Improving Your ML Models - Why AI Product Features Matter
Product features = Quality ML Models
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Last week, we discussed the must-haves for turning an ML model by taking a product focus mindset. But let's face itâ87% of these projects never go live. For senior level data scientists and data teams this is a costly problem.
Imagine a team of 10 data scientists costing $100,000 per month. If the model isn't business-ready in three months, that's $300,000 down the drain. With multiple concurrent projects, this adds up fast.
So what's the fix, especially when next-gen tech like GenAI and cloud-based ML are making costs tick? The key lies in treating your model as a product, pinpointing its vital business enabling features right from the get-go. We need to start with the âwhyâ behind the model â the reason for its existence.
Authors Note: features in this article refer to features of a ML model as a minimum viable product (MVP). Rather than features being used to build and train a ML model.
The recipe for success for data science teams and product managers is to find out what is possible now, possible later, or even too complex to do with our current team and resources. These needs determine features you will add to the model.
Product features are absolutely essential in transforming a technically proficient machine learning model into a valuable business asset. An accurate ML model, on its own, won't cut it; it must also possess user-friendly features that make its insights not just understandable, but actionable for the business.
Why Product Features, and Not Just Building the Model?
We ought to treat ML models as data products to maximize their value. This isnât just a tactical decisionâitâs a strategic one.
A product feature is what makes your machine learning model more than just smart codeâit an aspect makes it useful for real business needs. This could be how the predictions are used, apps they enable, or technical functionality.
It's not just about how well the model works, but also how people can actually use it to solve problems and make better decisions. Theyâre aspects of the model that enable specific characteristics of end user questions.
We need an effective minimum viable product with scoped and defined features to create effective AI/ML initiatives.
An ML minimum viable product (MVP) without defined product features is like a fancy car with no wheels or breaksâit may look great but it's not going anywhere.
When your team focuses on product features, you're making sure that the model not only works but is also useful in the real world. This boosts your return on investment (ROI) and gets more people on board with using your model.
So, to build a model that truly delivers value, focus on four key types of features:
Critical Features: These are must-haves that solve the core business problem.
Future Features: These are nice-to-haves that can improve the model later.
Unfeasible Features: These are the ideas that just can't work right now.
Wishlist Features: These are the dream features that might be possible one day â or not.
Now, let's dive into each of these a bit more.
Start with Critical Features to Build an Effective Minimum Viable Product (MVP)
Critical Features are the backbone of any ML model, making it a functional product that delivers on its potential. Both data scientists and product managers need to collaborate early on to outline these essential elements.
This is essential if you want a simple ML model that worksârather than a shiny paperweight.
Understanding what makes a feature key to the model's success is influenced by several important factors:
Business Context: Knowing what the business aims to achieve is fundamental for aligning the model's key features with those objectives. If there's a disconnect here, you could end up with a technically impressive but irrelevant model. It's like having a state-of-the-art GPS system that doesn't know which destination to aim for; it won't help you get where you need to go.
Data Quality and Availability: The bedrock of any effective ML model is quality and available data. Without it, even the most sophisticated algorithms can't make reliable predictions or analyses. Stakeholders and data teams must be on the same page about what constitutes good data, ensuring the model has a reliable foundation. Think of this as the fuel that runs your machine; poor-quality fuel can clog up the engine and make it inefficient.
Functionality: The selected key features must not only address the business problem at hand but also be compatible with the existing technological ecosystem. It's like choosing the right pieces for a puzzle; each feature must fit perfectly within the larger operational framework to create a cohesive, functioning model.
Usability: It doesn't matter how advanced your model is if the people who need to use it can't understand its outputs. Usability ensures that the key features produce results that are both clear and actionable for the end-users, whether they're business analysts, marketers, or C-suite executives. If your model is a tool, think of usability as the handle; it needs to be designed so that people can grip it comfortably and use it effectively.
Monitoring: Keeping an eye on how well the models are doing is crucial for it to work well in the long run. This means checking on different versions of the model and the data it uses. By doing this regularly, you can catch problems early and make sure the model keeps helping the business.
Maintenance: An effective ML model isn't a "set it and forget it" endeavor. There needs to be coordinated maintenance. Scheduled upkeep, like data updates and retraining cadence, is essential to maintain the model's relevance and effectiveness.
Prioritizing these aspects of key features from the get-go can help you avoid common pitfalls like going over budget or launching a model that doesn't deliver the expected value.
Scoping these features helps create the simplest ML model possible, which answers the business questions it was built for. Its the effective baseline that you iterate on later if its sucessful.
Simply put, these are the non-negotiables that determine a model's viability and value.
Use Future Features to Iterate on the MVP
Not all ideas floated by the end users are bad ideas. They just may not be doable now. Future features are part of the AI product roadmap but not necessarily for the initial launch.
These are elements that aren't critical for the initial launch but can enhance the model's utility and performance in later iterations. Often, these features evolve due to user feedback and changing requirements.
Here are some guidelines to identify and handle future features effectively:
Alignment with User Feedback: Future features often surface from ongoing user engagement. These features are not vital for the immediate functioning of the model but could solve specific user problems or enhance user satisfaction. For example, in a recommendation engine, a future feature could be personalized user profiles that allow for different types of recommendations based on mood or occasion.
Data-Driven Opportunities: Evaluate whether the data needed for these future features is attainable but not immediately available. For instance, a predictive healthcare model may rely on basic patient data initially. A future feature could incorporate genetic data to provide more personalized healthcare recommendations.
Technological Scalability: Future features should be technically feasible within the near-term technological roadmap. For example, if your natural language processing model currently understands English, a future feature could be the capability to understand multiple languages as you scale.
