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Curated SQL Posts

C# Text Classification via ML.NET 2

Matt Eland tries out ML.NET 2.0:

Recently ML.NET 2.0 was released, giving us a bevy of new features for the open source machine learning library for dotnet applications.

The release improved ML.NET’s text processing capabilities and improved some aspects of the already fantastic automated machine learning capabilities it had. Moreover, the release seemed to reaffirm ML.NET’s determination to be relevant for advanced machine learning tasks, including deep learning and transformer-based architectures.

In this article we’ll explore ML.NET 2.0’s new text classification capabilities and see how you can use C# to analyze sentiment, match utterances to intents, or otherwise classify textual data without having to write a lot of custom code.

Read on to learn more about ML.NET and plenty of turtles.

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Running Python Code from R via Reticulate

Rick Pack crosses the streams:

I wanted a REPL (read-evaluate-print-loop) so that I could quickly experiment with Python without, for the moment, leaping over what some consider one of the biggest hurdles to Python usage: Work environment set up.

The reticulate R package by Posit enables the use of Python while working within the R Studio IDE. One can find a Posit tutorial here.

Read on for Rick’s notes.

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Working with the AML Python SDK

Tomaz Kastrun continues a series on Azure Machine Learning. Day 9 takes us through a piece of the Python SDK:

Python SDK namespace is azureml.core.environment. Environments specify the set of Python packages, environment variables, and software settings around your training and scoring scripts. In addition to Python, you can also configure PySpark, Docker and R for environments.

You can use namespace  Environment (or created object/asset) to make deployment and code reusable for training purposes at given docker images, configurations and compute type.

Day 10 shows us how to work with the Python SDK via VS Code or a local Jupyter notebook:

Let’s continue to explore the power of SDK and the namespaces. And we will look into namespace that will help you connect to Azure ML resources with Python SDK.

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Managing Azure Log Analytics Data Access

Bruno Gabrielli wants to limit data access to Log Analytics:

I am back with another important topic arising from my customers’ visits. How can I give very specific access to Log Analytics data, whether they be Security or Monitoring data?

Tricky one, isn’t it? A very simplistic answer could be: “manage your access list through IAM on the workspace”, but this is not enough. Say, for instance, that you would give scoped access to data coming from specific resources or, even more complicated, you would like that given the same resource one team can see some info and another one all the rest.

Looks complicated, but hey … good news: this is doable

Read on to learn how.

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Refreshing One Power BI Dataset Table via SSMS

Nicky van Vroenhoven performs a small update:

A few weeks back I was working on a dataset at a client where I needed to import Excel files from a folder into said dataset. I filtered the files on a prefix and loaded around 30 files of the same structure to a table in my dataset. The Excel files are exports from a budgetting system (I know, right?) that have to be updated multiple times in the next coming weeks on an ad-hoc basis.

Rather than refreshing the entire dataset, there’s a better way, though there is a caveat.

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Additional Backup Tools for MySQL

Lukas Vileikis wraps up a series:

As already mentioned, Zmanda Recovery Manager (or ZRM for short) is a member of the Zmanda family of products – Zmanda is famous for offering backup tools for MySQL and MariaDB. The company allows its customers to scale up without any issues (they offer a pay-as-you-go subscription model), and its tools are capable to back up terabytes of data in MySQL.

Read on to learn more about it, as well as a couple more tools you can use to back up a MySQL database.

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An Overview of Quarto for R Users

Nicola Rennie and Colin Gillespie provide an overview of Quarto:

Earlier this year, Posit (formerly RStudio) released Quarto. Quarto is an open-source scientific and technical publishing system that allows you to weave together narrative text and code to produce high-quality outputs including reports, presentations, websites, and more.

One of the main features of Quarto is that it isn’t just built for R. It’s language-agnostic. It can render documents that contain code written in R, Python, Julia, or Observable. That makes it incredibly useful if you work in multilingual teams, or collaborate with people who write in a different programming language from you. But what if you don’t use any other programming languages? What benefits does Quarto bring to people who only use R?

Read on to learn why you might want to use it over R Markdown.

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AML Environments and SDKs

Tomaz Kastrun continues an advent of Azure ML. First up is environments:

We have explored how to create a compute instance and compute target and learned that ML frameworks and scripting packages always come preinstalled.

Choosing the right set of components (CPU, GPU, RAM, Core) and corresponding software (OS, ML Framework, packages) can be time-consuming.

Under Curated environments, you will find predefined environments, with settings for running particular frameworks, like PyTorch or TensorFlow.

Then an overview of the Azure CLI and Python SDK for AML:

What is Azure CLI? It is an Azure Command Line, a great tool for running commands out of CMD. It is a multi-platform and can be run from Azure or from the client’s machine. It is great for scripting and automating repetitive tasks or making the complex task look like lines of code, especially when it comes to infrastructure, managing, provisioning and monitoring. It can also be run from Azure Cloud Shell. It is native to Azure and can be used across all the services and offerings. Usually, the Azure CLI commands start with “az ..”. On top of that, you can also install Azure Machine Learning CLI, as an extension to Azure CLI. The AML CLI will give you additional commands to manage resources for machine learning.

The same functionality (to some extent) in Azure Machine Learning can be achieved with Python SDK. In addition to that, it offers also great ways to create and manage resources you use for training and deployment of models.

And, so that we can catch up a bit to Tomaz, one more post covering the Python SDK:

Looking briefly into Azure CLI and Python SDK, let’s explore the power of SDK and the most important namespaces.

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Ignoring Warnings in Powershell

Kenneth Fisher puts a sticky note over the blinking red light so it won’t bother him anymore:

Ok, great! Good information. Not something that affects me right now but still helpful to know. And I really appreciate the fact that going forward I can expect this type of information.

I found this information less useful and less appreciated after I put it into a loop and it ran ~40 times in a row, hiding any real information beneath a pile of warnings. Fortunately, right there in the warning there is a helpful note on how to suppress it

Read on for the story of why this message popped up, as well as how to prevent it from popping up and Kenneth’s medium-term plan for dealing with it.

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