Category: Data Science
Data Profiling in Power BI (using Azure Databricks)
In Microsoft, there are two worlds i.e. MS Azure and MS Office 365. They are two two different Active Directories in Microsoft world. Hence, they have their own tools to...
Elements of a Data Science practice
For a successful Machine Learning or Data Science practice, the following elements are key: Business Case Quality Data Skilled Teams Technology Risk Management Business...
Data Profiling options in Azure
The first step of Data Science, after Data Collection, is Exploratory Data Analysis(EDA). However, the first step within EDA is getting a high-level overview of how the...
Experiments in Azure Machine Learning
Introduction to Experiments Continuous Experimentation is key to MLOps. Hence, tracking each iteration, artefact of an experiment is a key to success in Data Science....
Machine Learning with Azure Synapse SQL
Machine Learning is not only about building models but consuming them. ML Models are consumed in two ways: Batch Inferencing and Real-Time Inferencing. In Real-Time...
Azure Machine Learning Pipelines for Model Training
With AI becoming mainstream, automation of ML workflows is becoming critical. This includes automation of Training, Deployment and Inference of ML Models. These Machine...
Motivating DP-100: Designing and Implementing a Data Science Solution on Azure
Data Science has a come a long way. From Jupyter notebooks on a Data Scientists’ laptops, we have moved to complex ML workflows running in cloud infrastructure....
Explainable Machine Learning with Azure Machine Learning
In the previous two articles, we took a dive into Explainable Machine Learning. The first one dealt with LIME and SHAP for a supervised machine learning setting. The...
Machine Learning Interpretability for Isolation forest using SHAP
In our previous article, we covered Machine Learning interpretability with LIME and SHAP. We introduced the concept of global and local interpretability. Moreover, we...
An Introduction to Interpretable Machine Learning with LIME and SHAP
Introducing Interpretable Machine Learning and(or) Explainability Gone are the days when Machine Learning models were treated as black boxes. Therefore, as Machine...