Philipp Drieger
Go with your Data Flow - Improve your Machine Learning Pipelines
How do you organize the data flow in Splunk Enterprise or Splunk Cloud? Splunker Philipp Drieger shares typical data pipeline patterns that will help you improve your existing or future machine learning workflows with MLTK or DLTK.
Deep Learning Toolkit 3.6 - Automated Machine Learning, Random Cut Forests, Time Series Decomposition, and Sentiment Analysis
We’re excited to share that the Deep Learning Toolkit App for Splunk (DLTK) is now available in version 3.6 for Splunk Enterprise and Splunk Cloud. Read all about the updates here.
Deep Learning Toolkit 3.5 - Part 2: Change Point Detection, Matrix Profiles and LSTM-based Predictions
In the first part of this 2-part-series we talked about recent additions to version 3.5 of the Deep Learning Toolkit for Splunk (DLTK). Here in part 2 we want to explain a few new algorithmic approaches available for time series analysis. These can be especially interesting for anomaly detection and time series prediction.
Deep Learning Toolkit 3.5 - Part 1: Git, MLflow and Image Updates
Part 1 of this blog series, talks about the latest improvements for model management, code version control and recent image updates of Deep Learning Toolkit for Splunk (DLTK).
Accelerated Data Science and Deep Learning for a Predictive Testing Strategy
During .conf20 we presented alongside BMW Group the way a predictive testing strategy can enable better process efficiency in automotive manufacturing. We also introduced briefly which machine learning tools and analytical techniques were useful within the given situation.
Deep Learning Toolkit 3.4: Grid Search, Causal Inference and Process Mining
We're diving into three interesting new algorithmic approaches available with the latest version 3.4 of the Deep Learning Toolkit (DLTK) App for Splunk.
