Sold by: Fennel
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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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
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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
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.ai
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
Real-time and Batch Feature Engineering
Supports unified code execution for both batch and real-time feature computation without requiring separate implementations.
Feature Quality Monitoring and Governance
Provides built-in tooling for prevention and detection of data and feature quality issues in production ML systems.
Feature Authoring and Computation
Enables authoring, computing, and storage of ML features with integrated serving capabilities.
Self-Service Feature Engineering Workflows
Allows data science and ML teams to independently manage end-to-end feature engineering workflows from development to production deployment.
Cost-Optimized Data Processing
Designed to handle large-volume data processing with cost efficiency comparable to or better than hand-rolled systems.
SQL-Based Pipeline Development
Cloud-native platform enabling pipeline development, testing, and deployment using SQL without requiring complex code, with automated orchestration and scheduling.
Streaming and Batch Data Ingestion
Continuous ingestion and replication of data from multiple sources including PostgreSQL, SQLServer, Kinesis, and file systems to Iceberg lakehouse and data warehouse systems.
Iceberg Live Tables with Automated Optimization
Managed transformation layer that automates task orchestration, compute scaling, data quality validation, schema evolution, and file system optimization for Iceberg tables.
Adaptive Iceberg Optimizer
Optimization engine that profiles data files and write patterns to automatically determine optimization techniques, file selection, and frequency to improve query performance and reduce storage costs.
Data Observability and Monitoring
Built-in monitoring and observability capabilities for detecting operational and data-related issues including volume changes, data value drift, and schema evolution with alerting functionality.
Native Feature Store
Platform includes a native feature store designed to support production-grade machine learning systems with real-time decision capabilities.
Python-Based Feature Pipeline Authoring
Enables data, AI, and ML teams to author feature pipelines using Python programming language for enterprise-grade infrastructure deployment.
Real-Time Feature Serving
Supports serving fresh features in real time with versioning, branching, and full observability for low-latency inference requirements.
Point-in-Time Correctness
Provides point-in-time correctness capabilities for building training datasets with high throughput batch offline queries.
Production ML Use Case Support
Supports production machine learning use cases including fraud detection, payments and underwriting, recommender systems, search and ranking, and growth decisioning.
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