Ingesting Data & Managing Datasets (Part 2 of 2)

By Ashwin Venugopal -

 






To read part 1, please click here




Exploring Options for Storing Training Data in Azure

Database systems can be classified according to the type of data and data access into the following types:
  • Relational Database Management Systems (RDBMSs)- They are generally used to store normalized transactional data via B-tree-based ordered indices and the joining of multiple rows with multiple columns may lead to typical queries filter, group, and aggregate. Azure can support various RDBMSs, such as Azure SQL Database, as well as Azure Database for PostgreSQL and MySQL.

  • NoSQL- These are Key-value-based storage systems used for storing de-normalized data with hash-based or ordered indices. Typical queries can access a single record via a collection distributed according to a partition key. Azure can support various NoSQL-based services like Azure cosmos DB and Azure Table storage.

Hence, both the database technologies can be used to store data for machine learning, according to your use cases. While RDBMSs are good for storing training data for machine learning, NoSQL systems are great for storing lookup data like training labels, or ML results such as recommendations, predictions, or feature vectors. 

Creating a Datastore & Ingesting Data

Creating Blob Storage & Connecting it with the Azure ML Workspace

Creating Blob Storage

  • Firstly, navigate a terminal of your choice then, login to Azure, and check if you are working on correct subscription. 

  • In order to create a storage account, we have to explore the available options and required settings by running this command-

 $ az storage account create -h

  • Now, we create our storage account and choose a globally unique name for it.

  • Finally, we have to create a container in our new blob storage.

Now, after completing this part, we can connect the storage to our Azure ML workspace.

Creating a Datastore in Azure ML

  • First of all, we have to understand all the requirements to create a datastore via this command-

az ml datastore create -h

Its output will help us to understand that the name of the resource group, name of the ML workspace, and a YAML file is required. 

  • For YAML file, we will navigate through https://docs.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-blob, to locate the required schema of our file with some examples, in which the most secured are the ones with limited access via an SAS token.

  • After that we can either download the blobdatastore.yml file from the GitHub repository or create a file with the same name.

  • Now, we have to generate an SAS token as well as the permissions to add, create, delete, list, read, and write in the mlfiles container. Users either choose an expiration data that's far enough in the future or normally select a much shorter expiry to rotate this key accordingly.

  • Navigate to the directory the YAML file is in to finally create the datastore in Azure ML workspace. 

These above steps ensures that we have registered a datastore connected to our blob storage using an SAS token.








To read part 1, please click here


























Comments

Post a Comment

Popular posts from this blog

Deployment (Part 3)

By Ashwin Venugopal -
Image
  To read part 1, please click  here To read part 2, please click  here Microsoft Monitoring Agent (MMA) MMA is used across multiple Microsoft solutions and has undergone a few name changes as its features and functionality have evolved. If it is being considered to be used, then following areas should be covered during a Microsoft Sentinel deployment project: Consider the timelines and the strategy for a migration to AMA (Azure Monitor Agent) before the MMA end-of-life.  Consider the central deployment and management methodology for MMA. Determine which endpoints are in scope for deployment as these affect the log ingestion volume significantly.  Azure Monitor Agent (AMA) Organizations with MMA as the main Microsoft Sentinel agent should start planning the migration to AMA as soon as possible to avoid a rushed migration. One of the main advantages of AMA is the ability to control the log collection policy centrally and apply different policies against different groups of computers, re
Read more

Project Resourcing (Part 2)

By Ashwin Venugopal -
Image
  To read part 1, please click  here Engineering - SIEM The SIEM engineers configures Microsoft Sentinel, including Log Analytics, Logic Apps, workbooks, and playbooks. They are also responsible for the following high-level tasks: Initial configuration of Azure tenant, including provisioning required resources, assigning access roles, and configuring workspace parameters like log retention, resource tagging, and blueprints.  Deployment and configuration of syslog/CEF log collection agents in appropriate locations to collect logs from on-premises devices. Generally, it will be joint effort of both system engineer and SIEM engineer, involving configuration and potential troubleshooting.  Working with system owners to enable log forwarding and configuring any required parsing of log data in Log Analytics.  Working with security operations to create and deploy KQL analytic rules to offer detections for SOC/Computer Security Incident Response Team (CSRIT) use. Tuning of alert rule parameter
Read more

Design Planning (Part 3)

By Ashwin Venugopal -
Image
  To read part 1, please click  here To read part 2, please click  here Complex Organizational Structures SIEM can be an expensive security control, hence, it is common multiple businesses to contribute to the overall expense. Various organizational units may require a level of access to specific dashboards or sets of data. Microsoft Sentinel offers the ability to assign table-level permissions and limit the level of access to the minimum required to perform requisite job functions.  Role-based Access Control (RBAC) Requirements Microsoft Sentinel offers an extensive list of Azure built-in roles that can be used to provide granular access according to the job requirements and permitted level of access. Some of them are various Microsoft Sentinel dedicated roles: Microsoft Sentinel Contributor- Can perform all engineering-related configuration, such as creating alert rules, configuring data connectors, and additional similar tasks.  Microsoft Sentinel Reader- Can query the log data stor
Read more