How to Plan for Your Next Machine Learning Projects
The rise of artificial intelligence, or AI, is going to be seen as one of the major achievements of our time. While we still haven’t arrived at the grand dreams of science fiction, the strides that tech giants have already made have proved astounding. From digital assistants to the sublime weirdness of Google’s Deep Dream, we’ve made steps that would have seemed like pure fantasy even just a decade ago. One of the most exciting fields in AI is machine learning, the ability for a program to teach itself independent of direct human input. If your company is looking to harness the power of AI, it pays to know how to plan for machine learning projects while managing potential pitfalls that come along with it.
And pitfalls there are indeed. While the benefits of AI and machine learning algorithms are, perhaps, limitless, the algorithms themselves are limitlessly complex. Making sure that you dot your ‘i’s and cross your ‘t’s as you go along can mean the difference between success and failure – a poorly defined instruction or set of metrics can cost your company dearly. Creating clear, quantifiable and testable goals, along with assembling a team of developers that are as experienced as they are ready to learn, will help you break out in front of the pack.
This might all sound intimidating, but luckily there are a few principles that your company can rely on when embarking on your next Big Thing. Follow these tips and you’ll be well on your way to plan for machine learning projects effectively and designing algorithms with an eye to the future.
Doing the Groundwork
What makes machine learning truly innovative is a program’s ability to learn by itself. That said, it can only learn what we tell it to learn – it won’t self-correct on its own. This makes the initial instructions and data we feed it incredibly significant, and it pays to figure out ahead of time precisely how we want these algorithms to behave. Which is why one of the best pieces of advice for planning machine learning projects is to figure out exactly what you want it to do. It’ll save your company (and your developers) a lot of grief later on.
The crucial element here is being as concrete as possible about your goals, and then designing a set of metrics that will give you results that are relevant to your project.
Take a company that wants to design an algorithm that measures and predicts sales on its online store. They might start with the intention to compare the number of visitors with how many transactions there are. This will generate a conversion rate, which is certainly useful, but it would be far more relevant if your developers adjust the algorithm to measure where the users are coming from, how much time they spend looking at particular products, what they add or remove from their shopping cart or whether or not they explore related or different products while on the site.
Both projects are driven by the same goal (analyze customer behavior with an aim of increasing sales) but only one of them is, from a marketing point of view, much more likely to produce relevant data. A clear vision will help you communicate specific instructions that your developers can put to work from the start. It goes a long way to creating an actionable proof of concept (POC) that can later be used to assess if your algorithms are successful or not. When thinking of how to plan for machine learning projects, this is a step that cannot be ignored, and it can take as little as a few weeks to do thoroughly.
Initial Testing and Further Development
Once your company has developed actionable metrics and communicated them to the development team, the next step to properly plan for machine learning projects involves an initial testing stage. Think of it as a tentative exploration period where your POC model is built as an API and explored to see if it lives up to initial expectations. This can take up to a month or two to do comprehensively.
After this, the development stage starts in earnest. With all of your metrics and initial planning in place, your developers will be able to start working on the algorithms and subjecting them to ongoing testing. Ideally this kind of testing happens after every sprint. With everything closely being monitored, your team will be able to spot unexpected consequences (which always happen when we talk about machine learning) regularly and nip them in the bud.
Think back to the example of the online store. Perhaps the algorithm has started producing results outside of its original parameters. It might not be taking into account all of the metrics fed to it in the beginning, and on closer inspection it may have found a more accurate way of predicting customer behaviour. When you plan for machine learning projects well in advance, developments like these can then be incorporated into the official instructions for a more comprehensive set of end data.
When your company reaches the end of the algorithm’s initial development cycle, it can be tempting to think the job’s already finished. But effectively planning for machine learning projects means having an eye constantly kept on the future.
The power of a machine learning process is its ability to take in vast amounts of data and put them to work for you. But, as mentioned earlier, they can only be directed to do what developers tell them to do, and they may not be able to cope with the changing nature of data over time. As data changes, making sure that your algorithms keep up with them is an ongoing task that makes the difference from being cutting-edge or falling behind. It protects your brand just as much from irrelevance as from erosion.
This phase never really ends, as new directions for innovation open up every year. But your company can rest assured that, if you took the time to effectively plan for machine learning projects, you’ve built a foundation on which you’ll be able to confidently move ahead into an exciting future.
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