Use DSDL

• Leverage the examples provided in the Splunk App for Data Science and Deep Learning The Splunk App for Data Science and Deep Learning (DSDL) ships with more than thirty data science, deep learning, and machine learning example techniques that showcase different algorithms for classification, regression, forecasting, clu…
• Splunk App for Data Science and Deep Learning example workflow The Splunk App for Data Science and Deep Learning (DSDL) lets you to integrate advanced custom machine learning and deep learning systems with the Splunk platform. You can build, test, and operationalize customized models that leverage G…
• Develop a model using JupyterLab The Splunk App for Data Science and Deep Learning (DSDL) leverages predefined JupyterLab Notebook workflows so you can build, test, and operationalize customized models with the Splunk platform.
• Using multi-GPU computing for heavily parallelled processing Use the multi-GPU computing option for heavily parallelled processing such as training of deep neural network models. You can leverage a GPU infrastructure if you are using NVIDIA and have the needed hardware in place. For more informati…
• Splunk App for Data Science and Deep Learning commands The Splunk App for Data Science and Deep Learning (DSDL) integrates advanced custom machine learning and deep learning systems with the Splunk platform. The app extends the Splunk platform with prebuilt Docker containers for TensorFlow a…
• Performance tuning and handling large datasets Combine the power of the Splunk platform search with container-based machine learning workloads using the Splunk App for Data Science and Deep Learning (DSDL). It is important to manage large datasets with millions or billions of events…
• Advanced HPC and GPU usage The Splunk App for Data Science and Deep Learning (DSDL) integrates with containerized machine learning. DSDL supports graphics processing unit (GPU) acceleration, large-scale, high-performance computing (HPC) clusters, and distributed t…
• Extend the Splunk App for Data Science and Deep Learning with custom notebooks You can define custom notebooks for specialized machine learning or deep learning tasks with the Splunk App for Data Science and Deep Learning (DSDL). By writing your own Jupyter notebooks, you can incorporate custom algorithms, advanced…
• Container monitoring and logging The Splunk App for Data Science and Deep Learning (DSDL) leverages external containers for computationally intensive tasks. It is crucial to monitor these containers for debugging, operational awareness, seamless model development, and a…
• Container management and scaling Use external containers with the Splunk App for Data Science and Deep Learning (DSDL) to offload resource-heavy machine learning tasks from the Splunk search head. This architecture isolates potentially large workloads, and allows for ho…
• Advanced container customization The Splunk App for Data Science and Deep Learning (DSDL) relies on container images for CPU or GPU-based machine learning workloads. The app ships with certain default images, including Golden CPU and Golden GPU.
• Model governance and security in the Splunk App for Data Science and Deep Learning Train and serve advanced ML models in containerized environments with the Splunk App for Data Science and Deep Learning (DSDL). Enterprise-grade machine learning might require model governance, secure container management, and strict acc…

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