Skyportal
Skyportal is a GPU cloud platform built for AI engineers, offering 50% less cloud costs and 100% GPU performance. It provides a cost-effective GPU infrastructure for machine learning workloads, eliminating unpredictable cloud bills and hidden fees. Skyportal has seamlessly integrated Kubernetes, Slurm, PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA Drivers, fully optimized for Ubuntu 22.04 LTS and 24.04 LTS, allowing users to focus on innovating and scaling with ease. It offers high-performance NVIDIA H100 and H200 GPUs optimized specifically for ML/AI workloads, with instant scalability and 24/7 expert support from a team that understands ML workflows and optimization. Skyportal's transparent pricing and zero egress fees provide predictable costs for AI infrastructure. Users can share their AI/ML project requirements and goals, deploy models within the infrastructure using familiar tools and frameworks, and scale their infrastructure as needed.
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BentoML
Serve your ML model in any cloud in minutes. Unified model packaging format enabling both online and offline serving on any platform. 100x the throughput of your regular flask-based model server, thanks to our advanced micro-batching mechanism. Deliver high-quality prediction services that speak the DevOps language and integrate perfectly with common infrastructure tools. Unified format for deployment. High-performance model serving. DevOps best practices baked in. The service uses the BERT model trained with the TensorFlow framework to predict movie reviews' sentiment. DevOps-free BentoML workflow, from prediction service registry, deployment automation, to endpoint monitoring, all configured automatically for your team. A solid foundation for running serious ML workloads in production. Keep all your team's models, deployments, and changes highly visible and control access via SSO, RBAC, client authentication, and auditing logs.
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AWS Neuron
It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP).
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AWS Deep Learning AMIs
AWS Deep Learning AMIs (DLAMI) provides ML practitioners and researchers with a curated and secure set of frameworks, dependencies, and tools to accelerate deep learning in the cloud. Built for Amazon Linux and Ubuntu, Amazon Machine Images (AMIs) come preconfigured with TensorFlow, PyTorch, Apache MXNet, Chainer, Microsoft Cognitive Toolkit (CNTK), Gluon, Horovod, and Keras, allowing you to quickly deploy and run these frameworks and tools at scale. Develop advanced ML models at scale to develop autonomous vehicle (AV) technology safely by validating models with millions of supported virtual tests. Accelerate the installation and configuration of AWS instances, and speed up experimentation and evaluation with up-to-date frameworks and libraries, including Hugging Face Transformers. Use advanced analytics, ML, and deep learning capabilities to identify trends and make predictions from raw, disparate health data.
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