MLOps for Earth Observation

From mapping land use to monitoring global supply chains — Opportunities to apply machine learning are plenty but how to continuously develop and serve machine learning models remains as a barrier for many. MLOps seeks to address the operationalization of ML with a set of tools and best practices.

Earth observation poses unique MLOps challenges.

Here’s a few we’ve identified with our partners.

Data sets are GIGANTIC.

When dealing with orbital data, your local machine will grind to a halt very quickly. To train machine learning models with satellite imagery you’ll need access to powerful cloud compute, and preferably you’ll want to have the data as close to the computation as possible.

ML Pipelines get complicated.

Creating a data set for training a model often can have many preprocessing steps like creating cloud-free composite imagery. You’ll want to build a pipeline that can automate all steps, no matter what programming languages are used.

Evaluation requires rigor.

In earth observation, evaluating whether your model works overall or for your specific training data only is a key issue. You’ll often want to evaluate your model not just against your training data but also benchmarking data sets that might be for example more geodiverse.

Versioning is key.

Data is always changing and therefore versioning is critical to understanding if your models stop performing as expected. Whether it’s because of outdated labels or changes in source quality, you’ll want to be able to pinpoint when and how a model was trained.

Introducing Valohai

The MLOps platform for deep learning and unstructured data sets.

Valohai is an MLOps platform for teams that work with the most demanding machine learning problems. The platform makes it easy to build pipelines that automate everything from data extraction to model deployment.

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Valohai manages cloud infrastructure so a data scientist can run any kind of cloud instance with a single click. Our platform can also be installed on any private cloud or on-premise setup.

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Valohai is technology agnostic to ensure that data science teams can integrate pre-processing, training, evaluation and deployment steps together into a cohesive pipeline rather than running steps separately.

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Valohai offers powerful capabilities to build pipelines that cover many training runs with different data sets. Pipelines can be dynamic to evaluate models f.ex. against previously trained models.

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Valohai automatically versions every model but also what code, data and parameters were used to train them to ensure full reproducibility.

Learn more about MLOps best practices and Valohai from our experts.

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FORESTRY, FINLAND

Improving smart-forestry through machine learning

CollectiveCrunch makes use of a wide array of space data sets, optical and other, as well as LIDAR and process data to accurately predict wood quality and quantity. They’ve built a highly-scalable cloud-based GIS solution that is offered as a SaaS package, that provides real-time market intelligence at the click of a button.

Read full case study ➜

OBJECT DETECTION, AFRICA

Changing nature conservation with deep learning

Jacques Marais used machine learning to scan Africa’s elephant population from aerial infrared and color images taken from a plane. The work, which earlier took three weeks to complete, was finalized in only three days with Valohai while the project’s accuracy increased from 56% up to 67% while the overdetection rate dropped dramatically.

Read full case study ➜

At the end of the day, we‘re able to match supply and demand much better than any competitor thanks to Valohai boosting our model development speed by a factor of 2 to 5!

Rolf Schmitz – CEO, CollectiveCrunch

Discover the MLOps best practices.

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