Can Machine Learning Help Supercharge Your Products? [+Webinar] [280 Group]

As Product Managers, we’re always looking for new ways to better fulfill our customer’s needs. Various waves of technology have promised to bring new capabilities to our products. Some, like cloud services, and mobile technologies have indeed revolutionized many of our products. Others, like 3-D displays, have only disappointed our customers and led to product failures. So, what can we expect from Machine Learning?

In this article, our guest writer Bastiane Huang, Product at Osaro, will help us better understand the promising opportunities, and limitations, of what Machine Learning can bring to our products. She’ll provide a brief overview of what Machine Learning is about, how it has already started reshaping markets, where it may not be as helpful to your products, and how you as a Product Manager can approach integrating Machine Learning into your product.

What Is Artificial Intelligence (AI) and Machine Learning (ML)?

There’s no universally agreed definition of AI and the definition changes all the time. Once a certain task is performed by a machine, the task is no longer in the scope of AI. ML is a subset of AI. CMU professor Tom M. Mitchell defined Machine Learning to be a study of computer algorithms that allow computer programs to automatically improve through experience.

Types of Machine Learning

There are three main types of machine learning:

Supervised learning

This is the most common and widely used type of learning. The algorithms learn from labeled data, i.e. training data sets that are tagged with the outcome the model is trying to predict. An example of this is teaching machines to recognize pictures with cats or dogs. In short, it’s about predicting outcomes.

Unsupervised learning

On the other hand, unsupervised learning algorithms learn to identify patterns in the data without labeled data. It can be used in clustering, association, and anomaly detection problems. There’s also semi-supervised learning which is essentially a hybrid between supervised and unsupervised learning.

Reinforcement learning (RL)

Here, the algorithms learn as they get feedback on corresponding predictions over time. RL is used in control domains such as robotics or self-driving cars.

You can also find more details in my article: How to Manage Machine Learning Products.

In each case, Machine Learning enables a move away from having to manually program the machine to self-learned autonomy: machines make predictions and improve insights based on patterns they identify in data without humans explicitly telling them what to do. That’s why ML is particularly useful for challenging problems that are difficult for people to explain to machines.

How Artificial Intelligence Is Reshaping Industries

While Machine Learning holds tremendous promise, 85% of AI projects failed to deliver on their promises to business, according to a report from Pactera Technologies. But a select few companies have realized outsized gains from their ML investments, changing the world along the way. What do the successful products have that the failures lack?

With 83% of businesses claiming AI to be their strategic priority, it’s clear that the AI revolution is not an event that’s 10 years away, but is unfolding right now. And the world’s most innovative companies are setting the pace. Google shifted from mobile-first to AI-first strategy in 2017. Amazon announced a $700m plan to train its workers into highly skilled jobs in 2019. According to BCG, nearly half of executives perceive not only potential opportunities but also significant risks from AI if their competitors realize AI efforts first. Their concerns are warranted. The data-hungry nature of AI makes it more likely to be a winner-take-all world.

Same for individuals. AI will impact most of us at some point in our lives. According to a global survey by ZipRecruiter, one in five job seekers fear they will one day lose their job to AI. Fortunately, the choice is in our hands. From sales and marketing, to manufacturing and supply chain, every one of us can choose to prepare ourselves to utilize the technology and stay relevant. Automation will displace jobs but it will generate more new ones. 40% of Global 2000 companies are adding more jobs due to AI adoption. The World Economic Forum predicted 133 million new jobs to be created by AI by 2022.

How to Manage Machine Learning for Your Product?

#1: Planning – start with defining the problem well

Don’t build a Machine Learning product if the problem you are solving doesn’t require ML. ML is a tool, a means to an end. Start with identifying the problem – the customer pain point that is in great demand (has business potential) – and determine if it is solvable (it’s technical feasibility). You can do a market sizing exercise to estimate the business potential. Then, the next question is: how do we know if ML can help address our user problems? There are numerous ML applications, but, to the core, ML is best suited for making decisions or predictions.

