One of the goals of Loud ML is to make machine learning simple and accessible to everyone. That's why, last summer, we created our TICK-L stack 1-Click ML tool -- point and click machine learning right inside Chronograph (part of the InfluxData TICK stack -- Telegraf, InfluxDB, Chronograf and Kapacitor, with added Loud ML machine learning). Before we made this tool, devops had to compose a model manually -- feature by feature, without any data visualization to preview input data, nor the ability to see the effect of some parameter settings, such as grouping by interval. There was also no data visualization on measurements, tag values or training time range selection either, yet can have a big impact on how the model fits the data for good predictions. Building a model from a data visualization was not a common way to do ML.

This is a guest post from my colleagues Naveen Swamy and Joseph Spisak. Machine learning is a field of computer science that enables computers to learn without being explicitly programmed. It focuses on algorithms that can learn from and make predictions on data. Most recently, one branch of machine learning, called deep learning, has been deployed successfully in production with higher accuracy than traditional techniques, enabling capabilities such as speech recognition, image recognition, and video analytics. This higher accuracy comes, however, at the cost of significantly higher compute requirements for training these deep models.

This is a guest post from my colleagues Naveen Swamy and Joseph Spisak. Machine learning is a field of computer science that enables computers to learn without being explicitly programmed. It focuses on algorithms that can learn from and make predictions on data. Most recently, one branch of machine learning, called deep learning, has been deployed successfully in production with higher accuracy than traditional techniques, enabling capabilities such as speech recognition, image recognition, and video analytics. This higher accuracy comes, however, at the cost of significantly higher compute requirements for training these deep models.