MLOps in practice: how to scale your geospatial practice with cloud-based shared MLOps platform

The key to drive innovation in EO science and applications, boosting geospatial mass adoption and in turn ‘geo-enabling’ companies, researchers and institutions, is moving away from complex, inefficient, and expensive workflows and making fundamental changes in ML practices. This is where geospatial MLOps, and platforms such as Picterra play a crucial role: cloud-native, shared platforms offer user friendly and efficient interfaces, smart toolkit and features paired with auto-scaling infrastructure and state of the art deep learning architecture. They allow to create and operate geospatial ML models at scale, enabling organizations to complete geospatial ML projects faster than ever before.

MLOps platforms systemize the process of building and training experimental machine learning models as well as translating them into production. This workflow efficiency empowers teams working with massive datasets, and allows organizations to leverage data analytics for decision-making and building better customer experiences.

Achieving productivity and speed requires streamlining and automating processes, as well as building reusable assets that can be managed closely for quality and risk. When significant model drift is detected, the ability to retrain and redeploy ML models in an automated fashion is crucial to ensure business continuity.

Shared platform, managed infrastructure, and integrable architecture results in streamlined pipelines and straightforward integration. This agility reduces the time to value and frees up time to serve more use cases, leading to increased value to the business. Companies implementing geospatial MLOps can speed up model training times, dramatically improve accuracy, and go from an idea to a live solution in just days – without increasing headcount or technical debt. Over time, they will also collect a library of strategic ML assets that will enable them to act on timely data - fast.

Using Picterra as a prime example of geospatial ML platform built with MLOps processes in its core, we will dive into how it facilitatesthe key steps of ML workflows incl: 

• Direct access to a diverse range of satellite imagery sources via the platform ie. Sentinel-1/2, Planetscope, open aerial imagery campaigns, ingesting WMS//XYZ server streams.
• Compatibility with any geospatial imagery sources (e.g. Optical, SAR, hyperspectral, thermal infrared, etc.) and possibility to connect to data cloud storage or directly upload via web interface, besides the above mentioned images servers.
• A unequalled MLOps interface to prototype the extraction of new information from imagery around any custom defined use case ie. biodiversity monitoring, crops mapping and classification, assets management and many more. Trained model are directly served and made available for inference at large scale.
• Extensive toolset on explainable & interpretable AI which is bringing robustness & efficiency in creating geospatial Machine Learning models for example dataset exploration 
• Fast turnaround time in creating and validating Machine Learning models to save time and resources, thanks to the auto-scaling infrastructure leveraging Kubernetes and an intuitive interface for fast prototyping.
• A unique set of advanced GIS pre/post-processing tools to manage imagery and the geospatial outputs extracted.
• A complete API interface and Python library to further integrate with existing workflows or softwares (e.g. ESRI ArcGIS, Safe FME, etc.)