MLOps: What is a Product First vs. Model First Mindset?
Product First versus Model First mindset is a important concept as you mature in data science.
Models can perform well in production, but ultimately fail to answer the business’s question. On the flip side, if you don’t focus on the model, you may be missing out on a opportunity to deliver more value.
This is the result of two different mindsets: product first vs. model first mindsets. There are two approaches we see in MLOps.
It’s critical for beginners learn this, since it affects everything: workflows, data quality requirements, etc.
What is the Difference?
It boils down to what the end goal is. Model mindset prioritizes the ML model that you are building. While product mindset focuses on the end data product: the minimum viable product.
While both mindsets are important, they prioritize different aspects of the machine learning process.
Its better to adopt a product first mindset when you start building a new model. When you are iterating, its better to adopt a model first mindset to improve a well performing ML model.
Each has its place.
Product First Mindset
A product-first mindset focuses on the end goal, which is delivering a valuable predictive insights to users. The ultimate goal is provide useful and high quality predict data that business users can act on.
Understanding user needs, business requirements, and desired outcomes are key. Scoping these use cases is even more important. This isn’t limited to just the business users, but also the individual partners along the way: the data analysts, data scientists, engineers, and ML engineers.
This collaboration encourages a product-first mindset encourages the development of a machine learning solution that is both useful, usable, and maintainable. Product first mindset requires a high amount of collaboration, trust in data, and understanding of use cases. Otherwise it fails.
Key aspects of the product-first mindset include:
- Identifying user needs and business requirements.
- Aligning machine learning models with the overall product or service.
- Prioritizing features and functionality that have the most impact on users.
- Ensuring seamless integration of machine learning components with existing systems.
- Constantly iterating and improving the product based on user feedback and data.
This plays a major part in a feedback loop, up an down the chain from end users to developers.
It can help streamline the model development process and focusing resources in the right places at the right time in all stages of MLOps. This means quicker deployment, retraining, and understanding of ML models in production.
Artifacts are an important part of the MLOps process. Check out this article on what Artifacts are and how they help MLOps processes.
When is Product First Used?
Product first mindset should be adopted the first time you are building a new model and is the foundation when building on a old one. Strive for simple model first.
It ensures that the model is built with user needs and organizational goals in mind. Which are the foundation for any debugging, artifact storage, and experiment tracking you’ll do. This helps reducing complexity that may cause model failure.
By focusing on minimum viable product, the model is more likely to be adopted, making it a successful first attempt. It builds credibility with users, which is absolutely critical.
You must tie predictions to some aspect of the business that can justify a models existence, and the help determine the operational costs of using the model. Or even if the business use cases are worth cost and effort.
This approach also encourages collaboration and communication between different teams, ultimately leading to better-designed products and more effective machine learning focused data products.
Model First Mindset
A model-first mindset, on the other hand, prioritizes the development and optimization of machine learning models.
This approach focuses on achieving high performance and accuracy in the models, and being able to serve them effectively. The model is the core.
Key aspects of the model-first mindset include:
- Developing and optimizing machine learning models.
- Achieving high performance and accuracy in the models.
- Focusing on model architecture and algorithm development.
- Experimenting with different techniques and algorithms to improve the model.
- Concentrating on the model’s capabilities rather than the user experience or business needs.
A model-first mindset can result in cutting-edge models, it may neglect important aspects of product development, such as user experience or business requirements. This is not a problem if model experimentation is a goal, however.
Its a major source of data scientist drift- where concept drift happens because the model was built around a tool/tech rather than answering a problem. Which unmaintainable and quickly fails in production.
This doesn’t mean it isn’t useful. Its a better idea to adopt a model focused mindset only after iterating on a successful and attributable ML model. It it’s used to improve, rather than build a basic model.
When is Model First the Focus?
Model first focus can be adopted after a ML model is built with a product first orientation. Its used to iterate on proven and value driven models, that consistently provide insights — even after retraining.
Model first works really well in certain situations:
- Research and Development. R&D’s focus IS building ML models and new algorithms. A model-first mindset is essential to push the boundaries of what’s possible and discover novel solutions.
- Complex Relationships and Patterns in Data. Sometimes a simple model answers the question, but needs to answer it better to deliver value. Prioritizing the model can help you identify, understand, and capture these patterns more effectively.
- Custom Models. Sometimes the business problem is so novel, you need to experiment a bit. Or even build multiple models. Starting with a model-first mindset allows you to explore potential model’s potential and limitations.
- Proof of Concept. Sometimes, you need to quickly demonstrate the potential of an ML model to stakeholders. So prioritizing the model can help you create a convincing proof of concept.
These MLOps workflows will look entirely different from each other. And different metrics and artifacts maybe logged and stored. Research and Development, for example, may need more metrics logged and artifacts stored than a production ML pipeline. Much of ML operations here focuses on showing the value of the model or experimentation.
Even when a model first focus is adopted, its critical that data science teams still have a product mindset as a base. Without it, its easy to over engineer a solution that may work, but is unmaintainable, costly, and doesn’t deliver value.
Conclusion
In practice, data science teams should strike a balance between these two mindsets. This will depend highly on your industry and the use cases.
Starting with a product first mindset helps growing data scientists focus on user needs and business goals, setting a strong foundation. As they start to successfully deploy models and gain business buy in, adopting a model first mindset enables optimization and improvement.
It’s essential to strike a balance between these mindsets throughout the development process to create effective and valuable machine learning solutions.
