Choosing the Right Machine Learning Service in Azure (Part 1 of 4)

By Ashwin Venugopal -

 




To read part 2, please click here
To read part 3, please click here
To read part 4, please click here









Choosing an Azure Service for ML

Azure offers vast number of services, it often makes it difficult for someone new to Azure to select the right one for a specific task. Choosing the right service with the right layer of abstraction could save you months if not years of time to market your ML-based product or feature, while the wrong service may initially allow you to start producing results quickly, but, it will also make it impossible to improve the model performance for a specific domain or extend a model for other tasks.

What is the Azure Machine Learning Service?

The term Azure Machine Learning Service refers to the popular Azure service that offers capabilities in building custom ML solutions and contains various components to manage resources (like compute clusters and data storage) and assets (like datasets, experiments, models, pipelines, etc.), as well as access to these resources and assets, all within the same workspace.

The access to the other ML services (like Azure Automate Machine Learner, the Azure Machine Learning Design, etc.) sharing the same resources and assets through the ML workspace, is also offered by the Azure Machine Learning service. Due to this fact, it is also referred as the Azure Machine Learning service or the Azure Machine Learning workspace.

Managed ML Services

These type of services are generally easy to use, quick to embed into an application, and don't require any operational overhead making it the most suitable to create an AI-based application or feature without any training data, models, and operating model deployments in production. 

These services are used with pre-trained models that sometimes can be trained or fine-tuned for a specific application domain, however, if you use customized models, then, it will add to the benefits of managed services with the flexibility of custom application domain. Hence, we can further look into the Azure Cognitive Services, customizable AI services, and Azure Applied AI Services.  

 






To read part 2, please click here
To read part 3, please click here
To read part 4, please click here











































Comments

Post a Comment

Popular posts from this blog

Query, Visualize, & Monitor Data in Azure Sentinel

By Ashwin Venugopal -
Image
  Azure Sentinel Workbooks Several ready for use templates are provided by the Azure Sentinel that can be used to create your own workbook and then modify them as needed for Contoso. Most of the data connectors it uses to ingest data come with their own workbooks, but better insight can be obtained by simply looking into the data that is being ingested by using tables and visualizations, including bar and pie charts. You can also make your own workbook easily by using this data from the beginning instead of using the predefined templates. Workbook Page You can easily access the workbook page from the Azure Sentinel from the navigation pane which consists of the: Workbook header- You can add a new workbook and review the saved workbooks as well as templates that are available on the workbook page. Templates section- You can access existing workbook templates on the Templates tab. You can save some of the workbooks for quick access and they will appear on the My Workbooks tab.  From the
Read more

Planning for Implementing SAP Solutions on Azure (Part 2 of 5)

By Ashwin Venugopal -
Image
  To read part 1 please click  here To read part 3 please click  here To read part 4 please click  here To read part 5 please click  here Load Balancing The load balancer uses probe ports to determine as well as route the traffic to an active DBMS node, but of there's any failover then the most common SAP application architecture can automatically reconnect against the private virtual IP address without the need for the SAP application to reconfigure it, while the load balancer redirect the traffic against the private virtual IP address without any need for the SAP application to reconfigure. The loaf balancer also offers an option of DirectServerReturn which if configured, can help in directing the traffic from the DBMS VM to the SAP application layer directly to the application layer without routing through the load balancer. But, if it isn't configured, then the return traffic to the SAP application layer is routed through the load balancer. You can also use Azure Load Balan
Read more

Work with String Data Using KQL Statements

By Ashwin Venugopal -
Image
  Extract Data from Unstructured String Fields Unstructured string fields often contains the Security log data and requires parsing to extract data. There are multiple ways of pulling information from string fields in KQL and the two primary operators used are extract and parse.  Extract It gets a match for a regular expression from a text string. You can convert the extracted substring to the indicated type.  Arguments regex- A regular expression. captureGroup- A positive int constant indicating the capture group to extract. 0 stands for the entire match, 1 for the value matched by the first '('parenthesis')' in the regular expression, 2 or more for subsequent parenthesis.  text- A string to search. typeLiteral- An optional type literal. If provided, the extracted substring is converted to this type.  Returns If regex finds a match in text- the substring matched against the indicated capture group captureGroup, optionally converted to typeLiteral. If there's no mat
Read more