Product School

How to Become a Product Manager for AI/ML Products

Natalia Kuznetsova headshot

Author: Natalia Kuznetsova

August 23, 2023 - 12 min read

Updated: January 24, 2024 - 12 min read

Let's dive right into the world of product management for artificial intelligence (AI) and machine learning (ML) products. These are not your everyday software products, and that's precisely what makes them an exciting challenge!

Editorial note: This post is based on a talk by Natalia Kuznetsova, Meta Sr Product Manager, on Product Managers for AI/ML Products and contains additional insights and examples from the Product School team. You can watch the webinar in full above.

Understanding machine learning products

First and foremost, let's demystify machine learning products. Unlike traditional software, where the outcome is generally deterministic and based on set algorithms, ML products evolve and learn from the data they're exposed to. It's a dynamic, ever-changing scene, and that means the product development process for these tools is equally dynamic.

Blog image 1: Become a ML PM

Distinguish between ML and AI

In this domain, terms like 'machine learning' (ML) and 'artificial intelligence' (AI) are often used interchangeably, but they're distinct. AI is the broader goal of machines mimicking human-like thought, while ML is a subset, teaching machines to learn from data. Yet, in practical contexts, "machine learning" is commonly used due to its specificity.

ML in products: Visualizing the spectrum

On a scale measuring the role of ML in products, at one extreme, ML is the core, like OpenAI's GPT or Google's search. At the other, ML enhances the experience but isn't central, as with Amazon's product suggestions or Netflix recommendations. In between, products like virtual assistants or self-driving cars rely heavily on ML but not exclusively.

What sets ML products apart?

ML products carry a promise, a charm, a challenge, and sometimes, a headache for product managers. So, what sets ML products apart? 

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The allure of high-performance

High performance is the crowning jewel of ML products, and it’s what justifies the high costs that often accompany them.  These should include:

1. The "Wow" element

The pinnacle of ML achievement is that jaw-dropping user experience – an outcome so impressive that it can't be replicated with mere rule-writing or arbitrary calculations. It's that 'magic' that leaves your users spellbound.

2. The art of adaptability

ML's beauty lies in its adaptability. Whether it's evolving user behavior or changing environmental conditions, a well-designed ML product seamlessly adapts, ensuring relevancy and precision.

3. Scalability at its best

Starting with one user segment and expanding to others? ML products, when adequately resourced, can scale gracefully, meeting varied needs without skipping a beat.

4. A lasting competitive edge

Nail your ML product, and you’ve not only created a marvel for now but have also equipped your business with a sustainable competitive advantage. It’s a skill that, once mastered, can be leveraged across multiple challenges with spectacular results.

The price tag: high costs

However, the splendor of high performance doesn’t come cheap. Here's the price you pay for the ML extravaganza:

1. The need for specialized talent

You can't merely swap roles here. Machine learning engineers are a unique tribe, specialized in their craft. If you bring them on board, ensure you've got a rich pipeline of projects to keep them engaged and productive.

2. Robust infrastructure

Planning to roll out complex models at scale? Brace yourself for significant investments in computational prowess and data storage. These aren’t your regular tech stacks.

3. Patience-testing development cycles

If you're accustomed to rapid, incremental progress, ML's often longer development cycles can test your patience. But remember, the wait is often worth the marvel that emerges.

4. The challenge of opacity

Transparency isn't ML's strongest suit. The 'black box' nature of some ML models can be a tad unsettling for those unfamiliar with its intricacies.

5. The demands of maintenance

ML products aren’t set-and-forget. From ensuring real-time data feed for accurate predictions to continuously monitoring and fine-tuning, the maintenance legwork is intensive. It demands resources, attention, and, above all, a commitment to excellence.

Essentials for ML

These are the key components you'll need to make it happen. Machine learning, while fascinating, does require a blend of resources to bring models to life.

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The fundamental elements

Two primary components form the bedrock of machine learning: data and algorithms. Think of them as the twin pillars supporting the edifice of your ML journey.

