Tag: , ,

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, artifact 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...

How to detect Data Drift in Azure Machine Learning

This is the age of AI. Hence, you want to automate a certain business process, let’s say, for instance, email classification.┬áSo, you have built a Machine...

The Azure Machine Learning Designer

At MS Ignite this year, Azure Machine Learning Designer went GA, which is the next generation of the classic Azure ML Studio. Although it is quite similar to its...