Showing 4 open source projects for "software development using ai"

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  • Gemini 3 and 200+ AI Models on One Platform Icon
    Gemini 3 and 200+ AI Models on One Platform

    Access Google's best plus Claude, Llama, and Gemma. Fine-tune and deploy from one console.

    Build generative AI apps with Vertex AI. Switch between models without switching platforms.
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  • Try Google Cloud Risk-Free With $300 in Credit Icon
    Try Google Cloud Risk-Free With $300 in Credit

    No hidden charges. No surprise bills. Cancel anytime.

    Use your credit across every product. Compute, storage, AI, analytics. When it runs out, 20+ products stay free. You only pay when you choose to.
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  • 1
    Positron

    Positron

    Positron, a next-generation data science IDE

    Positron is a next-generation integrated development environment (IDE) created by Posit PBC (formerly RStudio Inc) specifically tailored for data science workflows in Python, R, and multi-language ecosystems. It aims to unify exploratory data analysis, production code, and data-app authoring in a single environment so that data scientists move from “question → insight → application” without switching tools. Built on the open-source Code-OSS foundation, Positron provides a familiar coding...
    Downloads: 2 This Week
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  • 2
    SageMaker Training Toolkit

    SageMaker Training Toolkit

    Train machine learning models within Docker containers

    Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and...
    Downloads: 0 This Week
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  • 3
    AWS Step Functions Data Science SDK

    AWS Step Functions Data Science SDK

    For building machine learning (ML) workflows and pipelines on AWS

    The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related...
    Downloads: 0 This Week
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  • 4
    SageMaker Containers

    SageMaker Containers

    Create SageMaker-compatible Docker containers

    Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to...
    Downloads: 0 This Week
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  • MongoDB Atlas runs apps anywhere Icon
    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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