Exploring Micro-Frameworks: Spring Boot
Spring Boot is a brand new framework from the team at Pivotal, designed to simplify the bootstrapping and development of a new Spring application. The framework takes an opinionated approach to configuration, freeing developers from the need to define boilerplate configuration. In that, Boot aims to be a front-runner in the ever-expanding rapid application development space.
Why Model Explainability is The Next Data Science Super power
Some people think machine learning models are black boxes, useful for making predictions but otherwise unintelligible; but the best data scientists know techniques to extract real-world insights from any model. For any given model, these data scientists can easily answer questions like What features in the data did the model think are most important? For any single prediction from a model, how did each feature in the data affect that particular prediction What interactions between features have the biggest effects on a model’s predictions Answering these questions is more broadly useful than many people realize. This inspired me to create Kaggle’s model explainability micro-course. Whether you learn the techniques from Kaggle or from...
How Is Data Science Changing Web Design?
Data science isn’t just changing web design in a minor way: it’s changing every aspect of it from the start of the design process to the end (and even beyond through the update process). Whenever you have the resources and expertise to deploy it, it’s worthwhile, because having cut-and-dry insight into performance is invaluable.
3 Reasons Counting is the Hardest Thing in Data Science
Counting is hard. You might be surprised to hear me say that, but it's true. As a data scientist, I've done it all - everything from simple regression analysis all the way to coding Hadoop Map Reduce jobs that process hundreds of billions of data points each month. And, with all that experience, I've found that counting often involves far more time and effort.
Guide to Spring Boot REST API Error Handling
Handling errors correctly in APIs while providing meaningful error messages is a very desirable feature, as it can help the API client properly respond to issues. The default behavior tends to be returning stack traces that are hard to understand and ultimately useless for the API client. Partitioning the error information into fields also enables the API client to parse it and provide better error messages to the user. In this article, we will cover how to do proper error handling when building a REST API with Spring Boot.
Data Science Production Methods
Creating a data science project and executing its modules is the primary step in the production environment, which is where every startup or some established companies fail. While implementing a new module of an existing data science project seems to difficult, working on the module due to the discontinuation of complex tools and techniques used in the design environment is even more so.
Spring Boot @ConfigurationProperties
Spring Boot provides a very neat way to load properties for an application. Consider a set of properties described using YAML format: