As Artificial Intelligence is becoming an increasing number of popular, more corporations and teams want to start out or increase leveraging it. Due to that, many job positions are appearing or gaining importance available in the market. A great example is the figure of Machine Learning / Artificial Intelligence Product Manager.
In my case, I transitioned from a Data Scientist role right into a Machine Learning Product Manager role over two years ago. During this time, I even have been in a position to see a continuing increase in job offers related to this position, blog posts and talks discussing it, and lots of people considering a transition or gaining interest in it. I even have also been able to substantiate my passion for this role and the way much I enjoy my day-to-day work, responsibilities, and value I can bring to the team and company.
The role of AI / ML PM remains to be quite vague and evolves almost as fast as state-of-the-art AI. Although many product teams have gotten relatively autonomous using AI because of plug-in solutions and GenAI APIs, I’ll concentrate on the role of AI / ML PMs working in core ML teams. These teams are frequently formed by Data Scientists, Machine Learning Engineers, and Research Scientists, and along with other roles are involved in solutions where GenAI through an API won’t be enough (traditional ML use cases, need of LLMs wonderful tuning, specific in-house use cases, ML as a service products…). For an illustrative example of such a team, you may check certainly one of my previous posts “Working in a multidisciplinary Machine Learning team to bring value to our users”.
On this blog post, we’ll cover the essential skills and knowledge which are needed for this position, the right way to get there, and learnings and suggestions based on what worked for me on this transition.
There are various essential skills and knowledge needed to succeed as an ML / AI PM, but an important ones could be divided into 4 groups: product strategy, product delivery, influencing, and tech fluency. Let’s deep dive into each group to further understand what each skill set means and the right way to get them.
Product Strategy
Product strategy is about understanding users and their pains, identifying the appropriate problems and opportunities, and prioritizing them based on quantitative and qualitative evidence.
As a former Data Scientist, for me this meant falling in love with the issue and user pain to resolve and never a lot with the particular solution, and serious about where we are able to bring more value to our users as a substitute of where to use this cool latest AI model. I even have found it key to have a transparent understanding of OKRs (Objective Key Results) and to care in regards to the final impact of the initiatives (delivering outcomes as a substitute of outputs).
Product Managers have to prioritize tasks and initiatives, so I’ve learned the importance of balancing effort vs. reward for every initiative and ensuring this influences decisions on what and the right way to construct solutions (e.g. considering the project management triangle – scope, quality, time). Initiatives succeed if they’re able to tackle the 4 big product risks: value, usability, feasibility, and business viability.
Crucial resources I used to study Product Strategy are:
- Good vs bad product manager, by Ben Horowitz.
- The reference book that everybody advisable to me and that I now recommend to any aspiring PM is “Inspired: Easy methods to create tech products customers love”, by Marty Cagan.
- One other book and creator that helped me catch up with to user space and user problems is “Continuous Discovery Habits: Discover Products that Create Customer Value and Business Value”, by Teresa Torres.
Product Delivery
Product Delivery is about with the ability to manage a team’s initiative to deliver value to the users efficiently.
I began by understanding the product feature phases (discovery, plan, design, implementation, test, launch, and iterations) and what each of them meant for me as a Data Scientist. Then followed with how value could be brought “efficiently”: starting small (through Minimum Viable Products and prototypes), delivering value fast by small steps, and iterations. To make sure initiatives move in the appropriate direction, I even have found it also key to repeatedly measure impact (e.g. through dashboards) and learn from quantitative and qualitative data, adapting next steps with insights and latest learnings.
To study Product Delivery, I might recommend:
- A number of the previously shared resources (e.g. Inspired book) also cover the importance of MVP, prototyping and agile applied to Product Management. I also wrote a blog post on the right way to take into consideration MVPs and prototypes within the context of ML initiatives: When ML meets Product — Less is usually more.
- Learning about agile and project management (for instance through this crash course), and about Jira or the project management tool utilized by your current company (with videos reminiscent of this crash course).
Influencing
Influencing is the power to realize trust, align with stakeholders and guide the team.
In comparison with the Data Scientist’s role, the day-to-day work as a PM changes completely: it is not any longer about coding, but about communicating, aligning, and (quite a bit!) of meetings. Great communication and storytelling turn out to be key for this role, especially the power to elucidate complex ML topics to non technical people. It becomes also vital to maintain stakeholders informed, give visibility to the team’s labor, and ensure alignment and buying on the longer term direction of the team (proving how it’ll help tackle the largest challenges and opportunities, gaining trust). Finally, additionally it is vital to learn the right way to challenge, say no, act as an umbrella for the team, and sometimes deliver bad results or bad news.
The resources I might recommend for this topic:
- The entire stakeholder mapping guide, Miro
- A must read book for any Data Scientist and in addition for any ML Product Manager is “Storytelling with data — A Data Visualization Guide for Business Professionals”, by Cole Nussbaumer Knaflic.
- To learn further about how as a Product Manager you may influence and empower the team, “EMPOWERED: Abnormal People, Extraordinary Products”, by Marty Cagan and Chris Jones.
Tech fluency
Tech fluency for an ML / AI PM, means knowledge and sensibility in Machine Learning, Responsible AI, Data normally, MLOPs, and Back End Engineering.
Your Data Science / Machine Learning / Artificial Intelligence background might be your strongest asset, be sure you leverage it! This information will help you talk in the identical language as Data Scientists, understand deeply and challenge the projects, have sensibility on what is feasible or easy and what isn’t, potential risks, dependencies, edge cases, and limitations.
As you will lead products with an impact on users, including responsible AI awareness becomes paramount. Risks related to not taking this under consideration include ethical dilemmas, company status, and legal issues (e.g. specific EU laws like GDPR or AI Act). In my case, I began with the course Practical Data Ethics, from Fast.ai.
General data fluency can be essential (probably you could have it covered too): analytical considering, being inquisitive about data, understanding where data is stored, the right way to access it, importance of historical data… On top of that additionally it is vital to kow the right way to measure impact, the connection with business metrics and OKRs, and experimentation (a/b testing).
As your ML models will probably should be deployed with the intention to reach a final impact on users, you would possibly work with Machine Learning Engineers inside the team (or expert DS with model deployment knowledge). You’ll need to realize sensibility about MLOPs: what it means to place a model in production, monitor it, and maintain it. In deeplearning.ai, yow will discover a fantastic course on MLOPs (Machine Learning Engineering for Production Specialization).
Finally, it could possibly occur that your team also has Back End Engineers (normally coping with the mixing of the deployed model with the remaining of the platform). In my case, this was the technical field that was further away from my expertise, so I had to take a position a while learning and gaining sensibility about BE. In lots of corporations, the technical interview for PM includes some BE related questions. Ensure to get an outline of several engineering topics reminiscent of: CICD, staging vs production environments, Monolith vs MicroServices architectures (and PROs and CONTs of every setup), Pull Requests, APIs, event driven architectures….
We now have covered the 4 most vital knowledge areas for an ML / AI PM (product strategy, product delivery, influencing and tech fluency), why they’re vital, and a few ideas on resources that may aid you achieve them.
Identical to in any profession progress, I discovered it key to define a plan, and share my short and mid term desires and expectations with managers and colleagues. Through this, I used to be in a position to transition right into a PM role in the identical company where I used to be working as a Data Scientist. This made the transition much easier: I already knew the business, product, tech, ways of working, colleagues… I also searched for mentors and colleagues inside the company to whom I could ask questions, learn specific topics from and even practice for the PM interviews.
To arrange for the interviews, I focused on changing my mindset: developing vs considering whether to construct something or not, whether to launch something or not. I came upon BUS (Business, User, Solution) is a fantastic strategy to structure responses during interviews and implement this latest mindset there.
What I shared on this blog post can appear to be quite a bit, but it surely really is way easier than learning python or understanding how back-propagation works. If you happen to are still unsure whether this role is for you or not, know that you could at all times give it a try, experiment, and choose to return to your previous role. Or perhaps, who knows, you find yourself loving being an ML / AI PM similar to I do!