Microsoft Azure Services is one of the most popular choices for organizations that prefer both Platform-as-a-Service (PaaS) and Infrastructure-as-a-service (IaaS) computing models. Whereby, Infrastructure-as-a-service – Microsoft Azure cloud offers virtualization through its mature Hyper-V technology. For smaller organizations, its services entail tangible benefits.
From economies of scale to large-scale implementations, the services entail reduced IT maintenance costs and increased productivity. On the other hand, for Platform-as-a-service – the Microsoft Azure server renders comprehensive capabilities to your IT team. Enabling them to develop custom solutions, and manage their design, development, and deployment.
Therefore, there is absolutely no need to purchase and manage any additional server hardware, software, security, storage, or other network components used earlier to deliver custom solutions. Some organizations use Azure for data backup and disaster recovery. Organizations can also use Azure as an alternative to their own data center management strategies.
Rather than invest in local servers and storage, these organizations choose to run some, or all, of their business applications in Azure. So, what’s more, does Azure by Microsoft offer its customers? Well, we’ll lean more in a short while.
What Is Microsoft Azure?
Notably, Microsoft Azure, formerly known as Windows Azure, is Microsoft’s public cloud computing platform. It is, important to realize, that Microsoft App Virtualization provides a range of cloud services, including cloud computing, analytics, storage, and networking. Whereby, users can pick and choose from these services to develop and scale new applications.
Or even run existing applications, in the public cloud. Basically, to address issues with managing multi-cloud environments, some teams are turning to a cloud management platform to bring disparate environments under control. Microsoft Azure is widely considered both a Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) offering.
In addition, to ensure availability, Microsoft has Azure data centers located around the world. And as of January 2020, Microsoft Azure services are now available in 55 regions, spread across 140 countries.
Unfortunately, not all services are available in all regions. Therefore, Azure users must ensure that workload and data storage locations comply with all prevailing compliance requirements or other legislation.
Why You Should Consider Using Microsoft Azure
Simply, because Microsoft Azure consists of numerous service offerings. And in general, its use cases are extremely diverse. For example, running virtual machines or containers in the cloud is one of the most popular uses for Microsoft Azure. Whereby, these compute resources can host infrastructure components. Like Domain Name System (in short DNS) servers.
As well as Windows Server Services such as Internet Information Services (in short IIS), or even third-party applications. Microsoft also supports the use of third-party operating systems, such as Linux. In addition, Microsoft Azure is also commonly used as a platform for hosting databases in the cloud. Offering serverless relational databases such as Azure SQL so to say.
Equally, there are also non-relational databases such as NoSQL to consider. Microsoft Azure is also a popular platform for backup and disaster recovery. Additionally, many organizations use Azure storage as an archival solution in order to meet their long-term data retention requirements. By all means, Azure Cloud Solutions allows you to go even more steps further.
It helps you to;
Before proceeding, you can have a look at some more 11 Business Benefits of Microsoft Azure Cloud Solutions in detail.
How Does Microsoft Azure Work?
In the first place, Microsoft Azure is a subscription-based service. Once customers subscribe to Azure, they have access to all the services included in the Azure Portal. Plus key services to create cloud-based resources such as Virtual Machines and Databases.
Although Microsoft does not charge a subscription fee, the various Azure services are made available on a pay-as-you-go basis. Meaning subscribers receive a bill each month that only charges them for the specific resources they have used.
In addition, a number of third-party vendors also make software directly available through Azure. The cost billed for third-party applications varies widely but may involve paying a subscription fee for the application. Plus a usage fee for the infrastructure used to host the application. But, there are many other customer support options for Microsoft Azure consumers to note.
Eventually, these customer support plans vary in terms of scope and price. So, for your information, basic support is available to all Azure accounts. But Microsoft charges a fee for the other support offerings.
Then again, Developer Support costs $29 per month, while Standard support costs $100 per month and Professional Direct support is $1000 per month. Microsoft does not disclose the pricing for Premier support.
