Vertex AI
Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case.
Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.
Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.
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Appsmith
Appsmith enables organizations to create custom internal applications quickly with minimal coding. The platform allows users to build applications by connecting data sources, APIs, and workflows through a user-friendly drag-and-drop interface. Appsmith's flexibility with JavaScript lets developers fully customize components, while the open-source architecture and enterprise security features ensure scalability and compliance. With self-hosting and cloud deployment options, businesses can choose the best setup for their needs, whether for simple dashboards or complex business applications.
Appsmith offers a comprehensive solution for creating and deploying custom AI agents that can automate key business processes. Designed for sales, support, and people management teams, the platform allows companies to embed conversational agents into their systems. Appsmith's AI agents enhance operational efficiency by managing routine tasks, providing real-time insights, and boosting team productivity, all while leveraging secure data.
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Discuro
Discuro serves as a comprehensive platform designed for developers aiming to effortlessly create, assess, and utilize intricate AI workflows. With our user-friendly interface, you can outline your workflow, and when you're set to run it, simply send us an API call accompanied by your inputs and any necessary metadata, while we take care of the execution. By employing an Orchestrator, you can seamlessly feed the data generated back into GPT-3, ensuring reliable integration with OpenAI and facilitating easy extraction of the required information. In just a few minutes, you can develop and utilize your own workflows, as we've equipped you with everything necessary for large-scale integration with OpenAI, allowing you to concentrate on product development. The initial hurdle in connecting with OpenAI is acquiring the data you need, but we simplify this by managing input/output definitions for you. You can effortlessly connect multiple completions to assemble extensive datasets. Additionally, leverage our iterative input capability to reintroduce GPT-3 outputs, enabling us to make successive calls that broaden your dataset and more. Overall, our platform empowers you to construct and evaluate sophisticated self-transforming AI workflows and datasets with remarkable ease and efficiency.
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Laminar
Laminar is a comprehensive open-source platform designed to facilitate the creation of top-tier LLM products. The quality of your LLM application is heavily dependent on the data you manage. With Laminar, you can efficiently gather, analyze, and leverage this data. By tracing your LLM application, you gain insight into each execution phase while simultaneously gathering critical information. This data can be utilized to enhance evaluations through the use of dynamic few-shot examples and for the purpose of fine-tuning your models. Tracing occurs seamlessly in the background via gRPC, ensuring minimal impact on performance. Currently, both text and image models can be traced, with audio model tracing expected to be available soon. You have the option to implement LLM-as-a-judge or Python script evaluators that operate on each data span received. These evaluators provide labeling for spans, offering a more scalable solution than relying solely on human labeling, which is particularly beneficial for smaller teams. Laminar empowers users to go beyond the constraints of a single prompt, allowing for the creation and hosting of intricate chains that may include various agents or self-reflective LLM pipelines, thus enhancing overall functionality and versatility. This capability opens up new avenues for experimentation and innovation in LLM development.
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