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Showing 7 open source projects for "amazon"
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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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amazon-connect-wisdomjs
Gives you the power to build your own Wisdom widget
Amazon Connect Wisdom, a feature of Amazon Connect, delivers agents the information they need, reducing the time spent searching for answers. Today, knowledge articles, wikis, and FAQs are spread across separate repositories. Agents lose a lot of time trying to navigate all those different sources of information, and in the meantime, the customer waits for an answer. -
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AWS SDK for PHP
Repository of the AWS SDK for PHP
The AWS SDK for PHP makes it easy for developers to access Amazon Web Services in their PHP code, and build robust applications and software using services like Amazon S3, Amazon DynamoDB, Amazon Glacier, etc. You can get started in minutes by installing the SDK through Composer or by downloading a single zip or phar file from our latest release. Before you begin, you need to sign up for an AWS account and retrieve your AWS credentials. -
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RDM
Cross-platform GUI management tool for Redis
...RDM supports all latest Redis ® features: ACL, Streams, Cluster, Sentinel, ReJSON module, HyperLogLog, etc. Build-in TLS, SSH and TLS-over-SSH tunneling for easy and secure access to any redis-server. RDM works with Amazon ElastiCache, Microsoft Azure Redis Cache, Digital Ocean and other Redis ® clouds. RDM displays JSON, MessagePack, CBOR, PHP, Pickle, numpy.Array, pandas.DataFrame and binary data in human readable form and supports Native Formatters. RDM offers you an easy-to-use GUI to access your Redis ® databases and perform some basic operations. ... -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
...Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. -
Add Two Lines of Code. Get Full APM.Works out of the box for Rails, Django, Express, Phoenix, and more. Monitoring exceptions and performance in no time.
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AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
...In Amazon SageMaker, example Jupyter notebooks are available in the example notebooks portion of a notebook instance. To run the AWS Step Functions Data Science SDK example notebooks locally, download the sample notebooks and open them in a working Jupyter instance. -
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SageMaker TensorFlow Serving Container
A TensorFlow Serving solution for use in SageMaker
SageMaker TensorFlow Serving Container is an a open source project that builds docker images for running TensorFlow Serving on Amazon SageMaker. Some of the build and tests scripts interact with resources in your AWS account. Be sure to set your default AWS credentials and region using aws configure before using these scripts. Amazon SageMaker uses Docker containers to run all training jobs and inference endpoints. The Docker images are built from the Dockerfiles in docker/. ... -
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using SageMaker
Welcome to Amazon SageMaker. This projects highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker. If you’re new to SageMaker we recommend starting with more feature-rich SageMaker Studio. It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations.
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