The combined market for Earth Observation (EO), GIS and spatial analytics, and MLOps is expected to reach $300 billion by 2025. With the increased commercial adoption of satellite and aerial imagery for a wide range of use cases across industry, the demand for scalable geospatial apps and services continues to rise.
In a recent LinkedIn article, our CEO and Co-Founder Pierrick Poulenas shared his thoughts on this growth and the future of the geospatial industry.
As he outlined, meeting this demand can be challenging, especially when traditional machine learning (ML) life cycles can take months to reach production, with the success rate across industry embarrassingly low. That’s where geospatial MLOps comes in, revolutionizing and accelerating the geospatial ML life cycle as DevOps did for software development.
It’s time to move away from using traditional ML methodology to develop feature extraction models for EO images. To boost geospatial intelligence mass adoption we have produced a complimentary guide for enterprises developing geospatial apps and services that outlines a simple three-step approach to MLOps monetization.
Operationalize
Data needs to be organized and labeled before its value can be realized. However this takes time, which means by the time the task is complete, there is a risk the data is potentially already out of date and opportunities could be lost/missed. Download the full guide to discover how geospatial MLOps allows you to stop laboring over tedious machine learning tasks and start bringing ideas to life faster while focussing on higher-value work.
Productionize
Communication breakdowns, lack of resources, and lengthy lead times are three key reasons why ML projects don’t make it into production. Even after years of investment, many organizations find they aren’t moving any faster. Download the full guide to learn how companies implementing geospatial MLOps can go from an idea to a live solution in just days – without increasing headcount or technical debt.
Monetize
Businesses are interested in the technology, the outcomes, and the revenue promised by geospatial MLOps. But aside from hiring data scientists, the path to profit and success is rarely straightforward, and predicting ROI is problematic. Download the full guide to discover how geospatial MLOps gives ML teams a scalable, reliable way to operate geospatial models in production and continuously develop them.
Discover how your data science team can bring models into production in days rather than months and download the full 20-page guide “Three steps to monetizing geospatial MLOps” to see all of our recommendations and insights.
