Tempora makes it easy to transform raw data into AI-ready data
Tempora allows data science and ML teams to generate AI-ready batches directly from where their data lives, enabling pain-free data preparation whilst maintaining full governance.
By streamlining ML data prep workflows and connecting seamlessly to enterprise data stores, Tempora delivers production-grade ML pipelines for generating model-ready data.
We're Rethinking Data Preparation
for the Modern Enterprise
From ad-hoc pipelines stitched together with brittle Python scripts or unruly Jupyter notebooks, to data exports that break governance and obscure lineage, data preparation for ML is fraught with complexity.
At Tempora, we're rethinking how ML data preparation works from the ground up.
Our Core Principles
Treat Data Prep as Infrastructure
Data preparation deserves the same degree of engineering rigor as the rest of your ML stack. Tempora eliminates the brittle glue code and mounting technical debt that slow teams down, replacing them with robust, scalable ML pipelines that you can trust in production.
Integration, Not Duplication
Traditionally the only way to use enterprise data for machine learning was to export it — duplicating data and eroding governance in the process. Tempora integrates natively with leading data stores, enabling ML data prep directly where your data lives.
Lineage All The Way Down
Data lineage is often lost when data is exported — making it hard to reproduce experiments or trust results. Tempora maintains complete lineage for every dataset, transformation and batch, making reproducibility and traceability first-class features, not afterthoughts.
Self-Hosted By Design
Most ML platforms run your workloads on their cloud, putting data governance and control out of your hands while charging a premium for compute. By contrast, Tempora runs entirely on your infrastructure, giving you full control over your data and the compute resources that power it.
About Us
Nick Hughes
Founder & CEO
Nick founded Tempora to address the lack of purpose-built tools for ML data preparation. Before Tempora he worked as an independent ML consultant where he saw first-hand the challenges that data scientists faced in preparing data for ML.
Previously, Nick was the CEO and co-founder of Lecida – an industrial AI startup, and before that led early ML efforts at iRhythm Technologies. He holds a DPhil in Machine Learning and Signal Processing from the University of Oxford and undertook postdoctoral research at Stanford University.