Machine Learning for Product Managers – Machine Learning 101
Machine learning (ML) makes it possible for product managers to scale data analytics, expedite research, automate tasks, and alert users.
With a well-managed algorithm, you can easily enhance your product and grow your user base.
But how do you get started? Machine learning seems so mysterious. And that’s because it sort of is mysterious.
Even the algorithm creators don’t fully understand “why” an algorithm selects something over another. For a product manager, it’s a lot to comprehend. However, there are some things that are relatively simple to understand, and that’s what we’re going to take you through in this 101 crash course for product managers. With this knowledge, you can communicate with your team effectively and work with them to find solutions.
Here are the most important things that product managers should know about machine learning.
Machine learning technology is really good at finding anomalies and patterns in data.
Machine Learning 101 – What Is It?
Machine learning is sort of like The Oracle in the Matrix.
You bring it a question and it gives you an accurate, yet secret, answer.
A simple example might be determining whether pictures of people are smiling, neutral or frowning. A program would present an image to the machine learning algorithm and it would spit out an answer: smiling.
While people often associate ML with AI (artificial intelligence), they are not interchangeable terms. ML makes up a single part of the complex AI spectrum.
AI experts aim to create software and robots that mimic human behavior on a broad scale (for improved feedback/services etc.), and ML is one subset/application to realize this goal. There are various ML methods, depending on the type of processed data. However, all ML methods work by direct computation and applying data without a preset equation – similar to the human mind.
How Does Machine Learning Work?
ML most often involves supervised or unsupervised methods.
In supervised ML, data scientists “teach” a program with example data. Once the program has enough example data to recognize patterns, it can start to predict reasonable responses to new inputs.
For example, in programs designed to find bruises on apples, a person would upload images of apples with and without bruises, teaching the program to identify each instance. After a certain amount of repetitions, the program can accurately detect bruises.
Unsupervised ML uses inputs without labeled (recorded) responses. An exploratory data analytical technique known as clustering enables programs to detect the structures and patterns within input data, drawing inferences for the predictive model. As such, ML can learn experiences and apply them in novel situations.
Through the self-learning capabilities of ML, product development and engineering teams can invest more time in other aspects of the product life cycle.
Advanced ML versions may engage in deep learning through Deep Neural Network (DNN) structures with multiple layers, where successful layers within a program tap on previous layer outputs as new inputs.
The Mysteriousness of ML
As mentioned above, machine learning’s outputs are on the secretive side. In other words, ML algorithms typically can’t explain why they made a decision despite producing accurate results.
For things like apple and mango categorization, the mystery doesn’t matter, but for decisions like loan applications and credit card fraud, this can be troubling.
While the lack of visibility may be acceptable during training or software testing phases, a vague “black box” approach could be problematic in audits and long-term customer relations.
ML experts have recently developed explainable AI (XAI), aiming to improve clarity and transparency on the steps behind a generated solution.
Machine Learning Applications for Product Managers
Currently, ML technology is really good at finding anomalies and patterns in data. For example, heart monitors and COVID cough analysis.
In heart monitors, a machine learning algorithm is monitoring and learning a user’s heartbeat at rest, walking, etc. Using past data, it can tell what’s “normal” and what’s not. If a user’s heart suddenly drops, the program can tell and then alert appropriate parties.
Other anomaly detections include medical imaging, weather, sorting, facial recognition. Even autonomous vehicles use machine learning to “see” the road. Using supervised ML, humans have taught computers what stop signs, bikers, pedestrians look like. With enough data, a car can then tell on its own.
Machine learning that recognizes patterns might be marketing tools that learn your behavior and browsing history to recommend personalized products. If enough people buy socks with their shoes, then a machine learning algorithm will begin to recommend those socks on the shoe pages.
Modern businesses have also integrated ML technology in chatbots, offering effective round-the-clock customer service based on keywords found in visitor queries. Advanced ML chatbots have the capabilities to maintain the context of a chat, mainly via a technology known as natural language processing (NLP). NLP enables ML programs to derive meaning and patterns from human languages and respond.
Time Taken to Operationalize an ML Model
While product managers may discover a diverse range of high-performing ML products in the market, they should only invest in a solution once they have established a clear and measurable business application. Decision-makers need to determine the data they need to optimize predictions that drive product development.
The pricing of ML solutions varies according to the specific needs of a project. Mostly, the price depends on the type of data managed by a program. Data is necessary for validating any ML application, and acquiring that data could be the biggest challenge for many product managers, especially sensitive information.
Building an ML algorithm is merely the first step in the ML development process. Many additional considerations follow, spanning legal, compliance, IT, and data security processes. There will be disparate teams working together to apply and maintain the best ML practices collectively.
For example, although data scientists create the ML algorithm, it is typically up to DevOps teams to roll out and manage the ML systems.
With all things considered, it can take months to successfully deploy a machine learning algorithm.
Ultimately, product managers should prioritize ROI over data accuracy. Calculated returns should reflect potential miscalculations, the accuracy of predictions, and the cost incurred by mistakes. With the proper setup, ML solutions can automate continuous product improvement across multiple applications and data sources – without lifting a finger.
DEPT®’s team of trusted software developers can help your product management team discover the most effective ML solutions.
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