The Main Microsoft Azure Products And Services For You To Consider
Technically, data-related problems remain a dangerous Artificial Intelligence (AI) killer. By focusing on data accessibility and integration through AI-optimized cloud infrastructure and accelerated, full-stack hardware and software, enterprises can increase their success rate. By developing and deploying apps and capabilities to deliver business value faster and surely.
To this end, investing in research and development to define and test scalable infrastructure is a crucial key to scaling a data-dependent AI project into profitable production. Generally, security and privacy are built into the Azure platform. Microsoft is committed to the highest levels of trust, transparency, standards conformance, and regulatory compliance.
And, with the most comprehensive set of compliance offerings of any cloud service provider, this can all be achieved. Microsoft Azure offers solution partners that can help deploy and manage your existing solutions, as well as offer ready-made or custom solutions for you. Plus, you can find an experienced managed service partner that can help drive your business.
Or even, have your existing outsourcing partner become an Azure partner. As a matter of fact, it’s clear to note that almost 90% of Fortune 500 companies trust their business to run on the Microsoft Cloud and are doing amazing things with it. Whilst, keeping in mind, that Microsoft sorts Azure cloud services into nearly unlimited categories for you to consider such as:
1. Compute, Mobile & Websites
The Compute Services enable a user to deploy and manage VMs, containers, and batch jobs, as well as support remote application access. Compute resources created within the Azure cloud can be configured with either public IP addresses or private IP addresses, depending on whether the resource needs to be accessible to the outside world.
The Mobile Products help developers build cloud applications for mobile devices, providing notification services, and support for back-end tasks. As well as creative tools for building Application Program Interfaces (APIs) Key, and the ability to couple geospatial analysis based on context with data. There are also Web Services for app development and deployment.
2. Data Storage & Analytics
Under the Storage category of services, it provides scalable cloud storage for structured and unstructured data while mining and managing it. It also supports big data projects, persistent storage, and archival storage. In the same fashion, some Web Services also offer features for search, content delivery, API management, notification, and reporting.
It provides distributed analytics and storage, as well as features for real-time analytics, big data analytics, data lakes, Machine Learning (ML), Business Intelligence (BI), and the Internet of Things (IoT) in terms of data streams, and data warehousing.
3. Networking & Content Delivery
This group includes virtual networks, dedicated connections, and gateways, as well as services for traffic management and diagnostics, load balancing, DNS hosting, and network protection against Distributed Denial-of-Service (DDoS) attacks. For the Media and Content Delivery Network (CDN), these CDN services include on-demand streaming and the like.
As well as digital rights protection, encoding and media playback, and indexing. Artificial Intelligence (AI) and Machine Learning are a wide range of services a developer can use to infuse artificial intelligence. In addition, they may also help in terms of machine learning, and cognitive computing capabilities in applications and data sets.
4. Integration, Identity & Internet
The Integration services are for server backup, site recovery, and connecting private and public clouds. Offerings on Identity ensure only authorized users can access Azure services and help protect encryption keys and other sensitive information in the cloud. Services include support for Azure Active Directory and Multifactor Authentication (MFA).
As the Internet of Things (IoT), these services help users capture, monitor and analyze IoT data from sensors and other devices. Services include notifications, analytics, monitoring, and support for coding and execution.
5. DevOps, Development & Security
The DevOps group provides project and collaboration tools like Azure DevOps — Formerly Visual Studio Team Services — facilitate DevOps software development processes. It also offers features for app diagnostics, DevOps tool integrations, and test labs for build tests and experimentation. In the Development category, these services also help app developers.
Through the Security products, it provides capabilities to identify and respond to cloud security threats, as well as manage encryption keys and other sensitive assets.
6. Containers, Databases & Migration
Containers help an enterprise create, register, orchestrate, and manage huge volumes of containers in the Azure cloud, using common platforms like Docker, Kubernetes, etc. As for the Databases, it includes Database-as-a-Service (DBaaS) offerings for SQL and NoSQL, as well as other database instances — such as Azure Cosmos DB and Azure Database for PostgreSQL.
