Delivering AIOps on the Microsoft Azure Cloud Platform

Delivering AIOps on the Microsoft Azure Cloud Platform

 Microsoft Azure is one of the largest IaaS and PaaS service offered in Business Parlance

Cloud has become a primary destination for several SMEs and large enterprises, credit to its lower costs and improved performance versus on-prem infrastructure. This development has sparked a surge in technological advancements, including machine learning (ML), cloud IT operations management (ITOM), and artificial intelligence (AI) tools.

While AI and Machine Learning has bought a transformation into the digital way of life, AIOps is redefying cloud service building and operating.  AIOps holds a broader value and encapsulates broader range including customer experience at scale, service quality assurance, boosting engineers' productivity and continuous COGS reduction. Another concern to be addressed remains the uniqueness of AIOps solutions compared to applying AI and ML into other domains.

By implementing AI and ML on Microsoft Azure, enterprises gain maximum on-

• Efficient AIOps and MLOps

AI and ML workloads are massive making Azure cloud an excellent choice for Machine Learning Operations (MLOps) and Artificial Intelligence Operations (AIOps). Microsoft Azure offers resource scalability and lower overall resource acquisition costs when compared with on-prem, driving down the business cost of enabling AIOps and MLOps in the cloud.

• Edge processing for AIOps and MLOps

In addition to being massive, AI and ML workloads are highly sensitive to read/write and web packet latency, meaning the closer users are to the data centre, the better. Edge computing is a new computing paradigm that aims to bring data centre resources closer to the user or data source, reducing latency and improving performance. This feature improves the efficiency of AIOps and MLOps on Azure, reduces costs and increases processing capacity.

• Pre-trained Machine Learning models

On Microsoft Azure platform, a custom model offers more tailored processing for niche workloads, many ML workloads can be performed using pre-trained models on this platform. These models are available through  MicrosoftML for Python and MicrosoftML for R for statistical workloads.

MS Azure AI for the Cloud

AIOps platforms accelerate issue identification and resolution by increasing proactive identification and root cause analysis (RCA) accuracy, which reduces time to resolution and assisting in improving service level agreement (SLA) adherence. This enables all IT teams to take advantage of the benefits that AIOps platforms offer, categorised as-

AI for Platform

• Self-healing that predicts failures & protect customers' workloads.

• Self-adaptation that predicts workload behaviour & adjust for efficiency.

• Self-awareness to prevent regressions.

AI for DevOps

• Triage and diagnosis support

• Programming intelligence

• Regression prevention

AI for Customers

• Customer behaviour intelligence

• Customer support intelligence

• Intelligent customer engagement

Azure Services for the Business

• Data + Analytics

Data and Analytics includes big data tools like HDInsight for Hadoop Spark, R Server, HBase and Storm clusters; Stream Analytics; Data Lake Analytics; and Power BI Embedded, among others

• AI + Cognitive Services

This service suite includes multiple tools for developing applications with artificial intelligence capabilities, like Bing Web Search, Video Indexer, Language Understanding Intelligent Service, Computer Vision API and Face API.

• Compute

This includes Service Fabric for microservices and container orchestration, and Cloud Services for building cloud-based apps and APIs, Virtual Machines, Virtual Machine Scale Sets, Functions for serverless computing and Batch for containerized batch workloads.

• Storage

Storage includes Queue, File and Disk Storage, Blob, as well as a Data Lake Store, Backup and Site Recovery, among others.

• Web + Mobile

Web + Mobile platform adds to several services for building and deploying applications, important ones include the App Service, which comprises services for Logic Apps (a low-code, data-driven service), API Apps (for creating and using APIs), Web Apps and Mobile Apps.

• Internet of Things

IoT service suite includes IoT Hub and IoT Edge services which can be combined with a variety of machine learning, communications services and analytics framework.

• Security + Identity

Includes Security Centre, Key Vault, Multi-Factor Authentication Services and Azure Active Directory.

• Developer Tools

Develop Tools includes cloud development services like HockeyApp mobile app deployment and monitoring, Visual Studio Team Services, Azure DevTest Labs, Xamarin cross-platform mobile development and more.

• Networking

Networking includes a variety of networking tools, like the Virtual Network, which can connect to on-premise data centres; Network Watcher monitoring and diagnostics; Azure DNS for domain hosting, Content Delivery Network, Traffic Manager, ExpressRoute dedicated private network fibre connections; Load Balancer; Application Gateway and VPN Gateway.

• Containers

Azure containers include Container Service, which supports Kubernetes, DC/OS or Docker Swarm, and Container Registry, as well as tools for microservices.

• Databases

Collates several SQL-based databases and related tools, as well as Cosmos DB, Table Storage for NoSQL and Redis Cache in-memory technology.

• Monitoring + Management

Monitoring + Management suite corresponds to numerous tools for managing Azure workloads and hybrid cloud environments, such as the Log Analytics, Automation, Scheduler, Microsoft Azure Port, Azure Resource Manager, and more.

• Microsoft Azure Stack

Microsoft Azure Stack includes solutions for replicating Azure infrastructure in enterprise data centres with the goal of facilitating hybrid cloud deployments.

• Enterprise Integration

EI includes multiple tools for building and managing hybrid cloud computing environments.

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