teX.ai Integrations

TensorFlow is a comprehensive open-source machine learning platform that covers the entire process from development to deployment. This platform boasts a rich and adaptable ecosystem featuring various tools, libraries, and community resources, empowering researchers to advance the field of machine learning while allowing developers to create and implement ML-powered applications with ease. With intuitive high-level APIs like Keras and support for eager execution, users can effortlessly build and refine ML models, facilitating quick iterations and simplifying debugging. The flexibility of TensorFlow allows for seamless training and deployment of models across various environments, whether in the cloud, on-premises, within browsers, or directly on devices, regardless of the programming language utilized. Its straightforward and versatile architecture supports the transformation of innovative ideas into practical code, enabling the development of cutting-edge models that can be published swiftly. Overall, TensorFlow provides a powerful framework that encourages experimentation and accelerates the machine learning process.

Keras is an API tailored for human users rather than machines. It adheres to optimal practices for alleviating cognitive strain by providing consistent and straightforward APIs, reducing the number of necessary actions for typical tasks, and delivering clear and actionable error messages. Additionally, it boasts comprehensive documentation alongside developer guides. Keras is recognized as the most utilized deep learning framework among the top five winning teams on Kaggle, showcasing its popularity and effectiveness. By simplifying the process of conducting new experiments, Keras enables users to implement more innovative ideas at a quicker pace than their competitors, which is a crucial advantage for success. Built upon TensorFlow 2.0, Keras serves as a robust framework capable of scaling across large GPU clusters or entire TPU pods with ease. Utilizing the full deployment potential of the TensorFlow platform is not just feasible; it is remarkably straightforward. You have the ability to export Keras models to JavaScript for direct browser execution, transform them to TF Lite for use on iOS, Android, and embedded devices, and seamlessly serve Keras models through a web API. This versatility makes Keras an invaluable tool for developers looking to maximize their machine learning capabilities.

Selenium enables the automation of web browsers, and the possibilities it presents are entirely in your hands. While its primary use is for testing web applications, its utility extends well beyond that scope. Repetitive web-based administrative tasks can also be automated effectively, making your workflow more efficient and less tedious. For those looking to develop comprehensive browser-based regression automation suites and distribute scripts across various environments, Selenium WebDriver is the ideal choice, offering a set of language-specific bindings to control a browser as intended. Alternatively, if you are interested in quickly creating bug reproduction scripts or enhancing exploratory testing through automation, Selenium IDE serves as a convenient tool; this is a browser add-on for Chrome and Firefox that provides straightforward record-and-playback functionality for user interactions. Furthermore, if your goal involves scaling your testing processes by executing tests across multiple machines and managing diverse environments from a centralized interface, Selenium can accommodate those needs as well. Ultimately, the versatility of Selenium allows for a broad range of automation possibilities tailored to fit various requirements.

Smart City Solutions is a complete IoT system that monitors buildings. Smart City Solutions provides you with an iOS and Android compatible application that you can use for your customers. You can integrate numerous, manufacturer-independent sensors and monitor your buildings and rooms from anywhere. Smart City Solutions can be used to monitor your operations and make it accessible to your customers. Here is a list of all current users. The measured data from sensors in buildings and rooms are transmitted wirelessly and encrypted (e.g. via LoRaWAN to the loT platform. The transmitted data is then forwarded to the Camelot App, where it can be easily managed and clearly displayed.