Geospatial data and machine learning are the two powerful technologies that are transforming the way the military operates. The combination of these technologies has given rise to a new field called Geospatial MLOps (Geospatial Machine Learning Operations). Geospatial MLOps enables military organizations, intelligence agencies, and key industrial players to develop state-of-the-art AI models. These models enable analysts to process large volumes of remote sensing imagery and commanders to shorten the path to critical decision-making.
However, the adoption of Geospatial MLOps in defense comes with its own set of challenges. This ebook sums up the research and industry know-how of Deep Block experts and explores the current trends and challenges associated with the adoption of Geospatial MLOps in the defense sector.
At Omnis Labs, the company behind Deep Block, our steadfast vision is to actualize a future where artificial intelligence is not a far-fetched idea, but a ubiquitous reality. We fervently believe that everyone should have the opportunity to delve into the realm of AI and construct their own models. To that end, we are pioneering one of the foremost AI collaborative ecosystems. Our mission is to empower individuals and businesses alike to seamlessly harness the power of AI and revolutionize the way we live, work, and interact.
I hope you have a great read and that this ebook helps you shed a light on new and fascinating AI technologies.
Summary
- Chapter 1: Introduction to Geospatial MLOps.
- Chapter 2: History and modern trends in Geospatial MLOps in Defense.
- Chapter 3: The benefits of using Geospatial MLOps in Defense.
- Chapter 4: The challenges of Geospatial MLOps adoption in Defense.
- Chapter 5: Geospatial MLOps Workflow.
- Chapter 6: Geospatial data acquisition in Geospatial MLOps.
- Chapter 7: Model development in Geospatial MLOps.
- Chapter 8: Model training in Geospatial MLOps.
- Chapter 9: Model deployment in Geospatial MLOps.
- Chapter 10: Model monitoring and maintenance in Geospatial MLOps.
- Chapter 11: Data management in Geospatial MLOps.
- Chapter 12: Securing data labeling practices.
- Chapter 13: The future of Geospatial MLOps.
