What is Tecton?
It is a fully-managed, cloud native feature platform that operates and manages the pipelines that transform raw data into features across the full lifecycle of an ML application.
Tecton is a tool in the Machine Learning as a Service category of a tech stack.
Tecton Integrations
Tecton's Features
- Feature Pipelines - automatically compute and orchestrate the feature transformation process with unified batch and real-time abstractions. Tecton includes efficient pre-engineered pipelines that compute windowed aggregations on batch and real-time data with a single line of code
- Feature Store - store features in an offline store to optimize for large-scale retrieval during training and an online store for low-latency retrieval during online serving. Easily generate accurate training data through a Python SDK and backfill feature data. Serve data at very high scale (over 100,000 QPS) and low latency (under 100ms) through a REST endpoint. Tecton eliminates train-serve skew by ensuring consistency across training and serving environments, and also eliminates data leakage through correct time-travel
- Feature Repository - Manage features as files in a git repository using a declarative framework. Deploy features with confidence by integrating CI/CD processes and unit testing your features before deploying to production. Manage dependencies of features across models and version-control features
- Monitoring - Monitor the health of feature pipelines and automatically resolve issues that could produce stale feature data. Control costs by tracking the computation and storage costs for each feature
- Sharing - Discover features through an intuitive Web UI and produce new production-grade models with existing features with a single line of code. Break down silos, increase collaboration between data scientists, data engineers, and application engineers. Eliminate duplication across the ML data development cycle
Tecton Alternatives & Comparisons
What are some alternatives to Tecton?
Amazon SageMaker
A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
Azure Machine Learning
Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
Amazon Machine Learning
This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
Algorithms.io
Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables
Amazon Elastic Inference
Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.
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