Category: AI
Takeaways from a Microsoft engineer about ML on Azure
Takeaways on how to run ML projects on Azure from a Microsoft engineer – author of the Azure Data Scientist Associate Certification Guide.
Andreas Botsikas – Microsoft engineer, author of the Azure Data Scientist Guide
“ML models are in fashion, such as customer churn predictions. However, it’s not easy to define what a churned customer is unless you have a multi subscription system like Netflix, where you can quickly identify the customers who stopped paying. E.g., what does churn mean for a supermarket?”
Lessons learnt with a Google engineer to leverage data for business
Leverage data and Machine Learning for business. Lessons learnt with a Google Engineer about real-world implementations.
Héctor Parra – A Google Engineer and the exciting realities of ML
“The COVID pandemic has spoiled many forecasting models. We can’t use historical data to predict a scenario that has never happened before. Many companies have not included data from 2021 to train their models because it’s biased”.
K-Means Clustering: A Simple Explanation
Post with an infographic to explain how to use K-Means Clustering from a citizen, intuitive point of view. It adds uses cases and next steps.
Fuzzy Matching Demo: Inconsistent Company Names
Fuzzy matching use case. We improve data quality when loading data into BigQuery using Trifacta software, which simplifies the process.
