You can leverage GPUs for compute-intense training tasks and deploy models on CPU or GPU enabled containers. When you use the predefined workflows of Jupyter Lab Notebooks, the app enables you to build, test, and operationalize customized models with the Splunk platform. The app extends the Splunk Machine Learning Toolkit (MLTK) with prebuilt Docker containers for TensorFlow, PyTorch, and a collection of data science, NLP, and classical machine learning libraries. The Splunk App for Data Science and Deep Learning (DSDL), formerly known as the Deep Learning Toolkit (DLTK), lets you integrate advanced custom machine learning and deep learning systems with the Splunk platform. Splunk App for Data Science and Deep Learning *Smart Forecasting Assistant (provides enhanced time-series analysis for users with little to no SPL knowledge and leverages the StateSpaceForecasting algorithm): e.g. Smart Assistants (new assistants with revamped UI and better ml pipeline/experiment management): forecast data center growth and capacity planning. detect outliers in diabetes patient records. * Detect Categorical Outliers (probabilistic measures): e.g. * Detect Numeric Outliers (distribution statistics): e.g. * Predict Categorical Fields (Logistic Regression): e.g. * Predict Numeric Fields (Linear Regression): e.g. Look for our ML Youtube Playlist for simple explanations of how to use MLTK and what it is for. You can inspect the assistant panels and underlying code to see how it all works. The Splunk Machine Learning Toolkit App delivers new SPL commands, custom visualizations, assistants, and examples to explore a variety of ml concepts.Įach assistant includes end-to-end examples with datasets, plus the ability to apply the visualizations and SPL commands to your own data.
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