Workflow · Production
Qlik Predictive Modeling Guide: Types, Algorithms, and Best Practices
The problem
Organizations are inundated with massive amounts of complex and rapidly changing data and need better tools to make data-driven decisions that are less susceptible to human bias and error.
Workflow diagram · grounded in source
1
Define goals
trigger
“Before proceeding with model development, it's essential to have a well-defined business question or problem that needs to be addressed.”
2
Collect and prepare data
integration
“gather relevant data from various sources. This includes structured data like sales history and demographic information, as well as unstructured data like social media content, customer service notes, and web logs.”
3
Train model
ai_action
“Once you've selected the appropriate model, the next step is to optimize its parameters and fine-tune it for accuracy.”
4
Evaluate and adjust
validation
“To evaluate the performance of your model, you can use a validation set or cross-validation. This involves testing the model on a separate dataset that was not used for training, to ensure that it can generalize well to new data.”
5
Deploy model
output
“Now you're finally ready to integrate your model into the relevant application or system and deploy it in production to start making predictions.”
Reported outcome
(not stated)
Reported stack
Qlik AutoML®
Frequently asked questions
What did this team achieve with this AI workflow?
(not stated)
What tools did this team use?
Qlik AutoML®.
How is this workflow AI workflow structured?
Define goals → Collect and prepare data → Train model → Evaluate and adjust → Deploy model.