Sold by: FennelÂ
Fennel is a modern feature engineering platform that helps you author, compute, store, serve, monitor & govern both real-time and batch ML features.
Overview
Fennel is a modern real-time feature engineering platform inspired by the ethos of PyTorch and is architected in the service of the following four design goals:
-Easy to install, learn & use - be approachable to data science / ML teams and to make them fully self-dependent for end-to-end feature engineering workflows.
-Maintain data/feature quality via best practices - production ML systems often get plagued by data/feature quality issues. Prevention & detection of such issues (via best practices & tooling) should be a first-class citizen, not an afterthought.
-Work equally well for batch & real-time - ML is transitioning from being predominantly batch-oriented today to being more real-time. Fennel should support the exact same code working equally well for any combination of batch, streaming, & real-time computation.
-Keep cloud costs low - feature engineering often requires dealing with large volumes of data and gets very costly. For any workload, Fennel aims to be at least as cost-effective as a hand-rolled system while also being more powerful & easier to use.
Highlights
- Makes data science teams self-serve in shipping both batch and realtime features to production.
- No more feature or data bugs.
Details
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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, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Fennel Platform | Access to Fennel Platform with support, provisioning, management etc. | $60,000.00 |
Dimension | Description | Cost/unit |
|---|---|---|
core_hours | # of Core Hours | $0.12 |
Vendor refund policy
All fees are non-refundable and non-cancellable except as required by law.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
Vendor resources
Support
Vendor support
Email/slack support services are available 24x7 depending on the criticality. Response SLA of 1 hr 24x7 for production critical issues and for non-critical issues, response SLA is 1 business day.
support@fennel.aiÂ
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.
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AI generated from product descriptions
Feature Engineering Platform
Supports both real-time and batch feature computation with unified code execution
Data Quality Management
Provides built-in prevention and detection mechanisms for data and feature quality issues
Computational Flexibility
Enables seamless computation across batch, streaming, and real-time data processing paradigms
Platform Architecture
Designed with inspiration from PyTorch, focusing on approachability and self-service capabilities
Workflow Optimization
Supports end-to-end feature engineering workflows with integrated authoring, computation, storage, serving, monitoring, and governance
Feature Lifecycle Management
Manages complete machine learning feature lifecycle from development to production deployment
Multi-Source Data Integration
Supports feature building across batch, streaming, and real-time data sources with cross-source data joining capabilities
Enterprise-Grade Infrastructure
Provides scalable infrastructure with enterprise-level security, monitoring, and service level agreements
Feature Deployment Automation
Enables instant feature deployment with 100% offline and online parity and automated governance
Collaborative Feature Development
Offers centralized platform for data practitioners to collaboratively build, share, and reuse machine learning features
Data Ingestion
Supports continuous ingestion of streaming and batch data from multiple sources including PostgreSQL, SQLServer, and Kinesis
Data Transformation
Enables data transformations using SQL with automated task orchestration, scaling, and data quality validation
Table Management
Provides Iceberg Live Tables with native schema evolution and file system optimization capabilities
Performance Optimization
Implements adaptive Iceberg data file optimizer that profiles data files and write patterns to improve query performance and reduce storage costs
Operational Monitoring
Offers built-in task monitoring and data observability features to track volume changes, data value drift, and schema evolution
Standard contract
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