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Machine learning is definitely a confusing term. Is it AI or something different?
Well, its actually a subset of AI (which, by the way, is a massive category). “Machine learning is a method of analyzing data using an analytical model that is built automatically, or ‘learned’, from training data,” said Rick Negrin, who is the VP of Product Development at MemSQL. “The idea is that the model gets better as you feed it more data points.”
There are two key steps with machine learning. First, you need to collect and train the data, which can be a long and tough process. Then, you will operationalize the machine learning, such as by using it to help provide insights or as part of a product. There are a myriad of tools to help with the process, such as open source platforms like TensorFlow and commercial systems, such as DataRobot.
“Successful machine learning is only as good as the data available, which is why it needs new, updated data to provide the most accurate outputs or predictions for any given need,” said Panagiotis Angelopoulos, who is the Chief Data Officer at Persado. “And unlike what any one person can analyze, machine learning can take vast amounts of data over time and make predictions to improve the customer experience and provide real value to the end-user.”
Sometimes the models are so intricate that they are nearly impossible to understand. The lack of transparency can make it so that certain industries, like healthcare and banking, may not be able to use machine learning models. Because of this, more research is being focused on the explainability of models.
Another challenge with machine learning is the need to form an experienced team. “To build this team in-house, you will have to hire more than just data scientists,” said Ji Li, who is the director of data science at CLARA. “Full deployment of a new solution requires product managers, software engineers, data engineers, operational experts to develop process and operational workflows, staff to integrate data models into operations, people to manage onboarding and training of the employees who will ultimately use the solution, and staff who can quantify value generation.”
In other words, for many organizations, the best option with machine learning may be to buy an off-the-shelf solution. The good news is that there are many on the market—and they are generally affordable.
But regardless of what path you take, there needs to be a clear-cut business case for machine learning. It should not be used just because it is trendy. There also needs to be sufficient change management within the organization. “One of the greatest challenges in implementing machine learning and other data science initiatives is navigating institutional change—getting a buy-in, dealing with new processes, the changing job duties, and more,” said Ingo Mierswa, who is the founder and president of RapidMiner.
Then what are the use cases for machine learning? According to Alyssa Simpson Rochwerger, who is the VP of AI and the Data Evangelist at Appen: “Machine learning can solve lots of different types of problems. But it’s particularly well suited to decisions that require very simple and repetitive tasks at large scale. For example, the US Postal Service has been successfully using machine learning systems to sort the mail for decades. The task was simple: read the address on the mail (sense) and then understand the zip code (perceive) and then sort into different buckets (decide). The US Postal Service processes almost two hundred million pieces of mail per day—so sorting this by hand wouldn’t work.”
In fact, the examples are seemingly endless for machine learning. Here are just a few:
Netflix movie recommendations (note that the visuals for the thumbnails are also based on machine learning).
Logistics for ride-sharing operators like Uber and Lyft
Models to predict churn
“Machine learning is a tool and like most tools, it works best when used properly,” said Matei Zaharia, who is the chief technologist and co-founder of Databricks. “Machine learning can take something as simple as some images and some annotations or just drawings on those images and create a solution that can be automated efficiently and at scale. However, we are not in a technological state where a machine learning model can just work on anything that is thrown at it—that is, not without some kind of external guidance. A machine learns, a human teaches.”
Tom (@ttaulli) is an advisor to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction and The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems. He also has developed various online courses, such as for the Python programming language.