Note that there is a model development guide within the DLTK under the overview tab that you can use for developing your own use cases. Next up we’re going to go through the process of developing and testing our code.
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When I wrote the script for the entity extraction example here we didn’t have a pre-built NLP container image, so I ran the following from the command line to install the spaCy python library and associated NLP model:Īs mentioned though, you could use the NLP container in the DLTK if you want an easy button.
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Note that if you are thinking of creating your own container with additional libraries Anthony’s blog here provides a great walkthrough for how to do this. Ideally, you want an NLP container running, but don’t worry if that’s not the case as the instructions below will help you import the right libraries. You also want to check the type of container that is running – the app comes with four pre-built container image options: TensorFlow CPU, TensorFlow GPU, PyTorch, and NLP. You then want to launch a container from the DLTK – managing and viewing containers can be done from the Container dashboard within the toolkit. You should then set up Docker either in the same environment or in one that is accessible to your Splunk environment.įollow the set-up steps in the DLTK if it is your first time using it, making sure that it can connect to Docker. Preparing your environmentįirst of all, you will need an environment where you have installed Splunk with the following apps: python for scientific computing, the Machine Learning Toolkit (MLTK) and of course the DLTK. Looking at Splunk’s favourite type of data (no prizes for guessing the answer is machine data) a good example for us would be automatic classification of support tickets based on the description of the issue the customer is experiencing.įor the purposes of this blog, however, I’m going to stay away from machine data and extract some key features from text copied from the Wikipedia article about one of my favourite bands… And yes there will be python code. This is an awesome technique and has a number of interesting applications as described in this blog. Specifically, we’re going to develop a named entity recognition use case. In this blog, I’d like to take you through an example of how to develop a natural language processing (NLP) use case using the Deep Learning Toolkit. Maybe you’re interested in finding out more about deep learning? Maybe your current ML analytics are running too slowly or crushing your CPU and RAM? Or perhaps your boss has told you that they need an AI-based app so they can show off to their boss (who will then brag about it to their boss)?Īs some of you will have seen we’ve recently launched the Deep Learning Toolkit (DLTK), which allows users to access external machine learning and deep learning libraries such as TensorFlow from Splunk whilst also offsetting the compute to a containerised environment.
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