Low-Code ML: Batch Scoring

This is part six in a series on low-code machine learning with Azure ML. Last Time on 36 Chambers In the prior episode of this series, we built a real-time endpoint, showed how to call it, and saw how to make some minor changes to a real-time inference pipeline. Today, we're going to show off…

Low-Code ML: Deployment and Endpoints

This is part five in a series on low-code machine learning with Azure ML. Where We Are In the prior post, we trained a model using the Azure ML designer. Penguins scored In this post, we're going to make it available for the whole world to use, as what the world needs is a way…

Low-Code ML: Training in the Designer

This is part four in a series on low-code machine learning with Azure ML. The Machine Learning Process Machine learning is a lot like an action film from the 1980s: we see early on that there's a problem, we train in a cool montage with upbeat rock music, and then we come back to the…

Low-Code ML: Using AutoML

This is part three in a series on low-code machine learning with Azure ML. The Place of AutoML Automated Machine Learning (AutoML) provides two distinct benefits. The first benefit is the one that AutoML providers tend to tout: you don't need (much) machine learning experience to use them. According to the marketing, AutoML does all…

Low-Code ML: Data and Compute

This is part two in a series on low-code machine learning with Azure ML. Welcome to Machine Learning Studio Azure Machine Learning Services is the full-on product for machine learning in Azure. In addition to this, there is Azure ML Studio, a graphical interface for using Azure ML. You'll definitely spend a lot of time…

Low-Code ML: Creating a Workspace

This is part one in a series on low-code machine learning with Azure ML. Machine Learning with Less Code Getting started with machine learning is a real challenge, and there are two reasons for it: one mathematical and one programmatical. First, you need a good understanding of statistical and mathematical concepts to get very far…

A Slice of Time: Wrapping Up

This is part eleven in a series on window functions in SQL Server. What Did We Learn? Over the course of this series, we learned a lot about window functions. We started off with an overview of the concept and then moved on to the various types of functions (aggregate, ranking, offset, statistical, and ordered…

A Slice of Time: Batch Mode Power-Ups

This is part ten in a series on window functions in SQL Server. The Need for Speed SQL Server typically operates in row mode, which means that an operator processes one row at a time. This sounds inefficient, but tends to work out pretty well in practice. However, something which may work out even better…

A Slice of Time: Indexing and Window Functions

This is part nine in a series on window functions in SQL Server. POC: the Rule of Thumb If you've been around the block with window functions, you've probably heard of the POC indexing strategy: Partition by, Order by, Covering. In other words, with a query, focus on the columns in the PARTITION BY clause…

A Slice of Time: Window Function Limitations

This is part eight in a series on window functions in SQL Server. Not All Roses So far, we've seen some really cool things we can do with window functions. That said, there are some limitations with these windows. Some of them are built into how window functions work and others are incomplete implementations of…