Artificial Intelligence (AI) is quickly becoming integrated into many enterprise environments. Managing these production-level AI systems with best practices, proper architecture, redundancy, and scalability is a necessary step to ensure performance at scale. Reliability, ease of operation, and maintainability are increased when implementing the proper development operations standards.
AWS Services
Purpose-built cloud products
AWS Solutions
Ready-to-deploy solutions assembling AWS Services, code, and configurations
Partner Solutions
Software, SaaS, or managed services from AWS Partners
Total results: 5
- Publish Date
-
Gretel.ai
Gretel's synthetic data platform enables the development of domain-specific AI models for creating data that mirrors, boosts, or simulates real-world data without the privacy concerns. Engineered for enterprises and developers, Gretel supports various models and data types, and integrates seamlessly with MLOps pipelines. The platform can be used across a multitude of industries such as financial services, healthcare, and public sector. Gretel offers advanced capabilities for evaluating and validating the quality of the synthetic data, and is available either as a managed cloud service or in a hybrid cloud environment. -
WhyLabs AI Observatory: The Data and ML Monitoring Platform
WhyLabs is the essential AI Observability Platform for model and data health. It is the only machine learning monitoring and observability platform that doesn't operate on raw data, which enables a no-configuration solution, privacy preservation, and massive scale. Machine learning engineers and data scientists rely on the platform to monitor ML applications and data pipelines by surfacing and resolving data quality issues, data bias, and concept drift. These capabilities help AI builders reduce model failures, avoid downtime, and ensure customers are getting the best user experience. With out-of-the-box anomaly detection and purpose-built visualizations, WhyLabs eliminates the need for manual troubleshooting and reduces operational costs. The platform can monitor tabular, image, and text data. It integrates with many popular ML and data tools including Pandas, Apache Spark, AWS Sagemaker, MLflow, Flask, Ray, RAPIDS, Apache Kafka, and more. To learn more about what data types WhyLabs can work with and which tools we integrate with, check out the whylogs GitHub page: https://github.com/whylabs/whylogs WhyLabs was created at the Allen Institute for Artificial Intelligence (AI2) by Amazon Machine Learning alums and is backed by Andrew Ng's AI Fund. For custom pricing, EULA, or a private contract, please contact AWSMarketplace@whylabs.ai for a private offer. -
Hugging Face Platform
The Hugging Face Platform enables premium features for your organization on the Hugging Face Hub, including Inference Endpoints, Spaces Hardware Upgrades, and AutoTrain. With Inference Endpoints, you can securely deploy models from the Hugging Face Hub and custom containers on managed autoscaling infrastructure: - Optimized for LLMs: high throughput and low latency, powered by Text Generation Inference. - Deploy models as production-ready APIs with just a few clicks. No MLOps, no infrastructure to manage. - Automatic scale to zero capability for maximum cost efficiency. - Security first: we support direct connections to your private VPC. We have the SOC2 Type 2 certification and offer GDPR and BAA data processing agreements. - Out-of-the-box support for Hugging Face Transformers, Sentence-Transformers, Diffusers, and easy customization. Run inference at scale with any Machine Learning task and library. With Spaces, you can easily create and host any Machine Learning application, GPUs and batteries included: - Build ML apps and host them on Hugging Face. - Showcase projects, create an ML portfolio, and collaborate with others in your organization. - Wide range of frameworks supported: Gradio, Streamlit, HTML + JS, and many more with Docker. - Upgrade to GPU and accelerated hardware in just a few clicks. With AutoTrain, you can train state-of-the-art models with just a few clicks: - No-code tool to train state-of-the-art NLP, CV, Speech, and Tabular models without machine learning expertise. - Train custom models on your datasets without worrying about the technical details of model training. All Hugging Face services use a usage-based, pay-as-you-go pricing. Check out our pricing here: https://huggingface.co/pricing Inference Endpoints: https://huggingface.co/pricing#endpoints Spaces: https://huggingface.co/pricing#spaces AutoTrain: https://huggingface.co/pricing#autotrain -
Articul8 GenAI Platform
Articul8’s turnkey, full-stack, vertically-integrated Generative AI (GenAI) software platform enables companies to build, deploy and manage enterprise-grade, secure GenAI applications rapidly and cost-effectively. Articul8’s GenAI platform is vertically integrated and optimized across four key layers (Infrastructure, Data, Model and Application), and includes several pre-built and pre-packaged modules or packs that accelerate the customer GenAI journey and enable the deployment of GenAI applications at scale in a highly compressed ROI timeframe. Articul8’s model layer, ModelMesh™, includes a collection of state-of-the-art (SOTA) large language models (LLMs), probabilistic models, and decision models that are optimized for functionality and size, and are dynamically orchestrated at scale to deliver tangible business outcomes for specific use cases, with best-in-class price performance. -
UST SmartOps
UST SmartOps is a generative AI powered Intelligent Process Automation platform that reimagines your business processes and delivers hyper-scale efficiency. The platform automates document-centric business processes, IT operations, claims processing etc. holistically and reduces cost of operations significantly. It empowers teams to focus on high impact opportunities, boosts efficiency and profitability via orchestrating holistic automation of processes, systems, and data. Built on AWS cloud, the platform offers a comprehensive suite of advanced AI & cognitive techniques, including knowledge graphs, data fusion and computer vision, NLP/NLU, sentiment analysis, anomaly detection, fault prevention etc. Some of the top use cases include – Helpdesk / ServiceDesk automation Automated incident root cause analysis & resolution Invoice processing automation Automated asset transfer validation