Overview Of Azure ML And ML Studio

In this article, we are going to learn about Azure ML and ML Studio. As we know Azure is Microsoft Cloud computing service. And Machine learning supported by Azure is called Azure ML. It’s a complete automated framework to build, teach, train and deploy as a web service and have visual development environment to make it easy for data scientists.

Benefits of having Azure ML as a cloud solution,

  1. It’s providing Azure ML Studio which it uses to create model and deploy instantly.
  2. Visual user interface, drag and drop feature, real time data visualization.
  3. All projects, experiments are stored in the cloud. You can access it from anywhere.
  4. Almost all input data types are supported by Azure ML as data source.
  5. Extend model using R and Python or use trained model as module.

Supported Input data types

  1. Hive
  2. HTTP
  3. MySQL
  4. SQL Server
  5. PostgreSQL
  6. Power Query
  7. SharePoint
  8. Azure DB
  9. Web API
  10. Local Files
  11. Teradata

There are many other data sources; you can check it on the Microsoft portal.

Azure ML Studio

Here I will give an overview of Azure ML Studio. You need to sign up in Azure portal and select Machine Learning Studio to launch it. It opens in the browser and looks like the below image.

Overview Of Azure ML And ML Studio

It’s workbench software which has predefined protocols to follow while building and training a model. As per the image, the visual workspace enables developers to quickly create models and visualize data with just some clicks.

It has 6 high level navigations menus and those are Projects, Experiments, Web Service, Dataset, Trained Model and Settings.

Overview Of Azure ML And ML Studio

Projects

It lists all projects and models created by users. Project contains combinations of all module experiments and datasets.

Experiments

It allows developers to build, test and iterate multiple times on either its new model or existing model. You can copy models and do many experiments and get accurate predictive results.

Web Service

Tested and trained models are deployed as web services as public APIs to use outside of the Azure environment. It predict results based on input parameters. It returns value based on trained deployed model data.

Dataset

Dataset contains uploaded datasets in Azure ML studio. It lists uploaded datasets and you can also pick from Microsoft sample datasets, which can be utilized for your experiments. You can use big New + buttons to add data files from your local computer.

Trained Model

Save your trained models and experiment for future uses.

Settings

Settings tab allows us to view and edit workspace and regenerate authorization token.

Overview Of Azure ML And ML Studio

#azure #overriew #azure-ml #ml-studio

What is GEEK

Buddha Community

Overview Of Azure ML And ML Studio

Overview Of Azure ML And ML Studio

In this article, we are going to learn about Azure ML and ML Studio. As we know Azure is Microsoft Cloud computing service. And Machine learning supported by Azure is called Azure ML. It’s a complete automated framework to build, teach, train and deploy as a web service and have visual development environment to make it easy for data scientists.

Benefits of having Azure ML as a cloud solution,

  1. It’s providing Azure ML Studio which it uses to create model and deploy instantly.
  2. Visual user interface, drag and drop feature, real time data visualization.
  3. All projects, experiments are stored in the cloud. You can access it from anywhere.
  4. Almost all input data types are supported by Azure ML as data source.
  5. Extend model using R and Python or use trained model as module.

Supported Input data types

  1. Hive
  2. HTTP
  3. MySQL
  4. SQL Server
  5. PostgreSQL
  6. Power Query
  7. SharePoint
  8. Azure DB
  9. Web API
  10. Local Files
  11. Teradata

There are many other data sources; you can check it on the Microsoft portal.

Azure ML Studio

Here I will give an overview of Azure ML Studio. You need to sign up in Azure portal and select Machine Learning Studio to launch it. It opens in the browser and looks like the below image.

Overview Of Azure ML And ML Studio

It’s workbench software which has predefined protocols to follow while building and training a model. As per the image, the visual workspace enables developers to quickly create models and visualize data with just some clicks.

It has 6 high level navigations menus and those are Projects, Experiments, Web Service, Dataset, Trained Model and Settings.

Overview Of Azure ML And ML Studio

Projects

It lists all projects and models created by users. Project contains combinations of all module experiments and datasets.

