February 2023 Summaries

Sematic is an open-source development platform tailored for machine learning and data science, allowing users to efficiently build end-to-end ML pipelines that can be executed locally or in cloud environments. With support for integrations like PyTorch, Kubernetes, and Snowflake, it accommodates complex pipelines of Python-defined business logic across diverse computing resources. The platform's components include a lightweight Python SDK, an execution backend for local or Kubernetes orchestration, a command-line interface, and a web dashboard, with advanced features like run caching and fault tolerance. Sematic functions, which form the core of pipeline operations, are Python functions decorated to enable serialization and tracking, with their execution state monitored and visualized in a dashboard. The platform supports Python versions 3.8 to 3.10 on Linux and Mac, and can be installed via pip, with a web dashboard accessible through a local server. Sematic facilitates the iterative development and debugging of pipelines on local machines before deploying them to cloud environments, providing features such as step retries, pipeline nesting, and artifact visualization to enhance the user experience.

Deep links are crucial for navigating directly to specific in-app locations on mobile platforms and within single-page applications (SPAs) on the web, as they enrich URLs with additional information to preserve and share a particular app state. This method allows users to return to a specific point within an app or share that view with others, making the user experience more seamless and interconnected. In the context of the Sematic platform, deep links are implemented using hash fragments in the URL, which are preferred over query strings or paths due to their ability to separate concerns with React Router, their ease of manipulation with libraries like Jotai, and because they are not sent to servers, allowing for more extensive data storage. Jotai, a state management library inspired by Recoil, facilitates the handling of URL hash segments, allowing for efficient state synchronization and manipulation. The article outlines the technical intricacies of using the `atom-with-hash()` function in Jotai to maintain and update deep links, ensuring that user interactions can be preserved and shared without creating unwanted intermediate states in browser history.