Machine Learning Product Management 101

Product management isn’t new. It’s been around since businesses started making things people want. Machine learning? It’s been crunching numbers since the ’50s, but lately, it’s everywhere you look in tech—including your products.

For product managers who know their way around traditional roles but feel a bit out of depth with machine learning, this is for you. We’re going to cover:

• What exactly a machine learning product manager does
• The skills needed to nail this role as it grows and changes
• Practical steps towards either breaking into or mastering being a machine learning product manager

Let’s get straight to what you need to know about stepping up as a machine learning product manager.

What Is a Machine Learning Product Manager?

A machine learning product manager (ML PM) is, in plain terms, a technical product manager who’s taken on the challenge of overseeing products built with machine learning.

This role isn’t for the faint-hearted as it demands steering projects laden with complex algorithms and data sets that often have minds of their own.

Instead of managing straightforward software updates or feature rollouts, an ML PM juggles predictive models and analytics tools that can throw curveballs at any moment. 

They’re tasked not only with making sure these sophisticated tools are technically up to par but also that they hit business targets and genuinely solve users’ problems without causing new ones.

What we’re looking at here is someone who’s an equal parts translator and tactician—fluent in ‘data science,’ comfortable in engineering huddles, yet still able to talk shop with sales and marketing teams.

An ML PM needs to ensure everyone’s speaking the same language—or at least understands enough to play nicely together—in order to bring those smart ML-powered solutions from concept all the way through launch day successfully.

What Does a Machine Learning Product Manager Do?

A Machine Learning Product Manager, or ML PM for those who love acronyms, is essentially the translator between the high-flying world of machine learning tech and on-the-ground business impact.

First, let’s get our terms straight – AI is the cool kid everyone thinks they know, it’s all about machines doing things we thought only brains could handle. 

ML involves the development of algorithms that allow machines to learn and make decisions from data without being explicitly programmed, or “spoon-fed”.

An ML PM makes sure these algorithms play nice in products that hit market sweet spots while backing up company goals. 

Here’s what they do:

• Defining the Product Vision: This isn’t just tossing new tech around because it sounds fancy—it means latching onto machine smarts that make sense for the bottom line and give everyone something to cheer about at year-end presentations.
• Data Strategy Oversight: Think of data as the food for machine learning—it needs to be clean and high-quality, or things go sideways. Those in charge (that’s you, ML PMs) watch over how it’s gathered and used to make sure it meets the bar.
• Cross-Functional Leadership: You’re not just working with one group. You’ve got your hands in a few different cookie jars—like data scientists, coders from engineering, and story-spinners from marketing. As an ML PM, your job is to ensure everyone plays nice together and stays on point.
• Market and User Research: The job requires an attentive eye on what’s happening in the market and within user circles. As an ML PM, you’re expected to gather this intel diligently to ensure your product doesn’t just keep up but stands out.
• Performance Monitoring: Post-launch isn’t relaxation time—it’s when you watch closely how your product fares in the wild. You’ll analyze data, listen to users, and tweak things as needed while keeping one eye on today’s fixes and another on tomorrow’s opportunities for growth.

Sounds complicated? We won’t sugar-coat it, it is. But essentially, a machine learning product manager focuses on turning advanced ML tech into real products that people find easy to use and that meet company objectives. 

What Skills Do You Need as a Machine Learning PM?

The role of a Machine Learning Product Manager (ML PM) requires of course more than the average level of enthusiasm for tech. 

This position calls for an array of abilities that bridge technical knowledge, effective coordination, and sharp insight into what users really want. 

To excel as an ML PM, consider these must-haves:

Technical Expertise

No need to be a coding wizard, but grasp those machine learning fundamentals—algorithms, models, and data intricacies. It’s all part of your product’s backbone. Understanding this becomes important as you’re accountable for it end-to-end.

Project Management

Tackling an ML project? It’s not your garden-variety management and It’s quite multifaceted. You’re coordinating the birth and growth of these smart projects, all while juggling schedules, team dynamics, and those sneaky little things called “issues” that like to pop up when least expected.

Analytical Skills

Data is kind of a big deal in machine learning. We’re talking about diving into oceans of information to find those ‘needles in a haystack’ insights that will inform your strategy. Instincts are great for choosing lunch spots, not so much for making data-driven decisions here.

Communication

Your role pivots on understanding and disseminating the technical lingo of machine learning for everyone in the room. It’s essential to break it down so that both your data science team and non-tech stakeholders get what’s going on.

Empathy and User Focus: Never lose sight of who you’re building for – the users. Beyond engineering cutting-edge ML tech, you need to ensure your products are actually hitting home by addressing genuine user challenges.

Career Path and Earnings Outlook

Let’s talk turkey—machine learning product managers are in hot demand. From Silicon Valley mainstays to nimble startups all eyeing AI integration. 

The salaries are nothing to sneeze at. Averaging around $151k annually with potential spikes north of $200k depending on how deep your skills run and how much influence you wield within a project. 

Whether it’s industry leaders like Microsoft or disruptors like Netflix, there’s a seat at their table ready for an expert like yourself. 

And remember, while swinging into roles at startups might come with smaller paychecks initially, they offer different kinds of rewards, like watching a small tech company grow into something world-changing (OpenAI anyone?). 

