Initially funded by leading VCs in the renowned Israeli ecosystem and by the Israeli Innovation Authority, Presenso spent the last 2 years researching and developing its Auto-ML based solution which has 11 patents pending.

Presenso, provider of Machine Learning based solutions for Predictive Asset Maintenance, announced today the availability of its solution's production release. Incorporating the latest advances in Automated Machine and Deep Learning (Auto-ML), the Presenso solution has now been tested by leading industrial manufacturers worldwide.

From Startup to Commercialization
Initially funded by leading VCs in the renowned Israeli ecosystem and by the Israeli Innovation Authority, Presenso spent the last 2 years researching and developing its Auto-ML based solution which has 11 patents pending.

Presenso's system collects immense amounts of data at very high speed from hundreds of machines (thousands of sensors) and streams the data to the Cloud in real-time. Using unique, proprietary deep neural-network architectures, Presenso's analytic engine autonomously interlinks events with components within the machines and ultimately predicts evolving failures. In addition, it provides valuable information about the remaining time to failure and its origin within the machine.

Following this significant investment in R&D, a beta product was launched in early 2017 and was deployed at multiple customers' sites. As the result of extensive testing in multiple production environments, Presenso is now releasing its solution to the wider industrial market.

Initial Success with Leading Industrial Producers
In the last 12 months Presenso has successfully tested its solution within the plants of end-users such as Wien Energy and Total-Eren and with OEMs such as MAN Diesel, Ansaldo Energy and others. With over 20 customers in energy production, chemical processing, oil & gas and pulp & paper, Presenso believes that its Predictive Maintenance solution can address a major challenge faced by industrial producers: how to scale a predictive maintenance solution across an organization.

Applying Automated Machine Learning to Predictive Maintenance
Auto-ML has now been fully integrated into Presenso's Predictive Maintenance solution. As organizations adopt Industry 4.0 practices, many struggle to scale predictive maintenance programs. Presenso automates Machine Learning processes and provides a Software as a Service AI solution that requires no interaction with the plant's engineers and no data scientist to perform the application engineering tasks.

"Our experience shows that AutoML is the best way to automate many of the time consuming and repetitive Machine Learning tasks such as big data preprocessing, feature engineering and model selection and validation. This speeds up customer onboarding and widespread solution adoption," said Deddy Lavid (Ben Lulu), Presenso co-founder and CTO.

"As we continue to demonstrate a successful track record, we plan to expand our offering to additional markets," added Lavid.

Apart from commercial success, Presenso continues to be recognized by numerous industry analysts. Gartner recently recognized Presenso as a Cool Vendor in Artificial Intelligence across the Supply Chain.

About Presenso
Within the exabytes of sensor data generated by industrial machines are micro-patterns that can tell us when a machine is likely to fail. Until now these patterns were undetectable to even the most advanced industrial monitoring tools. Data scientists working to improve machines uptime lacked the tools to find these patterns and industrial plants lacked the data scientists to even try. Presenso develops advanced analytical tools for Predictive Maintenance using data science innovations such as Automated Machine Learning (AutoML). These tools are accessible to maintenance and reliability professionals without the need to hire Big Data experts. Presenso solution is available today for both OEM's which are now developing their Industry 4.0 offerings and to end users operating their own equipment.