Tecton
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About
Tecton is a feature platform that simplifies turning data into AI context. It helps data scientists and AI/ML engineers define features, embeddings, and prompts from any data source, automating accurate and reliable pipelines. Tecton automates data pipelines, enables rapid feature experimentation, and ensures data accuracy, reducing the cost of infrastructure. It provides features like real-time serving, training data management, and real-time computed context. The platform supports various cloud providers and offers security measures like SSO and data encryption.
Platform
Features
• prompt engineering
• training data generation
• feature store
• embeddings generation
• feature engineering
• real-time serving
• fine-tuning datasets
• time-window aggregations
FAQs
What is Tecton?
Tecton is a feature platform. Our feature store is focused on storing data offline and online, keeping data consistent, and providing an API to consume the data.
Who should use Tecton?
Tecton is designed for machine learning engineers, data engineers, and data scientists looking to use a feature platform to build optimized batch, streaming, and real-time data pipelines for ML-driven use cases.
How is Tecton used?
The primary use cases for Tecton are real-time ML-driven applications, including recommendation systems, fraud detection, dynamic pricing, search ranking, marketing personalization, or real-time loan approvals.
How do you define feature logic?
Feature logic in Tecton is code-based as opposed to being tuned within the UI. Users can define feature logic in SQL, PySpark, SnowPark, or Python in a notebook or any other Python environment
Does Tecton provide on-demand transformations?
Yes, Tecton provides on-demand transformations. Alongside the feature store, feature repository, and feature monitoring, on-demand transformations are a key component of the Tecton feature platform.
Does Tecton have a library of transformations/aggregations in addition to allowing a code-first approach?
Tecton has native support for time-window aggregations that are most commonly seen in machine learning use cases (min, max, count, sum, variance, etc.).
Can Tecton help with catching feature leakage?
Yes, Tecton’s SDK helps mitigate feature leakage. It accomplishes this by managing feature and data timestamps, and data delay modeling.
Which cloud providers does Tecton support?
Tecton is available on AWS and GCP. Azure support will be available soon.
Is Tecton supported as an air-gapped on-prem solution?
Not currently. As a SaaS solution, Tecton can run in your cloud environment, but not in an air-gapped environment.
Does my data engineering team need to set up an ETL pipeline as a prerequisite to start using Tecton?
You don’t need to set up an ETL pipeline to start using Tecton. Tecton automates that for you.
Is feature data stored in Tecton's account or in a customer's account?
The default is to store offline data in the user’s account and online data in Tecton’s account. We offer other deployment models where data can be stored entirely in the user’s account.
What does Tecton use as the online store?
Tecton supports Redis and DynamoDB on AWS and Redis and BigTable on GCP. The right choice depends on your use case. We typically help teams think it through based on the scale and data profile of their use cases.
If doing model training in Dev and Prod environments, do you typically deploy a single shared instance to have a single source of features?
Only one deployment for dev and prod is required.
Can my team use multiple git repositories that configure a single Tecton registry?
In Tecton, different git repos can map to different Tecton workspaces that can operate in parallel. You can pull from multiple workspaces in production.
How does Tecton secure customer data?
Tecton offers the option of authenticating users via SSO. Access can be granted to users, teams, or services using Access Control Lists (ACLs).
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