Experiments

It allows developers to build, test and iterate multiple times on either its new model or existing model. You can copy models and do many experiments and get accurate predictive results.

Web Service

Tested and trained models are deployed as web services as public APIs to use outside of the Azure environment. It predict results based on input parameters. It returns value based on trained deployed model data.

Dataset

Dataset contains uploaded datasets in Azure ML studio. It lists uploaded datasets and you can also pick from Microsoft sample datasets, which can be utilized for your experiments. You can use big New + buttons to add data files from your local computer.

Trained Model

Save your trained models and experiment for future uses.

Settings

Settings tab allows us to view and edit workspace and regenerate authorization token.

Overview Of Azure ML And ML Studio

#azure #overriew #azure-ml #ml-studio

Mia  Marquardt

Mia Marquardt

1624853402

Logging TensorFlow(Keras) metrics to Azure ML Studio in realtime

A real-time approach using a custom Keras callback.

Training a TensorFlow/Keras model on Azure’s Machine Learning Studio can save a lot of time, especially if you don’t have your own GPU or your dataset is large. It seems that there should be an easy way to track your training metrics in Azure ML Studio’s dashboard. Well, there is! It just requires a short custom Keras callback.

If you are new to training TensorFlow models on Azure, take a look my article “Train on Cloud GPUs with Azure Machine Learning SDK for Python.” It starts from the beginning and implements an entire training workflow from scratch. This post, however, assumes you know the basics and will only focus on the necessary tools to log your metrics to Azure.

There is a working code example that demonstrates the tools in this article in the examplesfolder of the GitHub repository for this project. The callback itself is in the log_to_azure.py file.

#python #azure #tensorflow #keras #azure-machine-learning #logging tensorflow(keras) metrics to azure ml studio in realtime

Eric  Bukenya

Eric Bukenya

1624713540

Learn NoSQL in Azure: Diving Deeper into Azure Cosmos DB

This article is a part of the series – Learn NoSQL in Azure where we explore Azure Cosmos DB as a part of the non-relational database system used widely for a variety of applications. Azure Cosmos DB is a part of Microsoft’s serverless databases on Azure which is highly scalable and distributed across all locations that run on Azure. It is offered as a platform as a service (PAAS) from Azure and you can develop databases that have a very high throughput and very low latency. Using Azure Cosmos DB, customers can replicate their data across multiple locations across the globe and also across multiple locations within the same region. This makes Cosmos DB a highly available database service with almost 99.999% availability for reads and writes for multi-region modes and almost 99.99% availability for single-region modes.

In this article, we will focus more on how Azure Cosmos DB works behind the scenes and how can you get started with it using the Azure Portal. We will also explore how Cosmos DB is priced and understand the pricing model in detail.

How Azure Cosmos DB works

As already mentioned, Azure Cosmos DB is a multi-modal NoSQL database service that is geographically distributed across multiple Azure locations. This helps customers to deploy the databases across multiple locations around the globe. This is beneficial as it helps to reduce the read latency when the users use the application.

As you can see in the figure above, Azure Cosmos DB is distributed across the globe. Let’s suppose you have a web application that is hosted in India. In that case, the NoSQL database in India will be considered as the master database for writes and all the other databases can be considered as a read replicas. Whenever new data is generated, it is written to the database in India first and then it is synchronized with the other databases.

Consistency Levels

While maintaining data over multiple regions, the most common challenge is the latency as when the data is made available to the other databases. For example, when data is written to the database in India, users from India will be able to see that data sooner than users from the US. This is due to the latency in synchronization between the two regions. In order to overcome this, there are a few modes that customers can choose from and define how often or how soon they want their data to be made available in the other regions. Azure Cosmos DB offers five levels of consistency which are as follows:

  • Strong
  • Bounded staleness
  • Session
  • Consistent prefix
  • Eventual

In most common NoSQL databases, there are only two levels – Strong and EventualStrong being the most consistent level while Eventual is the least. However, as we move from Strong to Eventual, consistency decreases but availability and throughput increase. This is a trade-off that customers need to decide based on the criticality of their applications. If you want to read in more detail about the consistency levels, the official guide from Microsoft is the easiest to understand. You can refer to it here.