Long-Term Strategic Fit: Future features should align with the long-term business strategy and goals how the ML model as a product will be used. In a fraud detection model, a future feature could involve integrating with additional financial databases to identify more complex fraud patterns over time.
Cost-Benefit Analysis: While a future feature may not provide immediate value, its long-term benefits should outweigh the costs involved in development and integration. For instance, in a climate prediction model, a future feature could involve simulating the impacts of new environmental policies, which could be expensive to develop but offer substantial long-term value.
Once you've got a list of future features, write them down. It's a good idea to team up with a product manager to decide when and how to add these into your model.
The goal isnât to build these features immediately into the model. Itâs to give options to position the model to support the future goals of a business and AI strategy. Having a regular delivery cadence has been one of the most important lessons I learned in my career.
By keeping these points in mind, you'll be better equipped to improve your ML model in the future, keeping it useful and up to date.
Avoid Unfeasible AI Product Features
When you think of unfeasible AI product features, youâre probably thinking technology. But the real culprits often boil down to your current resources: the time you have, the expertise of your team, your budget, and the tools at your disposal.
Time is limited. Teams have tasks to do. And tools and infrastructure have limitations.
The feasibility of a AI product feature can vary greatly depending on your industry, specific situation, or use cases. Not every company has the resources of tech giants like Google or Microsoft, so what might be doable for them could be a pipe dream for you.
So, how can you spot an unfeasible product feature in a model? Here are some ways:
Resource Assessment: Before adding a feature, assess if your team has the necessary time, budget, and skills. This is the main reason unfeasible features can cost so much for organizations. For example, if you're building a weather prediction model with a small team, adding a feature for long-term climate forecasts could be too resource intensive.
Technical Viability: Check if your current technology can support the new feature. Unfeasible features are not possible in the near future, without significant investment in infrastructure, training, etc. For instance, if you're working on a chat app, adding real-time translation may be too complex for your current tech stack or teamâs skills.
Business Alignment: Make sure the feature aligns with your current business goals. Functionality may be possible, but not aligned with the business model. For example, if your main goal is to improve a website's search function, adding a chatbot might be diverting resources away from that goal.
Complexity Level: Evaluate the complexity of implementing the feature. The effort to implement may rack up so much tech debt its unusable. For example, if your team has limited experience with image recognition, adding a facial recognition feature might be too complex.
The hard reality is that unfeasible product features isnât usually just one of these. Its several. When a ML model with unfeasible features is deep into production? It can be hard to root cause.
Reducing the number of unfeasible features early is crucial because they can become resource-draining pitfalls, increasing both costs, development, compute, and inference time. When I work with clients, identifying and avoiding such features is a top priority.
If a ML model is made of too many unfeasible features, when a manual process exists? Its probably time to question if you need ML model. Or if youâre building just to build. Its painful, but a good skill for a lead data scientist or AI product manager is to know when to cut their losses.
These features in a AI product are often the biggest bottlenecks for model building, and the biggest cost sinks. Many projects get stuck on these.
Identifying these early is a critical factor if youâre looking at ML model as a product.
Effective assessment and identification of unfeasible product features isnât just one time deal. A feature that looks easy at first can quickly become too hard to do because of surprises like running low on time, hitting tech roadblocks, changing company goals, or realizing it's more complicated than you thought.
While we have to avoid them when building?
Unfeasible product features arenât totally useless to a ML model. They can become useful parts as the data science organization matures, data infrastructure advances, and the data science team levels up.
Itâs wise to shelve them for the present, then reevaluate their ROI and usability later.
Think About Wishlist Features - Or Not.
Wishlist features for a machine learning product capture the "nice-to-haves," rather than the "must-haves." These are often the features users wish existedâeverything from a pie-in-the-sky idea to a well-considered but hard-to-articulate suggestion.
The wish for such features is often subjective and based on individual preferences or perceptions. However, these aren't necessarily flaws or shortcomings on the part of users.
It reflects a communication gap that product managers and data scientists need to bridge. Educating stakeholders about the complexities and limitations of ML technology is an ongoing process that can help manage these expectations.
Like with unfeasible product features, if you have too many of these when you scope? It means that the model is a dream, not a reality. Again, cut off ML model development early or risk costs.
Wishlist product features might seem overly ambitious or even fanciful (Iâve seen quite extravagant ones), it's important not to outright dismiss them. These imaginative requests can occasionally unveil hidden opportunities for improvement or innovation.
The trick is to engage in active listening and deeper inquiry to discern whether what initially appears to be a wishlist item could actually uncover a previously unrecognized need or functionality.
So instead of viewing wishlist features as mere daydreams or distractions, consider them as hints or signals that could, when carefully analyzed, yield actionable insights.
Taking the time to explore these could lead to uncovering needs or opportunities that are feasible and align well with both the technology and business strategy.
Final Thoughts
Evaluating features of a machine learning product is no easy task. And it will vary depending on your industry and the maturity of your tech stack and infrastructure.
To build great machine learning products, you need three things: technical skill, a focus on business goals, and a culture that supports change. These are crucial for both strong returns and making these practices a lasting part of your business.
Taking a clear evaluation of if a AI productâs core functional features take effective scoping, and involvement of product managers, lead data scientists, and management alike. Itâs a tactical and strategic force multiplier as businesses adopt AI more and more.
A machine learning models have the best chance of becoming a valuable, long-lasting strategic assets when its key features are carefully chosen, aligned with business objectives, and developed through a collaborative effort.
If you found this compelling, share it with your colleagues, spread the word, or engage with us. We're always eager to hear your valuable insights.
Iâd love to hear your thoughts.