Tecton vs wise.io

Use Wise.io for: Fraud detection, Intelligent sensors, Ad Targeting & Personalization, Genomics, Business Analytics, Finance, Healthcare, Sentiment Analysis;Dead simple machine learning.- Our intuitive, easy-to-use platform for machine learning enables anyone to build and deploy models with a few simple clicks.;A data science marketplace.- With the feature marketplace, we provide companies access to an expansive knowledge base.;State-of the art technology.- Our IP is 10-100x faster and more memory efficient than any other implementation we can find.;From experiment to production.- By breaking the barrier between sandbox learning and large-scale production environments, we decrease the lead time from inception to deployment.;Automated reports.- Every time you build a model, we generate an easy-to-read report detailing the insights gleaned from your data and the performance of your newly minted model.;Public or private cloud.- Our hosted platform makes it easy for businesses to deploy machine intelligence without having to build the infrastructure. For companies with security or latency concerns, we gladly offer an on-premise solution.

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