We can categorize ML applications into a few types:

Detection/Inspection: help users identify where defects or anomalies are. Examples include, fraud detection in banking or insurance or defect detection with computer vision in a production line.

Pattern Recognition: help users sift through massive amounts of data. Examples include recommendation, ranking, personalization, classification, predictive maintenance, clustering, and interaction with humans (e.g. natural language processing (NLP) for smart speakers such as Alexa or Google Home).

High Dimension Cognition: help users sift through massive amounts of high dimensional sensory data. Examples include AI-enabled robotics and self-driving vehicles.

Once you find the right problem to solve, the next critical task is to clearly define the requirements. Developing ML products is a highly iterative process. It might be tempting to skip proper planning and dive right into seeing what models can do. However, if you do so, you will likely end up wasting lots of time with no concrete results.

#2: Define the objective function (outcome) and metrics. Allow more space and flexibility.

As a Product Manager, you can help your team stay focused during such an extensive exploration process by:

• Defining an objective function: what’s the desired outcome that your model is trying to predict? Or are you trying to identify patterns in data? Is there any “ground truth” that you can compare the outcomes of your models to? For example, if you design a model to predict the weather, you can validate the performance of your model by comparing the forecast to actual weather data.
• Defining performance metrics: How do you measure the success or failure of your products? It’s not always straightforward to set acceptance criteria. For example, how do you measure the performance of a translation model against a human translator? Sometimes, you will need to see the initial results of the models and then decide on the criteria. But it’s important to take test criteria into consideration early on and constantly test the model until you find the right model that delivers satisfactory results.
• Testing the models early and frequently from end-to-end: You can think of ML models as black boxes. You define inputs and outputs that you want your model to generate without necessarily understanding what’s going on in the black box. That’s why it’s important to build end-to-end prototypes and test the models early and frequently whenever possible. Start with simple prototypes that can help you test key functionality and then iterate on it. Avoid starting with a comprehensive end-to-end solution at all costs.

#3: Think about your data strategy from Day One

Training ML models often requires lots of high-quality data. Deep Learning outperforms older algorithms when it’s trained with a large amount of data. Hence, it’s extremely important to outline your data acquisition strategy from Day One. You can buy data, partner with other companies, gather data from your customers, generate data internally, or hire a third party to generate or label data for you. You need to consider what your competitors do, what your customers and regulators think, as well as corresponding feasibility and cost for each of the strategies. It’s not the responsibility of data scientists to figure out your data strategy. It’s a strategic business decision that Product Managers, executives, and key stakeholders need to define.

#4: Think beyond Machine Language

In most cases, you are actually building more than an ML product. To make it a complete and production-ready product, you need a user interface, software to execute model predictions, and/or hardware components. You won’t have a successful product if you focus too much on building the ML model and overlook user experiences, for instance. You need a multifunctional team, including not only ML engineers and scientists, but also data engineers, software engineers, UX UI specialists, and/or hardware engineers. You also need to work with backend engineers to ensure that the infrastructure is there to support the ML team.

Decide if ML is Right for Your Product

Hopefully, this gives you some ideas of how Machine Learning could help your product, and how you should carefully approach integrating ML into your product. To learn more, join us for our webinar where we will discuss these topics in more depth, and share a few more best practices in managing Machine Learning products.

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About the Author

Bastiane Huang, Product at Osaro.
Bastiane Huang has extensive experience in product management and business development. She currently works at Osaro, a San Francisco based startup that builds machine learning software for robotic vision and control and Amazon’s Alexa group. She also worked with Harvard’s Future of Work Initiative and writes about AI-enabled robotics, machine learning, and product management for Robotics Business Review and Harvard Business Review. Follow her on: LinkedIn, Twitter @Bastiane_Huang, or Medium.

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