Weights & Biases Integrations

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

Open-source web application, the Jupyter Notebook, allows you to create and share documents with live code, equations, and visualizations. Data cleaning and transformation, numerical modeling, statistical modeling and data visualization are just a few of the many uses.

Keras is an API that is designed for humans, not machines. Keras follows best practices to reduce cognitive load. It offers consistent and simple APIs, minimizes the number required for common use cases, provides clear and actionable error messages, as well as providing clear and actionable error messages. It also includes extensive documentation and developer guides. Keras is the most popular deep learning framework among top-5 Kaggle winning teams. Keras makes it easy to run experiments and allows you to test more ideas than your competitors, faster. This is how you win. Keras, built on top of TensorFlow2.0, is an industry-strength platform that can scale to large clusters (or entire TPU pods) of GPUs. It's possible and easy. TensorFlow's full deployment capabilities are available to you. Keras models can be exported to JavaScript to run in the browser or to TF Lite for embedded devices on iOS, Android and embedded devices. Keras models can also be served via a web API.

Select the subset of data that has the greatest impact on the accuracy of your model. This allows you to improve your model by using the best data in retraining. Reduce data redundancy and bias and focus on edge cases to get the most from your data. Lightly's algorithms are capable of processing large amounts of data in less than 24 hour. Connect Lightly with your existing buckets to process new data automatically. Our API automates the entire data selection process. Use the latest active learning algorithms. Combining active- and selfsupervised learning algorithms lightly for data selection. Combining model predictions, embeddings and metadata will help you achieve your desired distribution of data. Improve your model's performance by understanding data distribution, bias and edge cases. Manage data curation and keep track of the new data for model training and labeling. Installation is easy via a Docker Image and cloud storage integration. No data leaves your infrastructure.

Generative AI has a profound impact on virtually every industry. It opens up new opportunities for creative workers and knowledge to solve the world's most pressing problems. NVIDIA is empowering generative AI with a powerful suite of cloud services, pretrained foundation models, cutting-edge frameworks and optimized inference engines. NVIDIA AI Foundations is an array of cloud services that enable customization across use cases in areas like text (NVIDIA NeMo™, NVIDIA Picasso), or biology (NVIDIA BIONeMo™. Enjoy the full potential of NeMo, Picasso and BioNeMo cloud-based services powered by NVIDIA DGX™ Cloud, an AI supercomputer. Marketing copy, storyline creation and global translation in many different languages. News, email, meeting minutes and information synthesis.