It also includes Azure SQL Data Warehouse support, caching, and hybrid database integration and migration features. Azure SQL is the platform’s flagship database service. Azure SQL is a relational database that provides SQL functionality without the need for deploying a SQL server.
7. Management, Governance & Mixed Reality
Management and Governance are services that provide a range of backup, recovery, compliance, automation, scheduling, and monitoring tools. Helping a cloud administrator manage an Azure deployment. There are also mixed reality services.
Where developers create content for the Windows Mixed Reality environment. Also, the migration suite helps an organization estimate workload migration costs and perform the actual migration of workloads from local data centers to the Azure cloud.
8. Blockchain & Intune
The Azure Blockchain Service allows you to join a blockchain consortium or to create your own. Equally important, Microsoft Intune can be used to enroll user devices, making it possible to push security policies and mobile apps to those devices. Mobile apps can be deployed either to groups of users or to a collection of devices.
Intune also provides tools for tracking which apps are being used. A remote wipe feature allows the organization’s data to be securely removed from devices. Without removing a user’s mobile apps in the process.
9. Agile Business Communication Services
According to a 2020 report by Grand View Research, the contact center software market is anticipated to grow to $72.3 billion by 2027. At Ignite 2020, Microsoft announced a very brand new communications — that’s offering Azure Communication Services that are built on top of Azure. Microsoft says it leverages the same network powering Microsoft Teams.
More so, it enables design developers to add multimodal messaging to applications and websites — while tapping into services like Azure Cognitive Services for translation, sentiment analysis, and more. With customer representatives required to work from home during the COVID-19 Pandemic, most companies are turning to managed solutions to bridge the gaps in service.
The platforms aren’t perfect, but COVID-19 has accelerated the demand for distributed contact center setups. Particularly, those powered by AI. Amazon — recently launched an AI-powered contact center product — Contact Lens — in general availability alongside several third-party solutions to achieve optimal service solutions delivery.
On the same note, Google continues to expand Contact Center AI, which automatically responds to customer queries and hands them off to a person when necessary.
10. Azure Communication Services APIs
In this remote-first world, businesses are looking to quickly adapt to customers’ needs and connect with them. More so, through engaging communication experiences. Every day, there’s always a new challenge that changes customer, developer, and business needs. The main goal of Microsoft Azure is to meet businesses where they are.
And then, provide limitless solutions. As well as other key services to help them be resilient and move their business forward in today’s market. By doing so, we see rich communication experiences — enabled by voice, video, chat, and SMS — continuing to be an integral part of how businesses connect with their customers across devices and platforms.
Today, voice, video, and chat capabilities are available through Microsoft Azure Communication Services APIs and software development kits. Followed by SMS and phone number support to help shift between voice and video calls instantaneously and launch chats with a single click. The SMS tool integrates with existing Azure services like Logic Apps and Event Grid.
As for phone number support, it extends to provisioning capable of inbound and outbound calls and porting existing numbers. While requesting new numbers, and working with on-premises equipment and carrier networks via a SIP.
Data Is Choking Artificial Intelligence — How To Break Free
AI is a voracious, data-hungry beast. Unfortunately, problems with that data — quality, quantity, velocity, availability, and integration with production systems — continue to persist as a major obstacle to successful enterprise implementation of the technology. The requirements are easy to understand, and notoriously hard to execute.
Like delivering usable, high-quality inputs for AI applications and capabilities to the right place in a dependable, secure, and timely (often real-time) way. Nearly a decade after the challenge became apparent, many enterprises continue to struggle with AI data: Too much, too little, too dirty, too slow, and siloed from production systems.
The result is a landscape of widespread bottlenecks in training, inference, and wider deployment, and most seriously, poor ROI. According to the latest industry studies, data-related issues underlie the low and stagnant rate of success (around 54%, Gartner says) in moving enterprise AI proof of concepts (POCs) and pilots into production.
Data issues are often behind related problems with regulatory compliance, privacy, scalability, and cost overruns. These can have a chilling effect on AI initiatives — just as many organizations are counting on technology and business groups to quickly deliver meaningful business and competitive benefits from AI.
