Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.
Building an intelligent, predictive application involves iterating over multiple steps: cleaning the data, developing features, training a model, and creating and maintaining a predictive service. GraphLab Create does all of this in one platform. It is easy to use, fast, and powerful. | It is an evaluation tool that fits into your development and production pipelines to help you ship high-quality models with confidence. Treat your LLM product like traditional software development. |
Analyze terabyte scale data at interactive speeds, on your desktop.;A Single platform for tabular data, graphs, text, and images.;State of the art machine learning algorithms including deep learning, boosted trees, and factorization machines.;Run the same code on your laptop or in a distributed system, using a Hadoop Yarn or EC2 cluster.;Focus on tasks or machine learning with the flexible API.;Easily deploy data products in the cloud using Predictive Services.;Visualize data for exploration and production monitoring. | Get alerts every time your AI fails;
Powerful testing, evaluation, and observability for LLMs;
Monitor with real-time alerts;
Track and version |
Statistics | |
Stacks 8 | Stacks 0 |
Followers 40 | Followers 1 |
Votes 3 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
| No integrations available | |

Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.

It is the easiest way to deploy Machine Learning models. Start deploying Tensorflow, Scikit, Keras and spaCy straight from your notebook with just one extra line.

That transforms AI-generated content into natural, undetectable human-like writing. Bypass AI detection systems with intelligent text humanization technology

It is a framework built around LLMs. It can be used for chatbots, generative question-answering, summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.

It allows you to run open-source large language models, such as Llama 2, locally.

It is a project that provides a central interface to connect your LLMs with external data. It offers you a comprehensive toolset trading off cost and performance.

It is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner.

BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services.

It is a platform for building production-grade LLM applications. It lets you debug, test, evaluate, and monitor chains and intelligent agents built on any LLM framework and seamlessly integrates with LangChain, the go-to open source framework for building with LLMs.

It is a new scripting language to automate your interaction with a Large Language Model (LLM), namely OpenAI. The ultimate goal is to create a natural language programming experience. The syntax of GPTScript is largely natural language, making it very easy to learn and use.