Machine Learning: The Why, When and How

Why machine learning?

At its simplest, machine learning (ML) uses mathematical models to analyze large volumes of data, identify patterns and make decisions. ML models can imitate human behavior to predict outcomes, such as those used for language translation, chatbot automation and predictive text. They’re also the power behind an Amazon product suggestion, autonomous vehicles and crop yield behavior predictions, to name a few.

ML’s effectiveness and varied uses make it a fast-growing branch of artificial intelligence (AI). Many of the current advances in AI involve ML, so many people use the terms interchangeably.

Perhaps the most interesting aspect of it all is that ML functions with minimal human intervention because it uses data to learn and continuously optimize outcomes from previous experiences. It uses patterns in data for prediction or classification in situations when doing so might seem too complex, too large or too time-consuming. This alone has made machine learning an indispensable tool to all types of organizations, regardless of size, sector or location.

It’s also not surprising that company leaders are becoming increasingly interested in learning how to leverage ML’s predictive abilities in their businesses. According to a 2020 Deloitte survey, 67 percent of organizations were using ML, while 97 percent were using or planning on using it in 2021. Added to this is the trend toward accumulating large quantities and varieties of data, while access to relatively cheap computational processing power and data storage have made machine learning attractive.  There are clearly opportunities for companies worldwide to exploit new or existing value or boost certain efficiencies with this technology.

Trends and challenges

As with other trends, you might think ML will automatically solve a problem. Unfortunately, it’s not that simple: Not everything you conjure up will or even should go from ideation to production.

Success with machine learning begins with data; without it, machine learning is useless. Access to a large amount of clean, integrated data is one key ingredient you’ll need to run a long-term, ML-powered business.

Another ingredient is choosing the right use case. These simple examples explain why ML works when applied to the appropriate use case:

• Recommendations on Netflix, Kobo or social media for deeper user behavioral predictions
• Medical imaging and diagnostics ML programs that learn to scan images and medical information and help detect certain illnesses
• Faster drug development and diagnosis to improve patient outcomes
• Fraud detection when your credit or debit card usage patterns or your login changes
• Behavioral targeting for better customer engagement, retention and loyalty programs

Even with some data and the right use case, you should be willing to accept that not everything requires a machine learning solution. Simply put, don’t build a machine learning model if you can use a simpler approach just as well.

Curious about machine learning?

Machine learning is different in theory from application. It might work seamlessly, and users might find its prompts easy to navigate, but this doesn’t mean that the solution is robust enough for daily use. While most companies are still in the early adoption stage, those that have developed machine learning models are still searching for ways to effectively implement ML and derive a true business impact from it.

While you don’t need to become an expert in ML, you should be aware of its limitations as well as its societal and ethical implications. ML is just one way to use AI, so you should first know how and why you want to use it. Ask yourself, “What problems can I solve with ML?”

Ideally, you should select a provider that can support the entire lifecycle of a machine build. From ideation and deployment to post-implementation performance and monitoring, your project should be guided by data scientists, engineers and developers who can ensure you get the most business value, as well as continuous transformation.

The Pythian difference

At Pythian, building models for the sake of models is not our goal. Our objective is to implement an ML model at scale that continuously learns and delivers insights that will help you drive growth, create loyal customers, reduce costs, save lives or achieve the  goal(s) you want to reach.

It takes specialized skills to execute an ML build efficiently, and our team offers the services that can take organizations through the entire process, from beginning to end. That’s what we did when we helped a major Canadian mining company reduce its haul truck downtime and predict failures with AI and Google Cloud.

If you’re ready to create a robust ML creation process, start with the following:

1. Create an Enterprise Data Platform (EDP).
2. Establish a well-defined ML use case with clear value for your organization.

If you need more hands on deck, Pythian can help you complete both tasks. With skilled data and AI scientists on our team, Pythian can also take you through the next steps to analyze the business problems that machine learning can help solve, build and deploy your models, architect your infrastructure to scale them, and optimize and fine-tune them to deliver results.