Data Management Is Central To AI Success — And Failure
Given the rising expectations of CEOs and boards for double-digit gains in efficiencies and revenue from these initiatives, freeing data’s chokehold on AI expansion and industrialization must become a strategic priority for enterprises. But how? The success of all types of AI depends heavily on availability, and the ability to access usable and timely data.
That in turn, depends on an AI infrastructure that can supply data and easily enable integration with production IT. Emphasizing data availability and fast, smooth meshing with enterprise systems will help organizations deliver more dependable, more useful AI applications and capabilities. Many factors can torpedo or stall the success of AI.
In terms of development and expansion. Such as lack of executive support and funding, poorly chosen projects, security and regulatory risks, and staffing challenges, especially with data scientists. Yet, in numerous reports over the last seven years, data-related problems remain at or near the top of AI challenges in every industry and geography.
Unfortunately, the struggles continue. A major new study by Deloitte, for example, found that 44% of global firms surveyed faced major challenges both in obtaining data and inputs for model training and in integrating AI with organizational IT systems. The seriousness and centrality of the problem is obvious. Data is the raw fuel (input).
It is also a refined product (output) of AI. To be successful and useful, AI needs a reliable, available, high-quality source of data. Unfortunately, an array of obstacles may still plague many enterprises.
Lack of data quality and observability
GIGO (garbage in/ garbage out) has been identified as a problem since the dawn of computing. The impact of this truism gets further amplified in AI, which is only as good as the inputs used to train algorithms and run them. One measure of the current impact: Gartner estimated in 2021 that poor data quality costs organizations an average of $12.9 million/year.
Suffice it to say, the loss is certainly higher today. Data observability refers to the ability to understand the health of data and related systems across data, storage, computing, and processing pipelines. It’s crucial for ensuring data quality and reliable flow for AI data that are ingested, transformed, or pushed downstream. Specialized tools can come in handy.
More so, to provide an end-to-end view needed to identify, fix and otherwise optimize problems with quality, infrastructure, and processing. The task, however, becomes much more challenging with today’s larger and more complex AI models. It can be fed by hundreds of multi-layered data sources, both internal and external, and interconnected data pipelines.
Nearly 90% of respondents in the Gartner study say they have or plan to invest in data observability and other quality solutions. At the moment, both remain a big part of AI’s data problem.
Poor data governance
The ability to effectively manage the availability, usability, integrity, and security of data used throughout the AI lifecycle is an important but under-recognized aspect of success. It’s good to adhere to policies, procedures, and guidelines that help ensure proper data management — this is quite crucial for safeguarding the integrity and authenticity of data sets.
It makes it much more difficult to align AI with corporate goals. It also opens the door to compliance, regulatory and security problems such as data corruption and poisoning, which can produce false or harmful AI outputs.
Lack of data availability
Accessing data for building and testing AI models is emerging as perhaps the most important data challenge to AI success. Recent studies by the McKinsey Global Institute and U.S. Government Accountability Office (GAO) both highlight the issue as a top obstacle to broader expansion and adoption of AI.
A study of enterprise AI published in the MIT Sloan Management Journal entitled “The Data Problem Stalling AI” concludes that many people focus on the accuracy and completeness of data. Given the degree to which it is accessible by machines — per data quality dimensions. It appears to be a bigger challenge in taking AI out of the lab and into the business.
The Basic Strategies For Data Success In Artificial Intelligence
To help avoid these and other data-based showstoppers, enterprise business, and technology leaders should consider:
Thinking about the big-picture data availability from the start
Many accessibility problems result from how AI is typically developed in organizations today. Specifically, end-to-end availability and data delivery are seldom built into the process. Instead, at each step, different groups have varying requirements for data. Rarely does anyone look at the big picture of how data will be delivered.
And then, be used in production systems. In most organizations, that means the problem gets kicked down the road to the IT department, where late-in-the-process fixes can be more costly and slow. A cloud-based infrastructure optimized for AI provides a foundation for unifying development and deployment across the enterprise.