Azure Cosmos DB Pricing Model

Now that we have some idea about working with the NoSQL database – Azure Cosmos DB on Azure, let us try to understand how the database is priced. In order to work with any cloud-based services, it is essential that you have a sound knowledge of how the services are charged, otherwise, you might end up paying something much higher than your expectations.

If you browse to the pricing page of Azure Cosmos DB, you can see that there are two modes in which the database services are billed.

  • Database Operations – Whenever you execute or run queries against your NoSQL database, there are some resources being used. Azure terms these usages in terms of Request Units or RU. The amount of RU consumed per second is aggregated and billed
  • Consumed Storage – As you start storing data in your database, it will take up some space in order to store that data. This storage is billed per the standard SSD-based storage across any Azure locations globally

Let’s learn about this in more detail.

#azure #azure cosmos db #nosql #azure #nosql in azure #azure cosmos db

Rylan  Becker

Rylan Becker

1621121100

Writing U-SQL scripts using Visual Studio for Azure Data Lake Analytics

In the 2nd article of the series for Azure Data Lake Analytics, we will use Visual Studio for writing U-SQL scripts.

Introduction

Azure Data Lake stores the unstructured, structured, and semi-structured data in the Azure cloud infrastructure. You can use Azure portal, Azure Data Factory(ADF), Azure CLI, or various other tools. In the previous article, An overview of Azure Data Lake Analytics and U-SQL, we explored the Azure Data lake Analytics using the U-SQL script.

In this article, we will understand U-SQL scripts and executing them using Visual Studio.

U-SQL scripts execution in the Visual Studio

U-SQL is known as a big data query language, and it combines the syntax similar to t-SQL and the power of C## language. You can extract, transform data in the required format using the scripts. It has few predefined extractors for CSV, Text, TSV for extracting data from these formats. Similarly, it allows you to convert the output to your desired format. It offers big data processing from gigabyte to petabyte scale. You can combine data from Azure Data Lake Storage, Azure SQL DB Azure Blob Storage, Azure SQL Data Warehouse.

You can develop and execute the scripts locally using Visual Studio. Later, you can move your resources to the Azure cloud. This approach allows you to save the cost for Azure resources ( compute and storage) because in the Visual Studio, it does not cost you for the executions.

To use these scripts in the Visual Studio, you should have _the _Azure Data Lake and Stream Analytics Tools installed. You can navigate to Visual Studio installer -> Workloads-> Data Storage and processing -> Azure Data lake and Stream Analytics.

Launch the Visual Studio 2019 and create a new U-SQL project. You get a few other templates such as Class Library, Unit Test project and sample application as well. We will work with a project template that creates a project with your USQL scripts.

#azure #sql azure #visual studio #azure data lake analytics #visual studio #u-sql

Eve  Klocko

Eve Klocko

1597019292

Beginner’s guide to Azure Machine Learning Studio using custom dataset

Before we talk about anything,how about we begin with a friendly example? When you receive an email, the provider automatically places it into the inbox/spam folders. Almost all the time, they are correctly placed in their corresponding folders while sometimes, even the mails that we wanted to see in our inbox are marked as spam 😕. But more than all this, who does this job for us? 🤔

Machine Learning is the magician in the background here!

Machine learning makes a certain task easier by learning on a set of data. The times, the mail has been correctly placed in the inbox is how accurate the model is and the times it fails to do the job, depend on how accurate the Machine Learning model is.

In simple terms, Machine Learning uses some set of algorithms to learn different examples of similar data to perform a specific task for a particular domain.

When I began to learn Machine Learning, I found this course by Google that helped me understand the concepts better. You can practice Machine Learning anywhere as far as you have a machine with good computational capacity. Now once you have a model working fine, what next? How do you put it in action? You need to deploy it somewhere, right? There are a lot of options you can choose from like either get a space on the cloud, create your own environment and deploy there or choose from existing service providers like Amazon Web ServicesMicrosoft Azure or Google Cloud.

#machine-learning-studio #azure-machine-learning #azure #machine-learning #ml-studio