At the AWS Innovate Online Conference 2018, Craig Stires, Head of Analytics, AI, and Big Data at AWS, shared that Machine Learning and AI aren’t new. They have been around for a while and thanks to recent technological developments, are now accessible to every developer and data scientist in all types of enterprises. Machine Learning/Artificial Intelligence (ML/AI) and Big Data are dominating in all computer sciences and new technology conferences today. It’s not that these are magical black boxes to solve every problem, but they do play big roles in supporting humans in solving business problems. So where can machine learning be applied and how does this technology matter to your business now?
What is Machine Learning?
Back in the 90s when computers helped solve automation problems, software developers programmed, and machines did exactly what they were programmed to do; good and bad, they took all. Humans made mistakes and their programing was not perfect. They needed the machine to learn from its experience to improve performance and avoid future mistakes. The core concept of machine learning is learning from experience, or in other words, it’s learning from data the human collects and provides.
Machine learning is a method in which a set of statistical algorithms are used to find predictable patterns in the provided data. What the machine learns is only from the dataset and it cannot learn from any knowledge outside of that. Therefore, the more data provided, the better prediction machines can produce.
Along with the development of cloud and computation technologies, machine learning is the trending method to solve data-rich, predictive problems. Machine learning could be applied when it meets these necessary conditions:
- Problems that require prediction, classification, high computation, and are sufficiently independent from outside the problem’s domain.
- Relevant and context-based data is available
- Adjustments to find the best predictions after experiments are available.
A good example is using machine learning to predict the likelihood that a specific group of users would click on a particular kind of ad link. A bad example might be to predict the next year’s sales from the past data when an important new competitor just entered the market this year. In this example, the problem’s key prediction indicator is not from the past data, it is from the new domain; therefore, a new feature of the dataset should be collected. So, if the problem is not dependent on its domain of knowledge, machine learning would lead to poor prediction.
In short, machine learning can help to solve complex problems using high-computation technology to find the patterns and predict the results, which no human brain can do.
Most Popular Machine Learning Business Use Cases
Since technology began contributing to the way people communicate and solve computation problems, businesses started collecting data. Nowadays, data are collected more than ever from various data sources, at huge volume and at an incredible velocity. Machine learning and data mining allow businesses to form highly accurate predictions for customer incentives or marketing offers to focused customers. Technology can help sales and marketing teams to define not only customer segmentation, but also recommend customer value, and provide churn prediction. For example, looking at the pattern of customer behavior from a group of customers during a promotional period, and the past behavior of the other customers, machine learning can help to identify the possibility that a customer will become a long-term customer. Hence the customer service department can intervene to increase the customer base.
Given historical sales of all the retail stores of the company, one business problem is to predict the sales and revenue for each store. Machine learning can solve this problem with high-computation technology to provide the forecast of each store’s sales, as well as the patterns of cross-store sales. The power of machine learning is in its connection with the vast information from big data to increase the accuracy in sales and revenue prediction.
Recommendation is a popular business problem that machine learning works well for. For example, in Amazon retail, machines can recommend products based on purchase history. Netflix uses the dataset of users’ watched movie history to recommend movies or genres to particular users. In this case, the more data, the more accuracy is improved. Big data analytics and machine learning play key roles in this area.
Machine learning can identify outliers and false alarms to ward off future issues in development operations and other financial analyses. These predictive algorithms identify anomalies within data from machine logs on mobile apps or websites. The data analytics or data science team wouldn’t need to set thresholds for abnormalities by themselves. Since people’s sense of outliers and abnormalities is limited, it is hard to define good thresholds which do not flag too much or too little. Machine learning can help in those cases. And then, after the noise of data is eliminated in preprocessing, the value of data in predictive tasks is increased.
One of the biggest contributions machine learning has made to the customer service field. By analyzing each request or each feedback, people can help to predict the main issues from customers in social media, emails, or customer service chat box. These days, we can predict why customers give up on a product or service, and customer service actions can be triggered beforehand to save those customers. In addition, machine learning can help to improve the customer service request process by classifying and routing the requests to the right place to reduce the processing time and improve the quality of service.
The above use cases are a few ways that ML/AI is changing the game for business. You can see machine learning in many more scenarios, such as identifying investment opportunities in stock markets, recommending good trading times, finding and preventing fraud, or increasing predictive maintenance and reduced cost, etc. The key note here is that machines can learn from experience, and the more data, the better solutions they can provide.
We are living in the era of Big Data and many problems are solvable with ML/AI. While helping customers to solve their business problems using the latest AWS services in machine learning and AI, we see the truth in Craig’s statement; ML technology is becoming accessible to all developers and data scientists, and ML tools and services can be useful for every enterprise.
If you are interested in learning more about how 1Strategy can help apply Machine Learning and optimize the Machine Learning Services on AWS, please contact us for more information at info@1Strategy.com.