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AI is creating a new generation of life, like the time when we started experiencing digitalization. So, there are a lot of demands for various AI models and AI-based products. To build AI models and products, two roles are critical: data scientists and machine learning engineers. But what do they do? What are their differences? Let’s check it together with a simple example: Two Types of Chefs Imagine you are a big, frontier restaurant. You invest a lot to invent new dishes and then serve your customers to have a new experience with your new dishes. To do so, you need some chefs to experiment with ingredients until the food tastes great and finally create a perfect recipe. It is like building a prototype in a kitchen lab. They are Data Scientists. On the other hand, just having a great recipe doesn’t mean you can serve thousands of customers each day. So, you need to design a kitchen to prepare that recipe quickly, consistently, and safely for a significant number of clients every day. They are Machine Learning Engineers. Customer Credit Risk Model in the Banking SectorA real example of building an AI product is a Customer Credit Risk Model in the banking sector to determine whether a customer should be approved for a loan. In this case, they work as follows: Data Scientists: Dive into the massive database of customer data. They check who paid back their loans and who couldn’t to find patterns. For example, they might discover that a customer's ratio of ‘monthly debt’ to ‘monthly income’ is a stronger predictor of a risky customer than just their credit score. They build a statistical model (an algorithm) that calculates: "If a customer had a high ratio of 'monthly debt' to 'monthly income' over the past 12 months, they are 80% likely to not be able to pay back the loan." They build a prototype model with a focus on the accuracy and logic of the decisions. ML Engineers: Once the Data Scientists have a successful prototype, the ML Engineers focus on the how and the when. They ensure the model is robust enough to run in a live banking environment. They build the automated data pipelines that pull real-time data from the bank’s databases to feed the model. This is critical because a loan decision needs to happen in seconds, not hours. In addition, they package the model into an API, allowing the bank’s website or app to call the model whenever a customer submits a loan application. Finally, they set up monitoring systems that alert the team if the model’s predictions start to lose quality. They manage the automated re-training process to ensure the model learns from the latest economic trends. At the end, I can say they are two sides of the same coin and work hand in hand to design, build, and run reliable and scalable AI models and products. Are you an action taker?If you are a Product Owner and want to migrate from a classic to an AI-aware Product Owner, attend my upcoming PSPO-AI Essentials (Professional Scrum Product Owner - AI Essentials) class. Click here for the class information. If you are a Scrum Master and want to migrate from a classic to an AI-aware Scrum Master, attend my upcoming PSM-AI Essentials (Professional Scrum Master - AI Essentials) class. Click here for the class information.
| # | Наименование новости | Тональность | Информативность | Дата публикации |
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| 9 | The Rise of AI Influencers: Can Users Still Tell What's Real? | 0 | 7 | 23-06-2026 |