AI & ML pipelines

Fresh features and traceable training data

Models are only as trustworthy as the data behind them. Synchronoid records where every value came from, so you can explain a prediction, rebuild a dataset and prove what a model saw.

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The problem

Nobody can say what the model was trained on

Training sets are assembled from many sources by scripts that change over time. Months later, someone asks why a feature had a strange value, and no one can answer.

Feature tables go stale when a feed fails silently, and bad rows slip into training without a trace.

Our approach

Provenance and replay for every dataset

Every field carries its source, fetch time and run id. A training set is a query on the published layer at a known point in time, and the raw layer lets you rebuild it exactly.

Data quality checks block bad data before it reaches a feature table, and alerts tell you when a source goes quiet.

What you get

Built for data you have to explain

Provenance on every value

Ask why a feature looks the way it does and get the source, time and run that produced it.

Point-in-time datasets

Rebuild a dataset as it was on any date from the change history in the published layer.

Quality gates

Checks block bad data from reaching feature tables and training sets.

Replay from raw

Change a transformation and re-run it over stored payloads without calling the source again.

Fresh features

Schedules and sensors keep feature tables current. Sources that fail are paused and flagged.

Lineage graph

See which sources, assets and tables feed each model input in the control room.

How it looks

A feature table with its history attached

Published assets track changes by default, so every version of a feature row is kept with its provenance.

Export snapshots to your warehouse or object storage
Pull records with provenance through the REST API
Stream changes to your feature store with a change feed

pipelines/features.py

import synchronoid as sy

@sy.asset(layer="published", deps=[customer_events])
def customer_features(ctx, customer_events):
    ctx.log.info("building features")
    table = customer_events.aggregate(
        key="customer_id",
        sessions_30d="count(session_id)",
        avg_basket="mean(basket_value)",
    )
    return table.merge(key="customer_id", track_changes=True)

# Why is avg_basket 41.2 for customer c-1042?
prov = customer_features.provenance("c-1042", "avg_basket")
# -> source=shop_events, fetched_at=2026-10-28T02:00Z, run=run_8f3a

“When a model result looked odd, we used to spend days tracing it by hand. Now we open the record, see which feed and which run produced the value, and move on.”

PPriya, Data Science Lead, Mosaic Health Research
Beta customer

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Train on data you can explain

Request access and we will help you connect your first feature sources.

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