By Keith Moore and Marla Rosner
AI, and more specifically, machine learning, is currently the most talked about technology. With all of the benefits machine learning can offer, it makes sense for companies to want to start implementing a machine learning project themselves. But how?
Successfully deploying machine learning solutions is easier said than done. SparkCognition is here to help, though—having deployed a large number of successful projects, we’d like to share some tips and common pitfalls so that your machine learning pilot can go as smoothly as possible.
1. Choose the correct application of AI
As cool as AI sounds, you shouldn’t blindly apply it when a simple solution will suffice. What AI excels at is finding subtle patterns in data sets of all shapes and sizes, particularly under complex or changing conditions. But there’s plenty of scenarios where a standard rules-based approach will work just as well. AI is great for analyzing things like complex long-term degradation in wind turbines, but you don’t necessarily need it to tell you that a temperature reading on a pump is spiking.
Make sure your desired outcome will provide substantial business value, as well. You’ll want to carefully define what it is you’re looking for. What, specifically, are your desired outcomes? What would you consider “success,” and how will it be measured? How will success impact system users, customers, employees, company reputation, and your bottom line? What will the end output look like, and who should the end user be? Make sure you have clear answers to these types of questions before you begin, and once you’ve started, measure your results. If you’ve chosen an appropriate project, you should be able to see significant returns.
You’ll also want to make sure you’re using the appropriate sensors and data integration platform. With advanced algorithms, a proper machine learning company can often achieve results with fewer sensors than you might think. On the other hand, a good machine learning company can also help you find a system integrator if you need more sensors.
2. Get an appropriate level of engagement from your team
You need to get buy-in from within your company from the bottom up. Make sure everyone understands what you’re trying to accomplish with AI—those who don’t won’t perform as well. You’ll want to have good lines of communication with your AI deployment team, as well. Poor communication is the number one factor that slows down implementation projects.
Go through all the questions mentioned above with your team. You’re not the only one who needs a clear vision on the business benefits, or what the end results (and end users) should look like. Anyone who’s collaborating on a machine learning project in any capacity should have a clear understanding of exactly what you’re doing and why.
3. Start with high-quality data
Low-quality data impacts the performance of a machine learning model. High quality data should have a consistent level of time granularity, sensor resolution, and reading accuracy. With these three things, the data can be trusted for model building. It also needs to be data in which you can reasonably expect to find a pattern. Patterns are not always obvious, of course; that’s why you use AI in the first place. If there’s any kind of pattern, AI will find it—but there has to be something there to find.
This is just the tip of the iceberg, but these are three key steps you should to take to ensure success on any machine learning project, regardless of the type or goal. Machine learning isn’t a perfect panacea; it has specific prerequisites, and like any major business undertaking, it can only be as successful as the team that works on it. But if you have a clear goal in mind, get your team engaged, and make sure you have the data you need, machine learning can be a powerful tool for your company.
