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Category: Cloud

Working with the AML Python SDK

Published 2022-12-12 by Kevin Feasel

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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AML Environments and SDKs

Published 2022-12-09 by Kevin Feasel

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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Data and Compute in Azure ML

Published 2022-12-06 by Kevin Feasel

Tomaz Kastrun continues an advent series on Azure ML. Day 4 covers data sources:

Yesterday, we have learned the general outlook of the Studio and in this blog post, we will be focusing primarily on getting data to the workspace and reading data from other data sources.

Day 5 has you provision some compute:

With a basic understanding of data assets, let’s create compute instances. Under “Manage” in the navigation bar, select “Compute” (denoted as 1), select “Compute instances” (d. 2) and click on “+ New”.

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Thoughts on a Migration: Azure Analysis Services to Power BI Premium

Published 2022-12-06 by Kevin Feasel

Dan English shares some thoughts:

Over the past month couple of months I got the opportunity to test out the new migration experience that was just made available for Public Preview this past month during the PASS Data Community Summit and announced on the Power BI blog here Accelerate your migration experience from Azure Analysis Services to Power BI Premium with the automated migration tool. The blog post also shows a very quick animated gif walkthrough of the process and there is a thirteen minute video from the MS Build conference earlier this year where this was first demoed that you can check out here as well The Future of Enterprise Semantic Models.

Click through for a detailed analysis.

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Creating an AML Workspace and Trying the Studio

Published 2022-12-05 by Kevin Feasel

Tomaz Kastrun continues an advent of Azure ML. First up, Tomaz creates a workspace:

You will select “New workspace”. For now, we will work on a workspace. But just to mention, the “New registry” will enable you to share assets among different workspaces, support multi-region replication and help you provision all resources to facilitate region replications.

From there, the focus shifts to using Azure Machine Learning Studio:

In this overview page, you can click the button “Launch studio” in the middle of the workspace or you can copy and paste the Studio web URL provided under the “Essentials” to start the Studio.

But before we launch the Studio, let’s explore some additional settings, worth mentioning.

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Azure SQL Managed Instance Performance

Published 2022-12-05 by Kevin Feasel

Reitse Eskens wraps up a series on Azure SQL performance comparisons:

So far, the blogs were about the really SaaS databases; the database is deployed and you don’t have think about it anymore. This ease of use comes at a ‘price’. You’ve got no control whatsoever on files, you’ve lost the SQL Agent and a number of other features. The managed instance is a bit different. When I was testing you could see the TempDB files but not change them, since then a few changes have been made to this tier where you’re able to change settings and, Niko Neugebauer told the data community on twitter, there are more changes coming. With the managed instance, you get the agent back and you can run cross database query’s again. So you can safely say the managed instance is a hybrid between your trusty on-premises server and the fully managed Azure SQL database.

Click through for Reitse’s thoughts.

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An Intro to Azure Machine Learning

Published 2022-12-02 by Kevin Feasel

Tomaz Kastrun has a new Advent challenge:

Azure Machine Learning (or Azure Machine Learning Service and abbreviation AML) is Azure’s cloud service for creating, managing and productionalising machine learning projects. It is a collaborative tool for Data Scientists, Machine Learning Engineers, and data engineers, covering their daily and operational tasks. From creating and training to deploying and managing predictive models and machine learning solutions.

Click through for the introduction.

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Apache Ranger on ElasticMapReduce

Published 2022-12-02 by Kevin Feasel

Laurence Geng looks at Ranger:

Whether you’ve successfully made it before or not, installing and integrating Windows AD/OpenLDAP + Ranger + EMR is a very hard job, it is complicated, error-prone, and time-consuming for the following reasons:

Read on for the list of reasons, some background on Ranger, and an automated installer intended to make life a bit easier.

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Controlling Cosmos DB Time to Live

Published 2022-12-01 by Kevin Feasel

Rahul Mehta pulls out the stopwatch:

As Microsoft states, Azure Cosmos DB “is a fully managed NoSQL database service for building scalable, high-performance applications”. Cosmos DB is widely used for storing NoSQL data with options to create using different Core (SQL), MongoDB, Cassandra, Table, and using gremlin.

With wide usage, the content storage also increases, sometimes even in Gigabytes a day. With such content storage, retention and archival of data are one of the common ask from the customer. Today, we are going to talk about how to retain data and remove unnecessary data periodically from Azure Cosmos DB. Before we do that, we need to understand a storage concept called “Container”

Read on to learn about containers, as well as the built-in way to garbage collect data.

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Migrating Azure Analysis Services to Power BI Premium

Published 2022-12-01 by Kevin Feasel

Gilbert Quevauvilliers dumps AAS:

I thought it would be a good idea to walk through the steps when looking to migrate AAS to PBI.

In the past when I had to do this for clients it was a lot of manual steps and a lot of small things to get just right. This process is now seamless and awesome!

Reviewing Gilbert’s step-by-step process, yeah, this is easy, though watch out for the pitfalls Gilbert found.

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