The best AI teams aren’t blocked by data engineering. They just use Tecton to turn their data into smarter models and predictions.
TRUSTED BY TOP ENGINEERING TEAMS
Easily turn raw data into production features (e.g. aggregations, attributes, embeddings), manage them at scale, and serve them for any Predictive AI or Gen AI application, on your infrastructure-of-choice.
Get new models to production 80% faster with Tecton automating all of the data pipelines under the hood.
Improve model performance with rapid feature experimentation and features served with 100% accuracy.
Lower the cost of infrastructure, turning expensive workloads into highly efficient data services.
Tecton’s Python-native, declarative framework simplifies feature engineering for structured and unstructured data
# Define features from any source
Declare the desired transformation for streaming, batch, and real-time data via a simple intuitive Python framework.
# Create training data in seconds
Generate historically accurate point-in-time training data with Tecton fetching the right historical features
# Apply features as code instantly
Send features to production, back-filling and forward-filling to an online/offline store via a single CLI command.
# Define
@stream_feature_view(
source=transactions,
entities=[user],
online=True,
offline=True,
features=[Aggregate(“amount”,“mean”,td(mins=30)])
def stream_features(transactions):
return df[[“user_id”, “timestamp”, “amount”]]
# Train
training_data = stream_features.get_features_for
_events(training_events)
$ tecton workspace select prod
$ tecton apply┃
Tecton automates and orchestrates accurate and reliable pipelines to materialize and serve features to your models. It’s like having a team of data engineers running and tuning your infrastructure.
Control pipeline sprawl and manage your data easily with Tecton’s enterprise feature store. It makes it easy to access, reuse, and lifecycle features, with all the security and data privacy built-in.
Get a full picture of every feature defined and used for every model and AI application. No rogue pipelines or sprawl.
Discover features, detect and remove duplicates, and share across teams to accelerate development and save costs.
Easily track feature changes, and prevent unintended modifications.
Protect sensitive data with SAML 2.0, RBAC, 2FA, and encryption.
Monitor feature health and freshness, data quality, and serving latencies.
Tecton’s highly performant serving system handles the strictest freshness, throughput, and latency requirements. And, our platform ensures data is 100% accurate, so no training-serving skew.
Tecton’s real-time serving infrastructure can compute, join, and retrieve features at 100,000 RPS in under 5 milliseconds.
Tecton processes features on-the-fly when the application requires real-time data freshness with millisecond latency.
Tecton ensures point-in-time correctness and avoids training-serving skew across the online and offline stores.
Tecton’s API makes it simple to access features, abstracting access and complexity of the underlying data stores.
Tecton’s data pipelines are architected for mission-critical use, backed with an enterprise SLA.
Tecton can easily create and manage training data sets at petabyte scale through its batch serving infrastructure.
