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There are so many different areas of tech in which one can be a Product Manager. Today’s area of focus is the Machine Learning/Artificial Intelligence space. Aniththa Umamahesan, a Senior Technical Program Manager at Amazon, highlights the role of a Product Manager in this field and shares helpful tips for any who might like to explore that space.
Okay so you know how you log into your Spotify and all the music there is tailored specifically to you? There’s the ‘Discover Weekly’ tab with a lineup of all the songs you want to listen to and at the end of the year, they create that fun little summary that you share on your social media. All that magic is the work of Machine Learning and Artificial Intelligence (ML/AI). Aka, Aniththa’s job. Okay, not exactly Aniththa’s job but she’s a Product Manager working in that space so it’s technically the same thing.
Product Management chose Aniththa. She went to an interview at Microsoft for the role of Software Engineer but after the interview, the company reached out to her and said that she was a better fit for the Product Manager role. And that’s how she became a Product Manager.
What about her made her a fit for Product Management? First, Aniththa enjoyed talking and solving customer problems. Then in her previous job as a consultant at Deloitte, she worked on solving a specific client problem and by talking about that experience during the interview, she established that she possessed the skills required of a Product Manager.
Additionally, when she worked at Amazon, she was working in Demand Forecasting, an aspect of Machine Learning which involved looking at the tons of products across Amazon and helping to forecast how many of those products should be in fulfillment centers at a regional level, country level and so on. Based on her specific qualities and experiences, first as a Product Manager and then within the Machine Learning space, her interviewer could safely conclude that she was a fit for the Product Management role.
In this article, Aniththa sheds more light on Machine Learning, which is still considered a relatively new space within the tech world, navigating the space without a tech background, and how to achieve relative success in the field.
Machine Learning is a tool used to create a customized experience for the user. Companies have realized that having a personalized connection to an application makes users want to use it more, so ML works by collecting certain metrics and information about the customer which the company then uses to deduce user behavior. Think Spotify, mentioned earlier, or Netflix, which begins to recommend movies and shows specific to your taste after you’ve used the app for a little while.
Ultimately, Machine Learning improves the productivity of the user and streamlines the workflow of whatever product is being used. Additionally, it helps with decision-making. These all sound really great and helpful, but how exactly does it achieve these things? Through the use of data.
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The prerequisite to using ML/AI is having data. Data is used to identify trends and problems and act on them to improve the user experience of an application. Machine Learning takes all the data available and does everything that a human being would do in their mind in the process of figuring out a solution to an issue, except it does it faster and better and at a much larger volume. It produces results by doing all the behind the scenes work that you would have done manually.
The biggest issue with data is collecting the data, as there are many questions that arise in the process of doing this. There is data flowing through every organization but what is the right amount of data? What should the data look like? Often you’ll notice that there are missing values, how do you input that? Then you want to make sure the data is representing the machine learning problem correctly. You don’t want to generalize it too much otherwise, it won’t solve the problem.
Often too, there are questions about the security and privacy of persons as companies mine all this data. It might be helpful to know that usually, companies use metadata, and not the individual’s personal data to acquire the answers that they need to improve their product. However, security and privacy concerns are common and valid concerns in the field of Machine Learning.
Evidently, questions are not unusual in the ML/AI world. In addition to those mentioned above, you will have questions when it comes to model performance too. A model is a file in ML/AI that has been trained to recognize certain types of patterns and make predictions. To use it, you’ll need to ask yourself: How does a model need to perform for you to know that it’s the best model? Or how often should you retrain your model? Then when deploying the model, you’re faced with questions regarding how to take it to production or how often you should retrain the model.
So if you’re looking to work in this space, don’t be afraid of questions. Don't be afraid to ask them, to receive them, and to work on finding answers to them.
Aniththa’s academic background is in Computer Science and Finance and so to better understand what was expected of her as a Product Manager working in ML, she took some relevant courses to acquaint herself with the basics. She advises aspiring Product Managers going into a sector that they’re not familiar with, to engage in a knowledge-share with other Product Managers or even Engineers on their team, and understand how they learned.
You’re working in this new, niche sector but remember that you’re first of all, a Product Manager. That means you’re responsible for bridging the Machine Learning gap as well as the gap between customer expectations and engineering. You need to understand the product, product goals and customer. In fact, Aniththa recommends that the Product Manager be customer obsessed. Understand the user experience and what the customer needs so that you can communicate these needs to the data scientist, then get information back from them and communicate that to Engineers and customers.
Understanding the entire Machine Learning process is integral to doing your job. You don’t need to become an expert in the field but understanding the flow helps you know whether there are any missing pieces.
Find opportunities to build relevant experience especially when you’re building your resume. Tailor your skillset to being a Product Manager by using programs like Co.Lab that give you relevant experience. Even if your product isn’t successful at the end of the day, you’d have learnt something valuable.
Seek out mentors to guide, empower and teach you especially if you’re new to the space and stay in touch with your mentors for guidance and access to opportunities. The keyword here is to seek out mentorship, don’t wait for it to come with you.
Understand that your primary assignment is to advocate for the customer. As Machine Learning is a new field, try understanding the process for yourself first by working with stakeholders and understanding their processes.
Finally, as someone with experience working in three of the biggest tech companies in the world, Aniththa wants you to remember that a company has a vision, and you must ensure that your vision/career goal lines up with that of the company and lines up with the team you’re in and that way, you’ll find value in your work.
To watch the entire discussion with Aniththa, from which this article was derived, visit our YouTube channel. Also, stay tuned to our socials to join in on our next discussion and visit our website to find out more about Co.Lab!