Today we are going to upload a ML model into IRIS Manager and test it. Note: I have done the following on Ubuntu 18.04, Apache Zeppelin 0.8.0, These days many available different tools for Data Mining enable you to develop predictive models and analyze the data you have with unprecedented ease. InterSystems IRIS Data Platform provide a stable foundation for your big data and fast data applications, providing interoperability with modern DataMining tools. In this series of articles we explore Data mining capabilities available with InterSystems IRIS.
At some point, we all have struggled in deploying our trained Machine Learning model and a lot of questions start popping up into our mind. What is the best way to deploy a ML model? How do I serve the model's predictions? Which server should I use? Should I use flask or django for creating REST API? Don't worry, I got you covered with all of it!!:) In this tutorial, we will learn how to train and deploy a machine learning model in production with more focus on deployment because this is where we all data scientists get stuck.
JupyterLab is an integrated environment that can streamline the development of Python code and Machine Learning (ML) models in Kinetica. With it you can edit Jupyter notebooks that integrate code execution, debugging, documentation, and visualization in a single document that can be consumed by multiple audiences. The development process is streamlined because sections of code (or cells) can be run iteratively while updating results and graphs. It can be accessed from a web browser and supports a Python console with tab completions, tooltips, and visual output. One of the difficulties of using Jupyter notebooks with Kinetica had been that an environment needs to be installed with all the necessary dependencies.