Sold by: Snorkel AIÂ
Deployed on AWS
Snorkel's AI Data Development platform enables enterprises to develop and operationalize AI systems by transforming their own proprietary data and domain knowledge into expert evaluation and training data, and can choose between in-house data development or white-glove dataset delivery.
Overview
Snorkel AI empowers AWS customers to scale automation to the iceberg under the surface of valuable but unstructured enterprise data. Our unique approach equips data science and machine learning teams to adapt ML models of all sizes - including multi-billion parameter large language models (LLMs) - with their own data and expertise, accelerating AI application development up to 100x.
Deployed on AWS and leveraging AWS generative AI services, Snorkel Flow enables teams to quickly generate high-quality training sets and models over complex, highly variable, often proprietary and confidential data.
Snorkel Flow integrations with AWS include...
Seamless access to data on AWS using Snorkel Flow's native S3 connector. Prompt and fine-tune LLMs with Bedrock and JumpStart integrations. Accelerated model training, serving, and adaptability with SageMaker. Easy deployment and scalability on EKS. ... and more!
This listing enables organizations with an existing Snorkel Flow platform subscription deployed on AWS to purchase Application Units.
Planning a new AI development project? For a custom proposal reflecting your use cases and internal requirements please contact us directly at info@snorkel.ai .
Highlights
- Develop AI data programmatically - Wrangling your data is crucial and challenging, even with the latest LLMs. Snorkel Flow captures and encodes your domain expertise and automates manual tasks like filtering, curating, labeling, and slicing.
- Increase accuracy with guided error analysis - LLMs often face issues such as producing hallucinations, preventing them from being deployed in enterprise settings. Snorkel Flow gives you the tools to systematically identify errors and accurately develop data slices to improve quality and fosters innovation through multi-persona collaboration. Efficiently harness SME collective expertise by seamlessly collaborating directly with business and data science teams.
- Unify prompting, RAG, and fine-tuning - Snorkel Flow offers access to the latest open-source and closed-source models and state-of-the-art techniques like prompting, retrieval-augmented generation, fine-tuning, instruction-tuning, and pre-training within a robust, enterprise-grade platform. Streamline programmatic data curation and model customization on a single platform and make swift adjustments to fine-tune AI performance to your unique business demands.
Details
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Delivery method
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Hosted Snorkel Flow | Application Units for Snorkel Flow on AWS | $60,000.00 |
Vendor refund policy
Payment obligations are non-cancelable and non-pro-ratable for partial months and fees paid are non-refundable.
Legal
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Support
Vendor support
Snorkel AI Support provides timely resolutions during business hours, 6AM PT through 6PM PT Monday through Friday, excluding U.S. public holidays.
Self-service is available via our Support Portal 24x7 where you can find documentation, how-to guides, best practices and other useful material to solve your machine learning challenges.
For more information please reference Snorkel AI Support and Maintenance Terms. Learn More:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Snorkel AI
By Dataloop AI/GenAI development platform
By Datasaur
Top
100
In Natural Language Processing
Top
50
In Data Preparation
Top
25
In Data Labeling Services, Software Development
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Data Programmatic Development
Automated data wrangling and curation using domain expertise for filtering, labeling, and slicing complex datasets
Machine Learning Model Integration
Native support for accessing and integrating with AWS services like SageMaker, Bedrock, and JumpStart for model training and fine-tuning
Large Language Model Optimization
Advanced techniques for prompting, retrieval-augmented generation, fine-tuning, instruction-tuning, and pre-training across open-source and closed-source models
Error Analysis and Quality Improvement
Systematic error identification and data slice development to enhance model accuracy and performance
Enterprise Data Connector
Native S3 connector for seamless access and processing of proprietary and confidential enterprise data
Data Management
Advanced platform for exploring and analyzing unstructured data from diverse sources with automated preprocessing and embeddings
AI Pipeline Orchestration
Drag-and-drop and code-based interface for creating complex AI workflows with data, models, apps, and human feedback integration
Model Management
Capability to use pre-existing AI models, build custom models, deploy to production, and perform versioning, experimentation, and fine-tuning
Enterprise Security Framework
Comprehensive security controls including RBAC, SSO, 2FA, AES-256 encryption, compliance with GDPR, ISO 27001, ISO 27701, and SOC 2 Type II standards
AI Application Development
Function-as-a-service offering enabling custom code development for complex tasks with direct data and model access without infrastructure setup
Language Model Diversity
Provides access to over 250 language models for automated data annotation and model evaluation
Model Deployment Capability
Supports simultaneous model comparison, inference quality assessment, and benchmarking across different language models
Custom Model Development
Enables proprietary model creation and deployment through integrated Data Studio environment
Automated Data Annotation
Implements robo-labeling techniques using pre-trained or custom ontologies and datasets
Compliance and Security
Maintains SOC 2 Type 2, HIPAA, and GDPR compliance with robust data protection mechanisms
Standard contract
No
Customer reviews
0 ratings
0 AWS reviews
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1 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
Tanbir G.
Data handler
Reviewed on Nov 09, 2023
Review provided by G2
What do you like best about the product?
It seamlessly integrates with popular machine learning libraries such as PyTorch and Tensor flow. It is a powerhouse when it comes to handle large databases. It's real strength is to handle data that is tricky and not flawless. Additionally, it's flexibility is a huge advantage.
What do you dislike about the product?
Snorkel is challenging to use as it requires understanding the concept of data labeling and weak supervision. It is not the go-to option for tasks that requires data of the highest quality. Setting up Snorkel requires effort.
What problems is the product solving and how is that benefiting you?
It enables you to handle data that is not perfect that allow to make best use of the resources in tasks where obtaining flawless data is a challenge. It helped me a lot to develop accurate machine learning model.