Making Space For IoT Machine Learning With Spatio-Temporal Data
January 5, 2023
Devices that share their locations, timestamps, and other spatio-temporal data with each other are providing enterprises with increasingly exponential amounts of data. In recent years machine learning has begun to provide highly useful predictions and classifications from complex datasets for a variety of IoT devices with strong time and space dependencies. IoT devices and the large spatial and/or spatio-temporal (ST) datasets they generate have necessitated research in this area of Machine Learning (ML). From automating transportation planning to developing meteorological prediction profiles, there is a lot to be accomplished from using ML with spatio-temporal data sets.
Model Deployment Uncertainties for IoT Spatio-Temporal Data
Despite the rise of IoT devices and the influx of spatial and spatio-temporal data, MLOps is far from primed to solve the obstacles that make this data unsuitable for conventional deployment methods. One major challenge is the complexity of the datasets. As opposed to traditional data, spatial and spatio-temporal data is constantly streaming and comes in a range of forms like instant and interval temporal data or vector and raster spatial data. That said, even with large non-linear data structures it is still possible to attain accurate predictions with advanced ML solutions. Convolution Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) create mappings that link the parameter and the observation zones to instantly derive practical insights. For example, CNNs maintain the structural integrity of images in their initial layers. This is what makes it possible to visualize the feature maps and see what is prioritized, which is very useful for debugging.
For the purpose of ML, spatial and spatio-temporal data is typically split into subsets for model training and initial validation. However, the data is often not randomly distributed in space and time leading to biased estimates of prediction errors. This flawed data with its unpredictable biases from uncertainties and data gaps making it somewhat unreliable and unscalable. Furthermore, many ML models are black boxes, with little to no clarity on the reasoning behind their generated predictions and classifications. To counter issues of unpredictability and uncertainty you can integrate occlusion analysis, interpretable local surrogates, layerwise relevance propagation, integrated gradients, and explainable models. Uncertainties can similarly be resolved in probabilistic frameworks through multi-level Bayesian hierarchical inferences and Bootstrap ensemble to create models through sequences of conditional and marginal distributions.
Spatio-Temporal Machine Learning with Breakthrough Insights
Wallaroo is an innovative ML solution equipped with cutting-edge ML architecture that integrates advanced features, allowing you to characterize conditional distributions. Wallaroo allows for easy deployment of complex non-linear ST models in production. It features efficient runtime for batch and streaming analysis of fast data and big data with minimum latency. It is also designed to pre-process different Spatial and ST data types into formats that are ready for deployment.
Ideally designed for Spatial and ST data applications that prioritize the monitoring, explainability, and observability of data, Wallaroo additionally offers Advanced Observability and Explainability of data through real-time analytics, data validation, data drift alerts, and model anomaly detection. The ability to generate advanced model insights as well as comprehensive audit logs provides the much-needed transparency for accurate and reliable predictions as well as error management. Moreover, Wallaroo supports experimentation capabilities for Spatial and ST Data, which allow enterprises to effectively make decisions that drive results.
For simplifying the use of your organization’s spatial and spatio-temporal data, Wallaroo offers the ultimate ML deployment solution designed for explainability and interpretability, providing more flexibility beyond the simple transformations offered in traditional statistical machine learning systems. Sign up for the Wallaroo Community Edition today to try out the Wallaroo features for free.
