Musings on Data Quality

Introduction For a successful Machine Learning or Data Science practice, the following elements are key: Business Case Quality Data Skilled Teams Technology Risk...

Evolution of Business Decision Making – From Humans to AI Driven

Practical life is all about Decisions. Often, we are at crossroads, and we need to decide about the next course of action. This is truer in the business world, where...

Azure Databricks and Azure Machine Learning make a great pair!

The two pillars of the Azure AI platform are Azure Databricks and Azure Machine Learning. And, this is a common debate in Azure AI circles. Naturally, the question...

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....