4.3 out of 5 stars
(1178 Ratings)
Gemini platform is an enterprise artificial intelligence development and machine learning platform from Google Cloud that provides the tools and infrastructure required to build, customize, deploy, and govern AI models and autonomous agents. The platform represents the evolution of Vertex AI and combines generative AI development, predictive machine learning, agent orchestration, model training, and enterprise governance within a unified environment. Its Model Garden provides access to more than 200 foundation models, including Google's Gemini family, third-party language models, and open-source alternatives that can be evaluated, customized, and deployed for different applications. Agent Studio provides a low-code workspace for designing AI agents, experimenting with prompts, testing model behavior, and prototyping intelligent applications through visual development tools. The Agent Development Kit offers a model-agnostic framework for building sophisticated agents capable of reasoning, using external tools, coordinating specialized subagents, and executing multistep workflows. Google Antigravity extends the platform's agentic capabilities with a development environment for managing AI coding agents, generating application code, and coordinating automated software engineering tasks. Gemini platform also provides custom machine learning training with support for different frameworks, configurable training jobs, hyperparameter optimization, and deployment of trained models for online or batch predictions. Its MLOps capabilities include automated pipelines, model evaluation, Model Registry, Feature Store, and monitoring tools that help engineering teams manage the machine learning lifecycle and identify performance degradation.
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5.0 out of 5 stars
(4 Ratings)
Hivenet Compute gives you on-demand RTX 5090 and RTX Pro 6000 GPUs, plus vCPU instances, billed per second while they run. Hivenet owns and operates the infrastructure, with data centres in France, the UAE and the US, so EU teams can keep workloads and data in Europe.
Use it for training and fine-tuning, inference, notebooks, rendering and batch jobs. Launch from ready-made templates in a few minutes, or automate everything through the public Compute API. If you'd rather not run the serving layer yourself, the Inference API gives you an OpenAI-compatible endpoint.
Teams get organisations with role-based access, prepaid credits, and S3-compatible object storage alongside compute.
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Amazon EC2 Trn1 Instances
Amazon's Elastic Compute Cloud (EC2) Trn1 instances, powered by AWS Trainium processors, are meticulously engineered to optimize deep learning training, especially for generative AI models such as large language models and latent diffusion models. These instances significantly reduce costs, offering training expenses that can be as much as 50% lower than comparable EC2 alternatives. Capable of accommodating deep learning models with over 100 billion parameters, Trn1 instances are versatile and well-suited for a variety of applications, including text summarization, code generation, question answering, image and video creation, recommendation systems, and fraud detection. The AWS Neuron SDK further streamlines this process, assisting developers in training their models on AWS Trainium and deploying them efficiently on AWS Inferentia chips. This comprehensive toolkit integrates effortlessly with widely used frameworks like PyTorch and TensorFlow, enabling users to maximize their existing code and workflows while harnessing the capabilities of Trn1 instances for model training. Consequently, this approach not only facilitates a smooth transition to high-performance computing but also enhances the overall efficiency of AI development processes. Moreover, the combination of advanced hardware and software support allows organizations to remain at the forefront of innovation in artificial intelligence.
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AWS Neuron
The system facilitates high-performance training on Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances, which utilize AWS Trainium technology. For model deployment, it provides efficient and low-latency inference on Amazon EC2 Inf1 instances that leverage AWS Inferentia, as well as Inf2 instances which are based on AWS Inferentia2. Through the Neuron software development kit, users can effectively use well-known machine learning frameworks such as TensorFlow and PyTorch, which allows them to optimally train and deploy their machine learning models on EC2 instances without the need for extensive code alterations or reliance on specific vendor solutions. The AWS Neuron SDK, tailored for both Inferentia and Trainium accelerators, integrates seamlessly with PyTorch and TensorFlow, enabling users to preserve their existing workflows with minimal changes. Moreover, for collaborative model training, the Neuron SDK is compatible with libraries like Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP), which boosts its adaptability and efficiency across various machine learning projects. This extensive support framework simplifies the management of machine learning tasks for developers, allowing for a more streamlined and productive development process overall.
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