Machine Learning and AI applications are everywhere. Recently Andrew Ng launched drive.ai – self-driving cars. He aptly said: “The future is here”. But I must say this future arrived pretty quickly!
After talking to our customers and over the period of time we found that there are many challenges before we apply machine learning or AI algorithms. Many of our customers want to see results quickly. We take them through methodical steps to avoid surprises.
Here are few challenges to applying machine learning to solve business problems.
1. Do we know what problems to solve? This is the first question you should ask, and the biggest one. My customers want to train deep learning model on the cloud but when my team asks them deeper questions about what they want to solve, they do not have answers. Or even if they do have answers they are not very clear.
This is where our team’s expertise comes in. We ask questions to our customers to help them understand what problems they want to solve. Questions range from asking about business objectives, what is the current problem, what results are they expecting, what is their vision etc.
2. What data do we have? Machine learning or AI algorithms rely on data. In order to predict future, you need to know past behaviors. In order to know past behavior, you need historic data. In most cases, you have data available. But the question is: is the data relevant? Is it cleaned? If you want to predict a customer’s next purchase you need to have customers historic transactions, demographics details. Another question is, is having data enough?
3. Do you have labeled data? For you to apply ML classification techniques you need to have labeled data. For example, we were working with one of our customers to automatically generate marketing headlines using Deep Learning models, in this problem we need a lot of marketing articles with “good” and “bad” headlines so ML engine knows what is good and bad.
Similarly, in a classic problem of tweet sentiment analysis, you need to label a tweet as positive or negative before ML engine can predict a new tweet’s sentiment.
Who will tag the headlines as “good” or “bad”? Who will put tweet’s sentiment as positive or negative?
4. Do you have trained people? This was the biggest challenge before but now no more. There are many online courses available where one can learn basics and advanced materials. One has to push their limits and learn new materials. I train my team through these courses that are available. Some of them are free while some of them cost as little as $10.
The challenges remain the same customer to customer. They just take different shapes and sizes. We take our customers through methodical steps in solving their business problems. We make hypotheses, test them iteratively, present findings and outcomes, and proceed to next milestone.
It’s always good to take baby steps in Machine Learning and AI Problems.
Please get in touch with us for your data analytics and data science needs.