Whether deployed on-premises or in a cloud-based data center, a “purpose-built” environment also helps with a crucial related function: enabling faster data access with less data movement.
Focus on AI infrastructure that integrates data and models with production IT systems
The second crucial part of the accessibility/availability challenge involves delivering quality data in a timely fashion to the models and systems where it will be processed and used. An article in the Harvard Business Review, “The Dumb Reason Your AI Project Will Fail”, puts it this way:
“It’s very hard to integrate AI models into a company’s overall technology architecture. Doing so requires properly embedding the new technology into the larger IT systems and infrastructure — a top-notch AI won’t do you any good if you can’t connect it to your existing systems.
The authors go on to conclude: “You want a setting in which software and hardware can work seamlessly together, so a business can rely on it to run its daily real-time commercial operations… Putting well-considered processing and storage architectures in place can overcome throughput and latency issues.”
As a key first step, McKinsey recommends shifting part of spending on R&D and pilots towards building infrastructure that will allow you to mass produce and scale your AI projects. The consultancy also advises the adoption of MLOps and ongoing monitoring of the data models being used.
Balanced, Accelerated Infrastructure Feeds The AI Data Beast
According to a 2020 report by Grand View Research, the contact center software market is anticipated to grow to $72.3 billion by 2027. As enterprises deepen their embrace of AI and other data-driven, high-performance computing, it’s critical to ensure that performance and value are not starved by underperforming processing, storage, and networking.
When developing and deploying AI, it’s crucial to look at computational requirements for the entire data lifecycle: starting with data prep and processing (getting the data ready for AI training), then during AI model building, training, and inference. Select the right compute infrastructure (or platform) for the end-to-end lifecycle and optimize for performance.
It has a direct impact on the TCO and hence ROI for AI projects. End-to-end data science workflows on GPUs can be up to 50x faster than on CPUs. To keep GPUs busy, data must be moved into processor memory as quickly as possible. Depending on the workload, optimizing an application to run on a GPU, with I/O accelerated in and out of memory matters.
Obviously, this is because it helps achieve top speeds and maximize processor utilization. Since data loading and analytics account for a huge part of AI inference and training processing time, optimization here can yield 90% reductions in data movement time. For example, because many data processing tasks are parallel, it’s wise to use GPU acceleration.
Particularly, for Apache Spark data processing queries. Just as a GPU can accelerate deep learning workloads in Artificial Intelligence, speeding up extract, transform and load pipelines can produce dramatic improvements here.
Storage I/O (Input/Output) performance is crucial for AI workflows, especially in the data acquisition, preprocessing, and model training phases. How quickly data can be read from varied sources and transferred to storage mediums further enables differentiated performance. Storage throughput is critical to keep GPUs from waiting on I/O.
Be aware that AI training (time-consuming) and inference (I/O heavy and latency-sensitive) have different requirements for processing and storage access behavior with I/O. For most enterprises, local NVMe +BLOB is the best, most cost-effective choice here. Consider Azure Managed Lustre and Azure NetApp Files if there’s not enough local NVMe SSD capacity.
Or rather, if the AI needs a high-performance shared filesystem. Choose Azure NetApp Files over Azure Managed Lustre if the I/O pattern requires a very low-latency shared file system.
Another high-impact area for optimizing data accessibility and movement is the critical link and transit path between storage and cloud computing. Traffic clogs here are disastrous. High-bandwidth and low-latency networking like InfiniBand is crucial to enabling training at scale. It’s especially important for large language models (LLM) deep learning.
Especially, where performance is often limited by network communication. When harnessing multiple GPU-accelerated servers to cooperate on large AI workloads, communications patterns between GPUs can be categorized as point-to-point or collective communications. Many point-to-point communications may happen simultaneously in an entire system.
More so, between sender and receiver and it helps if data can travel fast on a “superhighway” and avoid congestion. Collective communications, generally speaking, are patterns where a group of processes participates, such as in a broadcast or a reduction operation. It’s important to note that high-volume collective operations are found in most AI.