1. Data

Machine learning needs data, much like a car needs fuel. This data is structured information tailored to your prediction target, along with relevant features aiding the prediction. For predicting search result clicks, the clicks are 'labels' while user behavior and search details are 'features'. An example dataset is the Boston Housing dataset, where house prices are the labels and house attributes are the features. This helps test different models.

2. Algorithm

The algorithm turns data into actionable insights. Its complexity can vary, from simple linear regression to complex neural networks. The best choice depends on the specific task. Whether it's linear regression, decision trees, or neural networks, selecting the right algorithm is crucial.

Infrastructure and expertise

Beyond the basics, the actual implementation calls for specific infrastructure and human expertise.

1. Infrastructure

Large-scale or complex machine learning requires strong infrastructure, including data storage and computing power. This infrastructure supports both training and deploying the model and can add to ML-related costs.

2. People

The human aspect is crucial in machine learning products. Data scientists and ML engineers drive model training and refinement, while data engineers ensure data quality and optimize infrastructure. Their expertise, essential to the process, also contributes to ML costs.

The unique development process

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ML/AI product development goes beyond coding. It involves model training, data selection, and ongoing refinement. As a product manager, you'll navigate challenges unique to this field, coordinating with data scientists and ensuring algorithms meet user needs.

Let's dissect this journey, step by step, for clarity.

Step 1: Model training

At the very onset, you're aiming to train a potential model. This process has two key components:

  • Data preparation: More than just cleaning data, this step involves strategically crafting the key features for model training. It's a blend of science and creativity and is fundamental to ML projects.

  • Algorithm selection & tuning: After preparing your data, you'll select and then fine-tune the right algorithm. While the details are technical, understand that optimizing your algorithm greatly enhances your model's effectiveness.

Step 2: Performance evaluation

Once your model is trained, evaluate its accuracy. Regardless of its task, your model should get better with each iteration. After confirming its offline performance, you can move to real-world testing.

Step 3: Real-world testing

A/B testing compares your model to a control group or a previous version, assessing its impact on key business metrics. If metrics meet your goals, you can move the model to production. However, success may require multiple iterations and adjustments. Although you hope for consistency between offline and online results, discrepancies can occur due to technical issues, UI changes, or other challenges. If results differ from expectations, use a checklist to pinpoint potential problems.

The role of ML in product management

While you don't need to be a data scientist, a fundamental understanding of machine learning concepts is crucial. This will ensure that you can effectively communicate with your technical team and make informed decisions.

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1. Understanding the tech

To be effective in directing and guiding your team, you should grasp the basics of how algorithms work, the data requirements, and the challenges in training and deploying models. The clearer you are about the mechanics of ML, the better you can steer the product direction, set realistic expectations, and handle stakeholder interactions.

  • Identify the right problems to address

Understanding the technology helps discern which problems are right for ML. Not every issue requires machine learning; sometimes, simple analytics work best. Use ML for challenges with evolving patterns, like changing user behaviors or dynamic inventories. If a problem can be solved with set rules, it might not need ML's complexity. Choose judiciously, considering the resources ML demands.

  • Cultivate trust through knowledge

Gaining trust is vital. Understand ML intricacies to collaborate effectively with data scientists and engineers, leading the team to impactful tasks.

  • Approach with an objective lens

ML models mirror reality using provided data, so review for biases to prevent reinforcing prejudices. For instance, a recruiting tool relying on historical data might develop gender biases. As a product manager, prioritize ethical and unbiased models, emphasizing responsibility.

2. Evangelize

In the machine learning realm, balancing skepticism with optimism is key. As a product manager, you guide both the product and its perception. To succeed, champion ML by cultivating understanding, trust, and grounded optimism.

  • Balance Expectations

Many view machine learning as a complex magic. Some see it as an enigma, others as a solution to all challenges. As a product manager, your role is to clarify this technology, setting realistic expectations. Building trust means offering a truthful perspective on its capabilities and boundaries.

  • Educate stakeholders

In the tech landscape, stakeholders have varied understanding levels. As a product manager, you're both an educator and a visionary. Your role involves explaining machine learning's capabilities and limitations. It's vital to ensure stakeholders grasp and align with your vision. Balancing deep tech knowledge with clear communication is key to promoting collaboration and informed progress.

