Tons of non-tech organisations have recently surged their interest in real-time/batch big data processing specially banking, retail and insurance and typically experts are flown in to help them understand their jungle of databases storing data from the ages.
What happens next will definitely NOT surprise you. There is already a very large ecosystem of big data processing tools and technologies and varying on the kind of experts you have in your company, their opinion will vary. Add to that the surge of cloud computing and specially these good ol’ giants moving towards some amazing cloud solutions like Azure, GCP or AWS probably in that order, you are looking at a cost surge with many not understanding why such massive complexity is actually necessary, great era for resume facelift!! …
Quite recently, I have been digging a lot in quick experiments with Rasa stack that is using advanced NLP techniques for building chatbots (one of my most favourite subjects). I also heard so much about so many open-source technologies recently that drew me here to write about this.
Today, in the market you have so many Data Science Workbench solutions from Azure Databricks, Domino Data labs, Dataiku, AWS Sagemaker just to name a few or to be honest the ones i have heard of. 😜 But picking on the latest trends of the market which is Kubernetes, since most companies specially big companies deploying Microservices wants to be cloud vendor agnostic, are slowly moving towards deploying their applications on container orchestration solutions like Kubernetes with their Docker images. …
Let’s do a quick recap, the previous part, we have learned about the basic components of Rasa Core. Now let’s apply it to the below problem and talk about the results.
Bear in mind, i have shared the entire code for each model trained in Github. Feel free to look into it and use it to build your own Rasa Core chatbot or test mine if you like.