How to Become a Product Manager in Machine Learning?

Ready to tackle the world of machine learning as a Product Manager? It’s not just about being tech-savvy. You need a mix of sharp technical smarts, solid management know-how, and an appetite for cutting-edge trends. 

If you’re shifting gears from conventional product management or diving into this space for the first time, here’s your no-frills playbook. Follow these steps to make your mark in machine learning without getting lost in translation.

1. Start with ML 101

Get a solid grip on machine learning basics. Take online courses or snag some certifications to kick things off.

2. Build Technical Proficiency

Dive into the tech side of things—get comfortable with key tools and coding languages used in ML, such as Python or R. You can use our software engineering roadmap guide to get started.

3. Gain Practical Experience

Roll up your sleeves and tackle some hands-on ML projects. Whether that’s part of your job now or through internships, there’s no substitute for real-world experience.

4. Develop Cross-Functional Communication Skills

Polish those communication skills so you can chat up both the nerds and the novices without breaking a sweat.

5. Stay Current with Industry Trends

Machine learning isn’t slowing down, and neither should you. Read up on the latest via articles or listen to chatter on podcasts, even a workshop can offer insights.

6. Understand Data Management

Data’s your bread and butter in this gig—you need to read it like a captivating novel. If your data’s messy, your outcomes won’t impress anyone.

7. Sharpen Your Instincts for Great Products

Cultivate that gut feeling about what will click in the market when you mix in ML —it’ll set you apart from the crowd.

8. Build an ML Focused Portfolio

Your portfolio is more than just homework, it’s proof of your impact. Make sure yours tells a story that people want to hear (and see).

9. Connect with Peers

Rub shoulders (digitally, of course) with others in the machine learning space. Who knows? Your next job lead could come from a casual conversation.

10. Find a Mentor

Scout out someone who’s been around the block in ML product management. Their insights can be pure gold.

11. Stay Agile

Don’t get too comfy, machine learning evolves at breakneck speed. Be ready to shift gears using an agile roadmap when needed.

12. Prioritize End-Users

Remember, it’s all about the people using your products - make their experience smooth and intuitive. To ace that, you should also know how to nail user experience survey questions.

13. Champion Ethical AI Practices

Take a stand on keeping machine learning product development aligned with ethical guidelines, ensuring fairness and clear visibility into how decisions are made.

14. Seek Targeted Education

Eyeing targeted educational opportunities - think master’s programs or specific courses - can help deepen your understanding of artificial intelligence and its applications.

15. Learn to Manage Teams

Sharpen those leadership abilities. They’re essential when you’re at the helm of varied teams tasked with delivering products powered by ML.

16. Practice Problem-Solving

Be relentless in boosting your problem-solving prowess; it’s vital for navigating the complex issues that come hand-in-hand with machine-learning ventures.

17. Embrace Failure

Prepare to accept setbacks as part of the package deal when working on cutting-edge tech like ML – they’re not just headaches but also steps toward greater knowledge.

The PM’s Hot Take

Here’s a tough lesson to learn for aspiring ML PM’s: Machine learning is more than just shoveling data into an algorithm and hoping for magic. You will sometimes struggle to fix actual issues without inviting a bunch of new headaches. With ML, you can’t just engineer your way out of bad data, so sometimes that might involve some tough love: Ditching beloved features when they can’t cut it outside the lab.

Conclusion

Venturing into the realm of machine learning as a product manager definitely isn’t a walk in the park. For those equipped with resilience and an eagerness to tackle new challenges, it’s undoubtedly fulfilling. 

Let’s be honest with ourselves, too. Machine learning and AI skills are fast becoming table stakes in our PM community.

If you’re either breaking ground or honing your craft, rest assured there’s no shortage of chances for growth for those ready to roll up their sleeves. 

Thinking about leveling up your game with machine learning? It might be time to evaluate your toolkit – is it future-ready? Check out Fibery: Built by PMs who get what you need because they’ve been there too. Give it a spin, we offer 14 days on us, no gimmicks involved.

Eager for more insight? Check out the Fibery blog to learn from peers who speak your language.

FAQ

Q: How is machine learning used in product management?

In the realm of product management, machine learning acts as a savvy assistant. It crunches numbers to predict trends, gets inside users’ minds to understand their actions better, and helps roll out features that feel like they’re reading your mind—providing data-backed insights for smarter strategy calls and fresher features.

Q: What does an ML product manager do?

Think of an ML product manager as the bridge between tech and business strategists. They guide machine learning from concept to seamless part of a user’s day-to-day with products that don’t just function—they revolutionize. All while juggling diverse team perspectives so everything moves toward those big company goals.

Q: How to become an ML product manager?

To step into the role of an ML product manager, it’s essential to build a strong grasp of machine learning tech. It also means sharpening your project management prowess and getting hands-on with leading projects that are fueled by ML.

Q: How to become a PM in AI?

If you’re eyeing a career as an AI Product Manager, start by wrapping your head around artificial intelligence basics. Keep pace with what’s new and hot in AI, and get comfortable with guiding diverse teams toward crafting AI-powered solutions that tackle genuine challenges out there in the world.