- February 2, 2023
Table of Contents
The opportunities offered by ML (Machine Learning) were first used by Internet giants processing data from social media and online retail. Soon they were followed by hardware companies, who used it to optimise their chips, memory, and storage.
The recent pandemic forced many businesses around the world to set off on their digital transformation journey or to accelerate what they were already doing. The result is that today many services are delivered almost entirely online via automated or semi-automated processes. What does it all mean for you? It means it’s time to embrace ML and use it to your advantage! Let’s look at how to do it!
When you decide to start your ML journey, there are two ways you can go: you can either buy a ready tool or develop one. Both options carry some risk, both mean costs.
The cheapest way to go about introducing ML to your business is to buy a ready solution.
Even better is to find one on GitHub – there is an abundance of them there! But when using something that already exists, you need to take into consideration that it may not always do exactly what you are after, and it may not be the best option for your organisation.
The second way is to create the ML solution from scratch.
Such an approach means your solution will be tailored to your needs, it will respond to the problems you have, and it will be specific to your organisation. A definite downside here is cost.
Good news is there is a way in the middle, a combination of the two already described. You can find and buy a half-finished product and you can develop it so that it responds to your needs and works exactly as you want it to work. It’s a solution that is being used more and more often – a very pragmatic and cost-effective one.
Now that we’ve established the ways of getting your ML solution, let’s look at what you really need to succeed with your ML strategy. Here is our list:
It may sound obvious, but to start with, you need to know what problem you want to solve, and you need to be sure solving it will give you a proper value. What’s more, you need to check that the problem is solvable using ML.
Data lies at the heart of all organisations and at the centre of all IT projects they undertake. What counts is its quality and quantity, but also the relevance to the problem you want to solve using ML. Having access to high volumes of data does not necessarily mean it will be valuable for you: weather related data alone is not good enough for problems related to industrial processes, even if the weather influences them directly.
Data collection should start even before the beginning of your ML project: given the always increasing data volumes, it’s worth thinking about your data strategy as soon as possible and assess your current data landscape before you set off on any ML journey.
If you are outsourcing your ML engineers, they will definitely need support from within your organisation: they will need to speak to domain experts who understand the problem they are working on, who are able to explain it properly, and who will be able to evaluate the results provided.
No matter what stage of your ML strategy you are at, Future Processing can help! Let’s look at what we can do for you:
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ABOUT THE AUTHORS
Matt Stuart is a Data & Insights Practice Lead at embracent. He helps their clients set the right strategies, solutions and operating models to drive value from data assets.
Tomasz Gandor, MScEng, is a senior software developer and machine learning practitioner. He’s currently pursuing a PhD with the Polish-Japanese Acedemy of Information Technology, while working on Data Solutions projects by day.
His interests range from computer vision, object detection, and image processing to medical risk assessment and database optimization. From time to time he offers consulting about ML throughout the Company or conducts technical recruitment interviews.
