Thinking about adding ML to your product? Start here first

Before diving into your first ML project, consider this sobering statistic.

85% of ML projects fail.

A common reason cited is that product teams focus on the wrong areas.

If you sprinkle ML like fairy dust over your entire codebase you'll just become another statistic. 

So what can you do? Let’s get into it!

👋🏼 Hi, it's Raj. Welcome to Product Playbooks. Every week, I dive into reader questions about the challenges of working in product teams.

Send me your questions, and I'll provide no-fluff advice in an actionable "1 hour playbook" format. Let's jump into this week's play...

Q: When should I ML?

Incorporating ML is all about finding the right opportunities and dodging the pitfalls—a bit like a game of snakes and ladders.

So, what’s the playbook?

Below is your 1-hour activity product teams can use to spot the most promising places to introduce ML into your product. I've also included some useful ML tools and services.

Playbook Instructions:

⏰ Run time: 1 hour
🧑‍💻 People: Your product team
🔨 Material: A whiteboard and post-its.
🔁 Repeat: When you’re tempted to slap ML into something without a good reason

Step 1: Find the ladders 🪜 [when to ML]

1) Identify Repetitive, Predictable Tasks 🌀

If a task makes you feel like you’re in Groundhog Day, it’s probably a good candidate for ML. Think about areas in your product where users repeat actions, and you could automate decisions with data.

Examples:

• Email sorting: Auto-labeling or prioritizing emails based on content
• Recommendations: Suggesting related products, content, or actions
• Image tagging: Auto-tagging photos based on image recognition
  
  

ML Products:

• Google Cloud AutoML: Automates the process of training models to sort, tag, or categorize data like emails or images.
• Amazon SageMaker: Great for building, training, and deploying custom models for recommendations or sorting tasks.
• IBM Watson Visual Recognition: For fast image tagging and visual pattern detection.
  
  

Play: Start small. Identify a specific task or process your users repeat, and explore use of these ML tools to automate and optimize it.

2) Use ML to Personalize Experiences 🎯

Everyone loves a good personal touch, but manually tailoring experiences for each user? Yikes. Enter ML. By analyzing user behavior, you can adapt the experience to each person's preferences.

Examples:

• Content feeds: Curating articles, videos, or music based on user engagement
• Shopping suggestions: "People like you also bought this flamingo-themed coffee mug!"
  
  

ML Products:

• Dynamic Yield: Provides AI-driven personalization for eCommerce, mobile apps, and websites.
• Algolia Recommend: Adds personalized product or content recommendations to your product using pre-trained models.
• Optimizely: Experiment with personalized experiences across different platforms and channels.
  
  

Play: Look at your user journey. Where could ML bring personalization to make them feel like your app is speaking directly to them?