Search Results for "java-ml"
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Showing 6 open source projects for "java-ml"
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Integrated AI strategy and execution softwareInspire Software is an AI powered strategic execution platform. It empowers organizations to elevate performance by aligning people, teams, and strategic goals in one streamlined platform. Headquartered in Jackson, Wyoming, Inspire brings together tools for goals, performance tracking, 1:1s, feedback, learning, pulse surveys, and more. This all-in-one solution fosters leadership across every level, helping align team efforts with your organization’s growth strategy for impactful results. With Inspire, you’re not just managing performance—you’re driving growth through leadership. Discover more at inspiresoftware.com.
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Cloud-based observability solution that helps businesses track and manage workload and performance on a unified dashboard.Monitor everything you run in your cloud without compromising on cost, granularity, or scale. groundcover is a full stack cloud-native APM platform designed to make observability effortless so that you can focus on building world-class products. By leveraging our proprietary sensor, groundcover unlocks unprecedented granularity on all your applications, eliminating the need for costly code changes and development cycles to ensure monitoring continuity.
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Codiga VS Code
VS Code plugin that suggests code blocks as you type and check
VS Code plugin that suggests code blocks as you type and check for errors. Works for JavaScript, TypeScript, Python, Java, Scala, Ruby, PHP, Apex, Docker. -
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Learning Interpretability Tool
Interactively analyze ML models to understand their behavior
The Learning Interpretability Tool (LIT, formerly known as the Language Interpretability Tool) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. It can be run as a standalone server, or inside of notebook environments such as Colab, Jupyter, and Google Cloud Vertex AI notebooks. -
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Data Annotator for Machine Learning
Data annotator for machine learning
Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning. Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitate the creation and management of annotations. Active learning with uncertain sampling to query unlabeled data. Project tracking with real-time data aggregation and review process. User management panel with role-based access control. -
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AWS Toolkit for Visual Studio Code
Local Lambda debug, CodeWhisperer, SAM/CFN syntax, etc.
... Explorer when the AWS icon is selected in the Activity bar. The AWS CDK Explorer enables you to work with AWS Cloud Development Kit (CDK) applications. It shows a top-level view of your CDK applications that have been synthesized in your workspace. Amazon CodeWhisperer provides inline code suggestions using machine learning and natural language processing on the contents of your current file. Supported languages include Java, Python and Javascript. -
Intelligent Automation Solutions Built for Modern Finance TeamsDigitally transform your business with workflow automation and integrated payment solutions. Digitally store and secure your data with advanced search and accessibility features that keeps your documents at the tip of your team’s fingers.
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html2canvas
A JavaScript HTML screenshot renderer
html2canvas is a JavaScript HTML renderer. The script provides you with the tools to take screenshots of webpages directly on the browser. The screenshot is based on the DOM and therefore, it may not be 100% accurate to the real representation, given that it is not an actual screenshot, but a type of screenshot built based on the available data and information of the page. The script renders such page as a canvas image, by reading the DOM and the different styles of the featured elements. It... -
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TensorFlow.js models
Pretrained models for TensorFlow.js
... run the unit tests for any of the models by running "yarn test" inside a directory. Use off-the-shelf JavaScript models or convert Python TensorFlow models to run in the browser or under Node.js. Build and train models directly in JavaScript using flexible and intuitive APIs. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js.
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