Machine learning models take weeks to train; Activeloop and NVIDIA reduce that time to hours at half the cost

Mountain View, CA – August 4th, 2020 In a case study now available online, Activeloop (, a Y Combinator-backed startup, is announcing a major success in helping an early customer, IntelinAir, improve the efficiency of their AI analysis of aerial footage. Activeloop’s software builds plug-and-play data pipelines for unstructured data. The software helps data scientists streamline their data aggregation and preparation, and automates and optimizes their training of machine learning models. Together with NVIDIA, Activeloop has achieved a massive reduction in the time-to-value and cost of machine learning / deep learning efforts. The case study documents a breakthrough in the field of aerial imagery with their joint customer IntelinAir, a leading crop intelligence firm.


Activeloop’s solution is becoming available just in time for the exploding artificial intelligence and advanced machine learning market, projected to grow up to $281.24 billion by 2026 with CAGR of 37.95%. This coincides with the massive growth of data available to be analyzed by AI. All data generated by the end of 2020 will be about 40 trillion gigabytes (40 zettabytes), with IBM estimating that 90% of it has been created over the past 2 years. As data gets bigger faster than ever, translating it into actionable insights is becoming increasingly difficult and expensive. As a result, the effort needed to set up a new model and get it running efficiently can be beyond the reach of many teams who could otherwise benefit from machine learning. Existing solutions often have large cloud storage and processing costs. These solutions can’t be made more efficient without radical changes.


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