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features
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Description

AWS Deep Learning AMIs (DLAMI) offer machine learning professionals and researchers a secure and curated collection of frameworks, tools, and dependencies to enhance deep learning capabilities in cloud environments. Designed for both Amazon Linux and Ubuntu, these Amazon Machine Images (AMIs) are pre-equipped with popular frameworks like TensorFlow, PyTorch, Apache MXNet, Chainer, Microsoft Cognitive Toolkit (CNTK), Gluon, Horovod, and Keras, enabling quick deployment and efficient operation of these tools at scale. By utilizing these resources, you can create sophisticated machine learning models for the development of autonomous vehicle (AV) technology, thoroughly validating your models with millions of virtual tests. The setup and configuration process for AWS instances is expedited, facilitating faster experimentation and assessment through access to the latest frameworks and libraries, including Hugging Face Transformers. Furthermore, the incorporation of advanced analytics, machine learning, and deep learning techniques allows for the discovery of trends and the generation of predictions from scattered and raw health data, ultimately leading to more informed decision-making. This comprehensive ecosystem not only fosters innovation but also enhances operational efficiency across various applications.

Description

The Gemma family consists of advanced, lightweight models developed using the same innovative research and technology as the Gemini models. These cutting-edge models are equipped with robust security features that promote responsible and trustworthy AI applications, achieved through carefully curated data sets and thorough refinements. Notably, Gemma models excel in their various sizes—2B, 7B, 9B, and 27B—often exceeding the performance of some larger open models. With the introduction of Keras 3.0, users can experience effortless integration with JAX, TensorFlow, and PyTorch, providing flexibility in framework selection based on specific tasks. Designed for peak performance and remarkable efficiency, Gemma 2 is specifically optimized for rapid inference across a range of hardware platforms. Furthermore, the Gemma family includes diverse models that cater to distinct use cases, ensuring they adapt effectively to user requirements. These lightweight language models feature a decoder and have been trained on an extensive array of textual data, programming code, and mathematical concepts, which enhances their versatility and utility in various applications.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AWS Neuron
Amazon EC2 P4 Instances
Amazon EC2 Trn2 Instances
Amazon Web Services (AWS)
C#
Google AI Studio
Google Cloud Platform
Google Colab
HTML
Hugging Face
Java
JavaScript
LM-Kit.NET
LlamaCoder
MedGemma
NVIDIA DRIVE
Python
Scala
TensorFlow
TypeScript

Integrations

AWS Neuron
Amazon EC2 P4 Instances
Amazon EC2 Trn2 Instances
Amazon Web Services (AWS)
C#
Google AI Studio
Google Cloud Platform
Google Colab
HTML
Hugging Face
Java
JavaScript
LM-Kit.NET
LlamaCoder
MedGemma
NVIDIA DRIVE
Python
Scala
TensorFlow
TypeScript

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Amazon

Founded

2006

Country

United States

Website

aws.amazon.com/machine-learning/amis/

Vendor Details

Company Name

Google

Country

United States

Website

ai.google.dev/gemma

Product Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Product Features

Alternatives

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