In the process of building successful products, the common image is that of engineers deeply engrossed in coding the next great feature. While technical expertise is vital, equally important is effective collaboration among team members with diverse skill sets. Among these collaborations, the relationship between product managers and engineering teams, including data scientists, stands out as particularly crucial. Product managers are often the ones directly interacting with users, gathering valuable insights into what customers actually need and the challenges they face. On the other side, data scientists and engineers are usually engrossed in the technical aspects, potentially missing the user context. The bridge between these two worlds is effective communication. When product managers and data scientists communicate effectively, they ensure that the technical work aligns with customer needs. This alignment is essential, especially when it comes to the complexities of developing AI-driven products.
While AI product development shares similarities with traditional product development, it also introduces its own set of unique challenges. AI technology is often more technical and complex than products from the web 2.0 era. A notable challenge is the lack of tools available for non-technical team members like product managers to grasp the intricacies of the AI products they oversee. In order for product managers to effectively manage AI-driven products, they must be empowered with tools that give them visibility into a model’s behavior when interacting with data. Without gaining insights into how the model is interacting with different types of data, the product manager is left blind to the core of the model’s issues, and is incapable of effectively communicating what problems need to be worked on to the engineers on their team. The result of this lack of communication is often frustration, inefficiency, and prolonged feedback loops that slow down both product development and model refinement. Ultimately, these communication barriers can lead to dissatisfied users and underperforming products. As AI increasingly becomes a fundamental part of all products across all industries, it is important to solve this issue.
Artificial intelligence has ushered in new product categories, including autonomous vehicles and intelligent chatbots, while also enhancing existing products with enhanced capabilities. At the core of these products are the AI model itself, and the data that it is trained on. Data, in this context, serves as a kind of classroom for these models, where they learn to navigate the parameters of the real world. For instance, consider a self-driving car: It's not just coded to move; it's taught to navigate. This education comes from an expansive, ever-changing data set that encompasses countless variables from road conditions to pedestrian behavior. The goal is to provide a sufficiently broad and accurate understanding of the myriad of scenarios the vehicle will encounter, as the world continuously changes and evolves over time.
But herein lies the challenge. The real world is an extremely complex and dynamic place, presenting situations that no data set, no matter how robust, could fully encapsulate. Pedestrians don't always follow traffic rules; weather conditions can defy prediction. Even the urban landscape itself is a living, evolving entity that deviates from its digital representation in data. Addressing these "blind spots" requires more than just an initial dump of data. It necessitates a continuous, sophisticated data pipeline equipped with robust monitoring tools. This allows the AI models to adapt, refine, and extend their understanding of the world in real time, filling in gaps that were initially overlooked or could not have been anticipated. Data is not a static foundation but a dynamic framework, allowing AI to continually recalibrate and refine its understanding of an intricate and unpredictable world. Equipping teams with tools that demystify data for all members, including non-technical roles like product managers, is crucial for the efficient and effective development of AI products.
Communication barriers between product managers and engineering teams have long been a part of the tech world, but in the arena of AI, these barriers have their own unique challenges. The root of the issue often lies in the differing priorities and languages of these two key roles. While product managers are absorbed in questions like "What do we build?" and "How is it working for the user?", engineers are engrossed in the technicalities of constructing algorithms and optimizing data.
Product managers are the eyes and ears on the ground, gaining valuable insights from user interactions. They see what's effective and what's falling short. Their role requires them to understand how the product is functioning in the real world, an understanding crucial to making meaningful changes or additions to the product. Engineers, meanwhile, are often insulated from this user feedback, diving deep into the complexities of AI models and data pipelines. They might know how a machine learns but may know less about how users are interacting with the end product. This specialized focus sometimes leaves them with limited insight into how their algorithms are experienced by real users, making it harder to adjust these algorithms effectively.
Complicating this divide is the fact that many tools for monitoring AI performance are built for engineers and data scientists. This puts product managers at a disadvantage. They're left without a clear window into the real-time workings of the AI models they're supposed to guide. If they can't see where the model might be stumbling in the real world, they can't give focused feedback to the engineering team. The result? Extended feedback loops and a team that's not entirely in sync. Product managers struggle to convey what needs fixing or enhancing because they can't fully grasp the technical limitations or pinpoint where the model is falling short. Meanwhile, engineers might work on refining aspects of the model that, while technically interesting, don't necessarily solve the most urgent user problems.
This communication gap is more than just an internal issue; it has tangible repercussions for the product and its users. To break down these barriers, there needs to be a shared language and toolkit that both product managers and engineers can use. Otherwise, both parties work in parallel universes, hampering the product's potential for both innovation and user satisfaction.
Manot serves as a specialized MLOps platform with a focus on computer vision algorithms, offering critical understanding into how a model behaves and where it might falter. Starting with an in-depth analysis of the test set, Manot identifies and predicts areas where the model may struggle in unknown and novel conditions. Once these scenarios have been identified, the platform provides “insights”, in the form of images, to the user. Manot’s insights make it clear where, why, and how the model will fail in the real world, and allow the user to gain a clear understanding of where the model’s blind spots are.
What sets Manot apart is its versatility, offering tools useful for both product managers and engineers. A user-friendly interface allows product managers to delve into performance metrics and gain insights. This is crucial because product managers often identify user experience issues through customer interactions. Using Manot, they can relay these findings to engineers, focusing on specific areas in need of improvement. By empowering product managers with this type of tooling, AI teams significantly reduce their feedback loop, facilitating quicker and more efficient model refinement and redeployment, as compared to other platforms that merely highlight existing failures.
In addition to Manot’s ability to facilitate stronger communication between product managers and data scientists, the platform has several features that make for a better AI product development process.
Just as important as it is for product managers to familiarize themselves with the model’s performance in various scenarios, these insights at the end of the day must be utilized by data scientists and engineers to improve the model. Once a model is developed, data scientists can evaluate its performance on a variety of tasks using the platform and gain insights. The images that are extracted, or generated, using the platform can be used to train or fine tune the model in order to address the model’s blind spots. This level of specificity that Manot provides by curating the data is extremely important to data scientists, as it allows them to focus on the samples of data that will positively impact the model’s performance the most. In situations where data is abundant, Manot helps you concentrate on data segments that will most meaningfully influence your model's efficacy.
Great products are built by teams, not individuals. The best teams are ones where people with diverse skill sets come together to build something incredible. Ensuring that each team member has the right tools to effectively do their job is crucial. By building an MLOps platform with product managers in mind, this is exactly the gap that Manot has tried to fill. In order to effectively do their job, product managers must be able to gain an understanding of how their model is operating. This starts with having a tool like Manot that allows them to see and understand data. By enabling product managers, the entire team can work more effectively and efficiently, leading to reduced feedback loops, better products, and happier users. If this problem resonates with you, we’d love for you to try Manot by registering for an account here. After you’ve given the platform a shot, don’t hesitate to reach out to us with your feedback, questions, or comments.