AI Data Analyst Agent for Large Datasets

Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.

It is a small, yet powerful model adaptable to many use cases. It is better than Llama 2 13B on all benchmarks, has natural coding abilities, and 8k sequence length. We made it easy to deploy on any cloud.

Machine learning models are only as good as the datasets they're trained on. It helps ML teams make better models by improving their dataset quality.

It is an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese.

It helps you understand and explore advanced deep learning. It is actively used and maintained in the Google Brain team. You can use It either as a library from your own python scripts and notebooks or as a binary from the shell, which can be more convenient for training large models. It includes a number of deep learning models (ResNet, Transformer, RNNs, ...) and has bindings to a large number of deep learning datasets, including Tensor2Tensor and TensorFlow datasets. It runs without any changes on CPUs, GPUs and TPUs.

It is the machine learning platform for developers to build better models faster. Use W&B's lightweight, interoperable tools to quickly track experiments, version and iterate on datasets, evaluate model performance, reproduce models, visualize results and spot regressions, and share findings with colleagues.

It provides all you need to build and deploy computer vision models, from data annotation and organization tools to scalable deployment solutions that work across devices.

It is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. It was developed by researchers and engineers in the Google Brain team and a community of users.

It is an AI observability and LLM evaluation platform designed to help ML and LLM engineers and data scientists surface model issues quicker, resolve their root cause, and ultimately, improve model performance.

It is an open-source language model. It is trained with 1.5 trillion tokens of content. The richness of dataset gives StableLM surprisingly high performance in conversational and coding tasks.

It is an efficient and easy-to-use text annotation tool for Natural Language Processing (NLP) applications. With this, you can train an NLP model in few hours by collaborating with team members and using the machine learning auto-annotation feature.

It is an API for high-accuracy text classification and entity extraction. We make your unstructured text as easy to work with as your tabular data.

It is a framework to easily create LLM powered bots over any dataset. It abstracts the entire process of loading a dataset, chunking it, creating embeddings, and then storing it in a vector database.

It is a forecast library that allows you to do exploratory data analysis (EDA), forecast pipeline, model tuning, benchmarking, etc. It includes the Silverkite model, a forecast model developed by Linkedin, which allows feature engineering, automatic changepoint detection, holiday effects, various machine learning fitting methods, statitical prediction bands, etc.

It is a Python library to label, clean, and enrich text datasets with any Large Language Models (LLMs) of your choice.

Manage your entire data labeling workflow with a single tool. It uses AI to help humans label text data more efficiently for Natural Language Processing.

The platform for computer vision experts to iterate more quickly between data labeling, model training and failure case discovery.

It is an interactive AI evaluation platform for exploring, debugging, and sharing how your AI systems perform. Evaluate any task and data type with Zeno's modular views which support everything from chatbot conversations to object detection and audio transcription.

It is an open-source, uncensored, and commercially licensed dataset and series of instruct-tuned language models based on Microsoft's Orca paper.

It is a federated learning framework that allows developers to federate their machine learning workflows and train their models across distributed datasets without having to collect the data in a centralized location.

It is an open platform that enables LLMs to master thousands of real-world APIs. It provides a synthetic dataset, a tool learning framework, and a neural API retriever to facilitate LLMs to execute complex instructions and interact with various APIs.

It aims to make large models accessible to everyone by co-development of open models, datasets, systems, and evaluation tools.