolving AI infrastructure, without pivoting previous effort

Further Reading:

Scaling AI: What It Really Means? Vol.1 Scale (Infographic)

Why AutoML Is Not Enough for Scaling AI

About TurinTech:

TurinTech is a leader in Evolutionary AI Optimisation. …

Scaling AI means 4S: Scale, Speed, Scope and Sustainability.

This week, we spotlight on Vol.1 SCALE.

There are difficult trade-offs between AI accuracy and efficiency. Only AI that runs fast and efficiently uses hardware can truly scale in the real world.

TurinTech’s Multi-objective Optimisation feature enables businesses to tackle difficult trade-offs between accuracy and efficiency, rolling out AI to various clouds and devices at scale.

About TurinTech:

TurinTech is a leader in Evolutionary AI Optimisation. We are a research-driven deep tech company founded in 2018 based in London.

We envision an intelligent and efficient business world powered by scalable AI. We automate…

1. Introduction:

The practice of building AI is rapidly becoming automated. Automated Machine Learning (AutoML) is the process of automating the time consuming, iterative tasks of machine learning model development. However, AutoML on its own cannot bring AI to life in the real business world. In this article, we will give an overview of AutoML’s limitations, and explain how EvoML overcomes these limitations to make AI scalable.

2. What is AutoML?

Integrating AI into your business activities is a long-term process which requires specialised talent, a considerable amount of effort and investments. It requires well-sought-after data science talents who mainly tech giants can afford. Even in…

Introduction

Artificial Intelligence (AI) is an iterative optimisation process to achieve multiple objectives.

In a real business world with resources constraints, there are many trade-offs to tackle before deploying AI in business processes. These include accuracy, model complexity, explainability, running speed, cost, etc.

Let’s simplify Optimal AI as the formula below:
F is the optimal AI model; X is a specific use case. In each AI use case X, there is an ideal target value for each objective. For example, when developing an AI model running on a smart watch to predict heart attack, it may require:

1) Y 1 Accuracy=…

Introduction

AI is a game changer, but it will take time, scale and expertise to unlock its full potential and ROI.

Companies strategically scaling AI generate 5X ROI VS Companies unable to scale. 86% of executives believe they won’t achieve their growth objectives unless they can scale their AI. -Accenture

However, not every company understands the challenges of creating a first-class Scalable AI organisation. Today, it’s mainly the Tech Giants (Google, Amazon, etc) that are able to scale AI and reap the benefits. Whilst some large corporations are starting to see the results of their AI efforts to embed AI into…

In our last blog, we briefly introduced statistical modelling (SM), which is used by organisations to transform data into business insights before machine learning (ML) comes into the picture. Continuing our ML history blog series, this second article will shade some light on the topic of SM; most precisely how it differentiates itself (if at all) from ML and how can businesses decide which one is better suited to cater for their needs. Both SM and ML are based on statistics. We will start talking about their relation to statistics respectively, and then compare their differences.

Here is the article…

DS in the Real World

To gain a competitive edge today, companies have infused Machine Learning (ML) to automate ever-increasing streams of data, enable data-driven decision making and drive real-time business value.

Though ML was first introduced in 1950s, you may be surprised to know ML only took off in the business world in the last decade. If you want to better understand ML and how your businesses can capitalise on ML, look no further! 👀 In this article we will present an overview of the past, present, and future of machine learning in businesses:

1. What is ML?
2. Before ML coming into picture: statistical modelling
3. …