Qlik's AutoML is a powerful tool for analysts to speedily implement machine learning models. Data Science provides the framework for building them right.
Qlik AutoML is a powerful tool for analysts to speedily implement machine learning models in their Qlik applications. It takes most of the fuss out of data preparation, model selection & tuning, and model deployment. A competent data scientist can use it to take a machine learning model from concept to implementation in just a few days instead of weeks.
For the rest of us however, AutoML (and machine learning in general) are difficult to implement effectively. Even with automated tools, you still need to know things like:
Is our question well-formulated for modelling?
How do we prepare data for modeling? What kinds of data can we use?
Which model is the right one, and why?
How accurate is our model's prediction?
IPC Global's Chief Data Scientist will take you through the data science process of Data Collection, Data Preparation, Exploratory Analysis, Model Building, and Model Deployment using Qlik AutoML. We will also present a number of different use-cases for AutoML to help get your cognitive juices flowing.
So even if the phrase "hyper-parameter tuning" sounds to you like a recording technique the Eurythmics used to get that iconic synthesizer sound on "Sweet Dreams", you'll leave this webinar with the foundational knowledge you need to go out and start adding machine learning to your analytic repertoire.
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