You're reading from Hands-On Machine Learning with Azure

Product typeBook

Published inOct 2018

PublisherPackt

ISBN-139781789131956

Edition1st Edition

Tools

Azure

Concepts

Machine Learning

Authors (5):

Thomas K Abraham

Parashar Shah

Jen Stirrup

Lauri Lehman

Anindita Basak

View More author details

Integration with Other Azure Services

In addition to using Azure AI services directly, Azure also provides options for using these services from other non-AI services. Many Azure AI services provide REST API interfaces that can be consumed from other services. AI services can thus be used as subcomponents of other apps to provide insights and predictions. Many non-AI services in Azure have built-in integration with AI services, so that AI components can often be added to apps with a few clicks.

Some AI services do not include any automation features. Recurring tasks, such as retraining ML models or running batch workloads, require integration with other services that offer these features. In the following sections, we will present various options for launching AI jobs automatically. In addition to traditional time-scheduled workloads, Azure services also provide objects called...

Logic Apps

Azure Logic Apps is a graphical tool for automating various types of tasks, such as getting data from a Web API and saving it in cloud storage. Logic Apps can be developed without writing a single line of code, so no programming skills are required. However, Logic Apps provides some basic functionality for programming languages, such as conditional execution and iterative loops.

Logic Apps is meant for light-weight tasks that do not require complex logic or lightning-fast performance. Such tasks could include sending an email when a SharePoint list is modified, or copying files between Dropbox and OneDrive if the files have been modified.

For AI development, Logic Apps provides a number of basic functionalities. There are built-in modules for Cognitive Services APIs and Azure Machine Learning Studio Web Services APIs. In combination with storage triggers, it is possible...

Azure Functions

While Logic Apps provides a fast way to automate tasks, its collection of actions is limited to pre-selected options that cannot be customized. Moreover, the programmability of Logic Apps is quite limited, and the development of more complex programs is not necessarily any easier than writing code. If more flexibility is needed, it might be more productive to develop applications with Azure Functions. Functions can be developed with multiple programming languages familiar to web developers.

Azure Functions is a serverless coding platform in the cloud, where the underlying operating system has been virtualized. This means that many maintenance tasks, such as updating the operating system or language versions, is managed by the platform and the user does not need to worry about those tasks. On the other hand, the user cannot change the language version used by the...

Azure Data Lake Analytics

Azure Data Lake (ADL) is Microsoft's storage and analytics service for big data. It is capable of storing data on a petabyte scale and making efficient queries on the stored data. The storage and the analytics services are separate in Azure and the ADL service actually consists of two different products: Azure Data Lake Storage (ADLS) and Azure Data Lake Analytics (ADLA). In this section, we will focus on ADLA, but we will also touch on ADLS where appropriate.

Data Lake Storage is a file-based storage, with files organized into directories. This type of storage is called schemaless, since there are no constraints on what type of data can be stored in the Data Lake. Directories can contain text files and images, and the data type is specified only when the data is read out from the Data Lake. This is particularly useful in big data scenarios where...

Azure Data Factory

Azure Data Factory (ADF) is a cloud data integration platform that allows you to automate various data-related tasks, such as copying data between data stores, running analytical workloads, and retraining machine learning models. It supports a wide range of different data stores, including products from other vendors. Via its integration runtime model, ADF can also connect to on-premises locations such as self-hosted SQL databases.

ADF can make use of many different types of computing resources in the Azure catalogue. These include Machine Learning Studio, ADLA, Databricks, and HDInsight. ADF can also make requests to any service that exposes a REST API, such as Cognitive Services.

Data Factory is developed with ADF Visual Tools, a web portal dedicated to development and management. After creating the Data Factory resource in the portal, you can open the Visual...

Summary

As we have seen in this chapter, integrating Azure AI services with other non-AI services is easy and configuring these integrations can be done in a few simple steps. For codeless approach, Logic Apps and Data Factory provide tools to automate many data-related tasks. By leveraging AI services such as Cognitive Services or ML Studio Web Services, the incoming data can be enriched with insights and predictions produced by the AI services.

The trigger-based event handling system allows you to react to different kinds of events, for example when a new file is created or modified in cloud storage. The triggers can be used to launch data processing pipelines in scenarios where data moves infrequently and schedule-based data processing might introduce lags, since the system must wait for the scheduled time to lapse. With storage-based triggers, the data pipeline can be initiated...

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Authors (5)

Thomas K Abraham

Dr. Thomas K Abraham is a cloud solution architect (advanced analytics and AI) at Microsoft in the South Central Region of the USA. Since January 2016, he's been assisting organizations in leveraging technologies such as SQL, Spark, Hadoop, NoSQL, BI, and AI on Azure. Prior to that, Thomas spent 10 years in Ecolab, where he designed algorithms for IoT devices and built solutions for anomaly detection. In the oil and gas division, he designed and built customer-facing analytics solutions for multiple super majors. His work was focused on preventing equipment failure by modeling corrosion, scale, and other stresses. He has a PhD in Chemical Engineering from The Ohio State University in 2005. His thesis focused on the use of nonlinear optimization with reaction models.
Read more about Thomas K Abraham

Parashar Shah

Parashar Shah is a Senior Program Manager in the Azure Machine Learning platform team.Currently, he works on making Azure Machine Learning services the best place to do e2e machine learning for building custom AI solutions using big data. Previously at Microsoft, he has been a Data Scientist and a Data Solutions Architect in various Cloud and AI teams. Prior to joining Microsoft, Parashar worked at Nokia Networks as a Solutions Architect & Product Manager building customer experience analytics solutions for global telcos. He also co-founded a carpooling startup, which helped employees carpool safely. He has 10+ years of global work experience. He is an alum of Indian Institute of Management, Bangalore and Gujarat University.
Read more about Parashar Shah

Jen Stirrup

Jen Stirrup is a data strategist and technologist, a Microsoft Most Valuable Professional (MVP), and a Microsoft Regional Director, a tech community advocate, a public speaker and blogger, a published author, and a keynote speaker. Jen is the founder of a boutique consultancy based in the UK, Data Relish, which focuses on delivering successful business intelligence and artificial intelligence solutions that add real value to customers worldwide. She has featured on the BBC as a guest expert on topics relating to data.
Read more about Jen Stirrup

Lauri Lehman

Lauri Lehman is a data scientist who is focused on machine learning tools in Azure. He helps customers to design and implement machine learning solutions in the cloud. He works for the software consultancy company, Zure, based in Helsinki, Finland. For the past 4 years, Lauri has specialized in data and machine learning in Azure. He has worked on many machine learning projects, developing solutions for demand estimation, text analytics, and image recognition, for example. Lauri has previously worked as an academic researcher in theoretical physics, after obtaining his PhD on topological quantum walks. He still likes to follow the progress of modern physics and is eagerly a waiting the era of quantum machine learning!
Read more about Lauri Lehman

Anindita Basak

Anindita Basak is a cloud architect with almost 15+ years of experience, the last 12 years of which she has been extensively working on Azure. She has delivered various real-time implementations on Azure data analytics, and cloud-native and real-time event-driven architecture for Fortune 500 enterprises, ranging from banking, financial services, and insurance (BFSI)to retail sectors. She is also a cloud and DataOps trainer and consultant, and author of cloud AI and DevOps books.
Read more about Anindita Basak

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