3. Monitor regulations

The AI and machine learning field is influenced by both technology and regulations. As product managers, stay updated on current rules and upcoming changes. These guidelines affect your data access, model deployment criteria, and management methods.

  • Stay informed

In the AI realm, regulatory knowledge is crucial. Stay updated on current guidelines and upcoming changes, as privacy laws and data access can significantly influence your product strategy. Being informed ensures not only compliance but also a product that respects user privacy and meets all standards.

  • Prioritize compliance

In the evolving AI field, strict regulations stem from past mistakes. See them as a chance to create a trusted product. By anticipating and complying with these rules, you instill trust in your product, ensuring smoother regulator interactions and boosting user confidence.

  • Know your regulations

Different regions have distinct data privacy rules, such as Europe's GDPR or California's Consumer Privacy Act. In domains like online advertising or e-commerce, understanding antitrust laws is crucial. Additionally, as AI-generated content grows, copyright concerns arise, especially when AI models reproduce artworks without permission. These discussions highlight potential challenges for artists and the risk of AI overshadowing human creations. In your AI journey, view regulations as guiding principles, not just obstacles. Your task is more than just adhering to rules; it's about ensuring best practices, protecting users, and building a lasting, trustworthy product.

4. Plan for scalability

In the dynamic digital world, scalability is essential. With machine learning, you're not just addressing today's needs but also preparing for tomorrow's demands. Your role ensures growth, adaptability, and future readiness.

  • Optimize existing solutions

Review your current model before seeking new solutions. Can it be refined or adapted for other tasks? Given the effort and resources put into creating a model, it's essential to utilize it fully. This approach optimizes returns and broadens your impact with one investment.

  • Future-proofing with foresight

Look ahead and anticipate future challenges. How can your current resources address them? As a product manager, connect the present to the future. By proactively spotting issues and aligning them with your team's strengths, you can boost efficiency and ROI.

  • Embrace modular and flexible design

Scalability hinges on adaptability. By adopting a modular and flexible design, you can modify or scale your solution without a full redo. This conserves resources and quickens adjustments to evolving situations.

Choose your path to becoming an ML product manager

Eager to venture into ML/AI product management? The route isn't set in stone. Whether you're tech-savvy, data-oriented, or business-driven, there's room for you. To begin, dive into data, grasp AI/ML basics, partner with data scientists, and prioritize user needs. Whether pivoting as an experienced PM or starting fresh, the ML/AI realm promises endless opportunities. 

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1. Immerse yourself in an ML-driven company

To truly grasp ML, immerse yourself in an innovative company. Leading ML companies offer rich learning experiences and mentorship. Begin as a PM, show interest in ML, and soon you'll be collaborating with data scientists and shaping AI advancements. This setting not only boosts your ML expertise but also hones essential soft skills for the domain.

2. Advocate for ML within your current role

Why seek externally when you can pioneer internally? If your current role doesn't involve ML, introduce it. Find areas where ML can enhance value, research thoroughly, and pitch with conviction. This not only highlights your leadership but also positions you as the company's ML expert. Your initiative might spur new projects or even spawn dedicated ML teams.

3. Forge your own path with an ML startup

If you're entrepreneurial and see an ML solution to a unique problem, consider starting a venture. Assemble a team and create from the ground up. This challenging route provides deep insights into ML, business strategy, and fundraising. Leading your own startup gives you the chance to directly influence its trajectory and leave a lasting impact in the ML realm.

Whichever path you choose, remember that the world of ML is vast and ever-evolving. Continuous learning, curiosity, and adaptability are your best allies. Dive in, embrace the challenges, and watch as you transform from a product manager into an ML Product Management maestro. Your journey begins now!

Learn more with Product School

Discover the potential of data analytics and guide your Product teams to remarkable achievements with our free micro-certifications. Crafted by esteemed Product Managers, these micro-certifications provide essential skills and hands-on experience to help you advance in your product career.

Updated: January 24, 2024

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