AMA: DocuSign Senior Director of Product, AI/ML Platform, Deepak Mukunthu on AI Product Management

Assuming you are specifically interested in AI product management, I would suggest these approaches to get started with ML. While I was new to ML/AI, these approaches helped me. 1. Online courses 2. Part-time certifications 3. Conferences 4. Kaggle 5. Publish your work 6. Internship/Volunteering I covered this in my session on AI/ML Product Management: https://www.linkedin.com/video/event/urn:li:ugcPost:6929801568753971200/ If you are looking for guidance on general product management, let me kn ...Read More

I have seen 4 different types of AI Product Managers: 1. Product focused: Infuse intelligence into products (e.g., search, personalization) 2. Platform focused: Infrastructure & tooling for data scientists and ML engineers to manager ML lifecycle 3. Business/Operations focused: ML to improve product adoption/operations (e.g., customer churn prediction) or business outcomes (e.g., revenue forecasting). This role typically works with data science teams that work on internal optimizations lever ...Read More

While working on ML product/feature, there are 2 sets of metrics: 1. Product success metrics that product managers define. Purpose of the is to measure the business/product outcome you are trying to optimize for. Your standard metrics like customer adoption, usage, retention, satisfaction etc. fall in this bucket. So, product managers choose what's best for the situation at hand. 2. ML metrics that data scienstics define. These metrics measure how good the ML approach/solution/model is. For e.g. ...Read More

While infusing ML into a product can be extremely powerful to light-up new scenarios for customers and to optimize current scenarios, it can take a lot of effort to get it right. It all starts with defining and prioritizing the right scenario/problem to focus on, working with data science and ML engineering teams to map the business problem to an ML problem, trying out to different approaches to identify the right one to go ahead with, experimenting and optimizing and then operationalizing the a ...Read More

Broad understanding of ML lifecycle and knowledge of key ML trends in the industry are required to be successful in AI product management role. Beyond this, the level of hard/technical skills required depends on the type of AI product management role - there are 4 different types: 1. Product focused: Infuse intelligence into products (e.g., search, personalization) 2. Platform focused: Infrastructure & tooling for data scientists and ML engineers to manager ML lifecycle 3. Business/Operation ...Read More