Audience
Developers and professionals interested in a solution to streamline their model observability
About Superwise
Get in minutes what used to take years to build. Simple, customizable, scalable, secure, ML monitoring. Everything you need to deploy, maintain and improve ML in production. Superwise is an open platform that integrates with any ML stack and connects to your choice of communication tools. Want to take it further? Superwise is API-first and everything (and we mean everything) is accessible via our APIs. All from the comfort of the cloud of your choice. When it comes to ML monitoring you have full self-service control over everything. Configure metrics and policies through our APIs and SDK or simply select a monitoring template and set the sensitivity, conditions, and alert channels of your choice. Try Superwise out or contact us to learn more. Easily create alerts with Superwise’s ML monitoring policy templates and builder. Select from dozens of pre-build monitors ranging from data drift to equal opportunity, or customize policies to incorporate your domain expertise.
Pricing
Starting Price:
Free
Free Version:
Free Version available.
Company Information
Superwise
Israel
superwise.ai/
Videos and Screen Captures
Aporia
Create customized monitors for your machine learning models with our magically-simple monitor builder, and get alerts for issues like concept drift, model performance degradation, bias and more. Aporia integrates seamlessly with any ML infrastructure. Whether it’s a FastAPI server on top of Kubernetes, an open-source deployment tool like MLFlow or a machine learning platform like AWS Sagemaker. Zoom into specific data segments to track model behavior. Identify unexpected bias, underperformance, drifting features and data integrity issues. When there are issues with your ML models in production, you want to have the right tools to get to the root cause as quickly as possible. Go beyond model monitoring with our investigation toolbox to take a deep dive into model performance, data segments, data stats or distribution.
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Amazon SageMaker
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.
Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost. Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps. SageMaker Studio gives you complete access, control, and visibility into each step required.
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IBM Cloud Pak for Data
The biggest challenge to scaling AI-powered decision-making is unused data. IBM Cloud Pak® for Data is a unified platform that delivers a data fabric to connect and access siloed data on-premises or across multiple clouds without moving it. Simplify access to data by automatically discovering and curating it to deliver actionable knowledge assets to your users, while automating policy enforcement to safeguard use. Further accelerate insights with an integrated modern cloud data warehouse. Universally safeguard data usage with privacy and usage policy enforcement across all data. Use a modern, high-performance cloud data warehouse to achieve faster insights. Empower data scientists, developers and analysts with an integrated experience to build, deploy and manage trustworthy AI models on any cloud. Supercharge analytics with Netezza, a high-performance data warehouse.
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WhyLabs
Enable observability to detect data and ML issues faster, deliver continuous improvements, and avoid costly incidents.
Start with reliable data. Continuously monitor any data-in-motion for data quality issues. Pinpoint data and model drift. Identify training-serving skew and proactively retrain. Detect model accuracy degradation by continuously monitoring key performance metrics. Identify risky behavior in generative AI applications and prevent data leakage. Protect your generative AI applications are safe from malicious actions. Improve AI applications through user feedback, monitoring, and cross-team collaboration.
Integrate in minutes with purpose-built agents that analyze raw data without moving or duplicating it, ensuring privacy and security. Onboard the WhyLabs SaaS Platform for any use cases using the proprietary privacy-preserving integration. Security approved for healthcare and banks.
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Product Details
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SaaS
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