Intelligent Communication Software must get data to many GPUs repeatedly during a collective operation by taking the fastest, shortest path and minimizing bandwidth. That’s the job of communication acceleration libraries like NCCL (NVIDIA). And, this is usually found extensively in deep learning frameworks for efficient neural network training.
High-Bandwidth Networking optimizes the network infrastructure to allow multi-node communications in one hop or less. And since many data analysis algorithms use collective operations, using in-network computing can double the network bandwidth efficiency. Having a high-speed network adapter per GPU for your network infrastructure allows AI workloads.
Beyond setting up a strong infrastructure to support the end-to-end lifecycle of putting data to use with AI, regulated industries like healthcare and finance face another barrier to accelerating adoption. The data they require to train AI/ML models are often sensitive and subject to a rapidly evolving set of protection and privacy laws (GDPR, HIPAA, CCPA, etc.).
Confidential Computing secures in-use data and AI/ML models during computations. This ability to protect against unauthorized access helps ensure regulatory compliance and unlocks a host of cloud-based AI use cases previously deemed too risky. To address data volume and quality, synthetic data, generated by simulations or algorithms, can come in handy.
More so, to help save time and reduce the costs of creating and training accurate AI models. The launch of Azure Communication Services follows the debut of the call center platform Genesys Engage. It runs on Azure for a cooperative customer experience solution with Teams, Dynamics 365, and Azure Cognitive Service integrations.
How Much Does Microsoft Azure Cost?
Similar to other public cloud providers, Azure primarily uses a pay-as-you-go pricing model that charges based on usage. However, if a single application uses multiple Azure services, each service might involve multiple pricing tiers. In addition, if a user makes a long-term commitment to certain services, such as compute instances, Microsoft offers a discounted rate.
Given the many factors involved in cloud service pricing, an organization should review and manage its cloud usage to minimize costs. Azure-native tools, such as Azure Cost Management, can help monitor, visualize, and optimize cloud spending. It’s also possible to use third-party tools, such as Cloudability or RightScale, to manage Azure resource usage and associated costs.
Microsoft Azure is one of several major public cloud service providers operating on a large global scale. Other major providers include Google Cloud Platform (GCP), Amazon Web Services (AWS), IBM Bluemix, and the like. Currently, there is a lack of standardization among cloud services and capabilities.
Meaning no two cloud providers offer the same service in the exact same way, using the same APIs or integrations. This makes it difficult for a business to use more than one public cloud provider when pursuing a multi-cloud strategy in their strategies. Third-party cloud management tools can reduce some of these challenges.
Microsoft first unveiled its plans to introduce a cloud computing service called Windows Azure in 2008. Whereby, the preview versions of the service became available and matured, leading to its commercial launch in early 2010. Fortunately, its early iterations of Azure cloud services fell behind more established offerings like AWS, and the portfolio continued to evolve.
While, at the same time, offering support to a larger base of programming languages, frameworks, and operating systems. By early 2014, Microsoft recognized that the implications of cloud computing stretched far beyond Windows, and the service was rebranded as Microsoft Azure. Majorly, there are data security concerns, and regulatory compliance to care for.
Plus other key requirements — often make privacy a major issue for cloud subscribers. To address these worries, Microsoft Online Trust Center has been created. Mainly, which provides detailed information about the company’s security, privacy, and compliance initiatives. According to the Trust Center, Microsoft will only use customer data if it is necessary.
Other More Related Resource References:
- Hyper-V Versus Hyper-V Technology Overview
- Infrastructure-as-a-Service (IaaS) | Key Products & Vendors
- Software-as-a-Service (SaaS) | Its Key Products & Vendors
- AWS Marketplace | How Amazon Cloud Computing Works
- Platform-as-a-Service (PaaS) | Its Key Products & Vendors
By all means, we hope that the above-revised guide on Microsoft Azure for your business cloud computing needs was helpful and supportive enough. But, if you’ll prefer a personal touch, please Contact Us at any time and let us know how we can come in. Or rather, share your burning questions, contributions, suggestions, or recommendations in our comments section.