The appetite for AI & ML is just growing. It won’t be long before machine learning infiltrates every application in every industry. VCs already have a hard time taking software pitches that don't include ML/AI as an underpinning seriously. AI and machine learning will soon be recognized as enabling technologies that make every application more capable and smarter.
In today’s market, you might be looking to deploy ML within your business applications either to press your organization's competitive edge or to improve your bottom line. Either way, most companies that have successfully introduced machine learning within their organization recognized the importance of the practicing the below points.
They put strategy first. If C-level executives see ML as a tool to craft and implement a strategic vision, they will exploit it best. Machine learning might end up buried within a company’s routine operations unless you keep strategy as a starting point. Although it will prove useful still, its long-term value might get limited to a never-ending addition of “cookie cutter” apps like models for stimulating, acquiring and retaining customers.
A strong data strategy starts by analyzing gaps in the data. Then figuring out the money and time needed to fill the gaps, and break down silos. Various teams hoard and politicize access to information too often. This is one reason why companies have brought in a chief data officer who can tie together everything that's required. Also, it's a good idea to put the onus of generating data on frontline managers.
In the ML domain, where data is critical to success, competing with giants like Facebook, Google or Amazon in areas where they have deep data will prove foolish. And also good luck raising venture capital to do so. So where do you stand a shot? Focus on two key elements. Firstly, data which is mainly hidden from the big players. Work with data that doesn’t exist within the domains of the tech behemoths, and you'll be targeting use cases or verticals where they don’t have experience, access or focus.
But the clincher here is, we all depend on getting access to data from users. Which is why the second component of success for ML/AI software companies are win-the-market strategies. Adopt cost-effective, scalable win-the-market strategies. A go-to-market strategy won't get you where you need to be in this market. Delivering customer success is as important as selling. For this, your company needs to target an acute pain point. When you're dealing with various challenges and being sold software solutions every other day, you’re bound to initially focus on the most critical concern and the potential fix to that issue. Further, it’s a lot easier to access a budget that already exists to address a particular issue than to create a budget for something that seems new. And make sure your software doesn’t demand a change in user behavior. Consumers will prove reluctant to change their habits. And that’s even truer in a business context when several people or teams may be involved in a specific workflow or process.
Win-the-market is so crucial in the context of ML/AI software businesses as customer retention and scale yield even more data. And if this is the kind of data that the Giants don’t have, you’ve got yourself a virtuous cycle that leads to long-term success and differentiation.
Often, it's not the learning, but logistics that makes the difference. That is where you should be spending your resources and time. Adjusting the algorithm will give you a slight edge. But working on that data, the GUI or graphical user interface, and how you're engaging with and listening to your users could easily make a vast difference. Putting in time and effort to tweak the algorithm will benefit the businesses far less than listening to your users. This has nothing to do with your algorithm, and everything to do with how you are getting your data in the first place.
Making sure you're feeding them the right data is a big part of making sure the ML algorithms are conveying valuable insights. People tend to get ego-bound to certain algorithms, but nowadays, with the tools out there, so many are coming up with all sorts of new algorithms. Focus on the data rather than endlessly tweaking your algorithms, because the data will give you far more lift. If you're working on problems that require extreme precision, that's different. But in most cases, you'll benefit far more from changing the question and adjusting what data you're getting.
You can use many of the ML tools available today, for free. There are the open-source popular framework libraries like H20, TensorFlow, Torch, Shogun, and ML libraries in many ASF or Apache Software Foundation projects like Singa, Mahout, and Spark. There are also subscription-based options such as BigML, Amazon Machine Learning, and Microsoft Azure Machine Learning Studio. Microsoft also has a free Cognitive Toolkit. There are countless resources available. Stay away from anybody who tries to convince you that a certain tool is the only one you'll ever need. Build your system in an agnostic fashion. You need to measure, then evaluate, and vacillate between different tools, and your infrastructure needs to be welcoming to that.