Deliberate Machine Learning. Now.

Deliberate Machine Learning. Now.

We help you create Machine Learning products faster.

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360° transparency

360° transparency

As Machine Learning models get stuck, estimates of progress shift and benefits become uncertain. We can help you to stay on track with value focused product planning and low friction team communication.

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Insights from real customers

Insights from real customers

Machine Learning products suffer in development for too long, lacking real-life feedback from those who really matter. With Hypergolic’s support you can collect valuable guidance on the right direction from early on in the product lifecycle.

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Continuous improvements

Continuous improvements

We created “Lean Machine Learning” to overcome the ad-hoc and experimental nature of many data products. The “Evaluate - Modify - Deploy” loop enables teams to focus on rapidly solving real problems with Machine Learning.

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How can we help you?

Send us any questions or problems you have and we will get back to you straight away.

Latest Posts

How to solve Machine Learning problems for production? (Part 2)

How to solve Machine Learning problems for production? (Part 2)

Machine Learning in industrial settings operates in a different context than in academia. To increase success, one must deviate from the original Waterfall-like method of conducting modelling and adopt a more flexible approach. This is what we describe as “Lean in ML”.

, by Laszlo Sragner
How to solve Machine Learning problems for production? (Part 1)

How to solve Machine Learning problems for production? (Part 1)

Machine Learning in industrial settings operates in a different context than in academia. To increase success, one must deviate from the original Waterfall-like method of conducting modelling and adopt a more flexible approach. This is what we describe as “Lean in ML”.

, by Laszlo Sragner
The importance of a Data Acquisition Team

The importance of a Data Acquisition Team

One of the key and most overlooked aspect of Machine Learning is data labelling. I wrote about this here before, most recently in “Data Science Risk Categorisation” but as I was collecting my thoughts for our new e-book “Machine Learning Product Manual” I decided to revisit the topic one more time.

, by Laszlo Sragner