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The shift to the cloud has revolutionised the way data scientists work where the cloud has opened up a whole host of machine learning tools, pushing the capabilities of machine learning. One of the most popular tools is none other than the famous Databricks.

A fast and effective way of performing geospatial operations within Databricks. This article explores 3 use cases that utilise the library Mosaic.

Many businesses recognise the benefits of Artificial Intelligence (AI) and Machine Learning (ML) for boosting the potential of their data. This trend has been growing over the last few years. A Machine Learning project requires data scientists to implement machine learning models. In today’s market, more demand than supply has meant data scientists can often be expensive to hire which means getting ML projects off the ground can be tedious in terms of costs and time spent on implementing. This is where the solution accelerators come in handy by accelerating ML projects from an idea to a proof of concept within weeks. This article hopes to introduce the concept of accelerators to anyone thinking of taking on a machine-learning project.

This article gives a simple example of how to produce text clustering using a combination of word embeddings and density-based clustering methods. This technique can be used across a large range of industries, providing text segmentation and categorization.