The dawn of cloud computing has proven to be an essential event in this digital era. Having an on-site data centre is not a necessity now. It has led to substantial cost savings and improved agility for organizations.

A new model of business has emerged called the Infrastructure-as-a-service (IaaS) model. Here a third-party service provider takes care of providing hosting, maintaining core infrastructure which includes hardware, software, storage and servers for the customer.

Tech behemoths like Amazon, Microsoft and Google, have plunged into the market of IaaS and have upped the ante. These three bigwigs have addressed the data sovereignty and security concerns which thwarted the growth of cloud services in its initial days. The market size for IaaS is estimated to be around $32.4 billion in 2018, which was a growth of 31.3% from 2017.

Amazon has traditionally dominated the market for IaaS, but Microsoft and Google are quickly gaining ground. While the new CEO Satya Nadella has ushered Microsoft into a “Cloud first” era, Google is not much behind in the race with its GCP(Google cloud platform). Alphabet, Google’s parent company has spent $ 6 billion on R&D in the fourth quarter of 2018. Which is a 40% increase YOY, most of the spending was on future technologies like cloud and AI.

Microsoft has increased prices of its on-site only office 2019 packages by 10%, announcing a clear push towards the cloud strategy.

With competition rife amongst the “Big Three” of the cloud computing world, customers are bound to benefit. In this article, we shall compare the three cloud services provided by Microsoft, Amazon, and Google

Computing power

AWS

The leading computing service of the Amazon web services framework is the amazon elastic compute cloud. The database administrators can optimize for costs using the ECC with other Amazon web services which promote the right amount of flexibility and compatibility. The ECC platform could be scaled up or scaled down within minutes allowing the administrators to optimize their resources. The ECC allows the administrators to deploy thousands of server instances immediately.

Amazon gives you the power of machine learning using its AWS auto-scaling monitor. The monitor continually monitors your current requirements and adjusts its capacity accordingly, without increasing the price. Amazon guarantees 99.99% service availability as a part of their service level agreement(SLA).

Apart from this, Amazon offers Amazon Elastic Container service, which supports Docker containers. With this feature, you could manage the IP address of your website, access security groups, Cloudtrail logs, Cloudwatch events and Query the state of your application.

Azure

A network of virtual machines powers the Azure computing feature, which includes development, app deployment, datacenter extensions, and testing. The Microsoft Azure is compatible with Windows, SQL, SAP, Oracle and Linux. Azure offers a hybrid model consisting of an on-premise data centre and a public cloud.

A serverless container system called the Azure Kubernetes Service(AKS) allows containerized applications which can be deployed and managed faster. The AKS allows for continuous delivery and continuous integration experience. It will enable various teams working in a virtual office to work on a single platform.

AI and Machine learning tools

AWS Machine learning

Amazon is ahead in the race for integrating IoT and AI into the cloud. Amazon’s lex interface allows you to use the same technology which has is used in its groundbreaking voice assistant Alexa. Amazon even allows you to use the power of Sagemaker, and you can use it for deploying machine learning and for staff training. Amazon’s Lambda serverless environment is a boon for companies which who wish to completely untether themselves and deploy their apps from Amazon’s serverless infrastructure.

In 2015 Amazon launched its machine learning service, which helps developers in creating machine learning models. One year later, Amazon launched services like AWS Rekognition and Polly.

Microsoft has its own resource for machine learning called the Microsoft Azure Machine learning studio. The benefit of Azure Machine learning studio is that it allows the developers to use complex machine learning models through a simple graphical UI.

Storage

Storage is one of the critical functions of any cloud service. Both Azure and AWS have excellent storage capabilities, with both service providers giving necessary facilities like REST API and server-side data encryption. Blob storage is the name given by Azure to its storage mechanism while the storage mechanism of AWS is called S3(simple storage service). Automatic replication across various regions and high availability are the characteristics of the AWS storage solution. Both AWS and Azure use the block-storage function. In the block-storage service, the data is divided into small, equally sized pieces of data called blocks. This allows for faster access to the data. Amainfrastructurezon EBS(elastic block storage) is the block storage service of AWS, which acts as a primary storage device for Amazon EC2. While there are Azure virtual disks which connect to the Azure Virtual Machines using block storage.

Both AWS and Azure provide you with high availability through replication of your VM files to various different zones. In case your VM is damaged it is replicated quickly. AWS even gives you the option of taking snapshots of the VM to use them as backups at an extra fee.

Azure provides you with the option of launching your own operating system using a VHD file. You can upload the VHD file to a blob and launch it as a VM. The thing is that once you delete your VM in Azure, the uploaded VHD file also becomes unusable. This is not the case with AWS.

Hybrid

Hybrid cloud is a strategy in which companies choose to use a combination of different infrastructure environments like public cloud service providers and on-site servers. This approach is taken by companies who cannot afford to use 100% cloud infrastructure due to data residency concerns for e.g.:- banks.

Microsoft is well- established amongst enterprises as a good option for hybrid cloud infrastructure. Through Azure stack, businesses can easily use various Azure cloud service through their own data centre. The azure stack provides you with the freedom of deploying your applications either on Azure cloud or on your own datacentre without the hassle of rewriting the code.

Using Azure stack, your company can avail a host of services like virtual machines, networking, storage, load balancing, VPN gateway, containers, functions and active directories on your own datacenters. The hardware support is provided by a lot of vendors like Dell, Cisco, Lenovo, and Huawei. The pricing is flexible, starting at rates as low as $0.008 per virtual CPU per hour.

AWS launched its own hybrid infrastructure by the name of Outposts in 2018 at reinventing conference. An AWS Outpost is a fully managed infrastructure service wherein AWS provides a set of pre-configured hardware and software to the on-site location of the customer. These racks use the same equipment which powers AWS in all the regions that amazon services. These Outposts can be configured with a variety of EC2 instances and EBS volume storages. Customers can utilize AWS outposts to launch and manage a range of AWS services like ALB for load balancing, ECS, EKS for containers and EMR for big data along with RDS. Outposts allow the customers to use the same AWS management console, SDK(Software Development Kit) and CLI(Command-Line Interface) tools which AWS provides today.

Pricing

Pricing acts as a significant determinant for those who are considering a move to the cloud infrastructure. With competition rife between various cloud infrastructure providers, the pricing has seen a constant downward trend. If you take a look, the prices are roughly the same, but a detailed comparison is difficult as both offer slightly different pricing models and come up with many special offers and discounts to lure more users.

Both Azure and AWS offer free to try services which let you test the cloud waters, helping you in deciding whether the cloud is for you.

Customer base

It helps if there are proven use cases of companies using cloud infrastructure successfully. Being the oldest service provider in the IaaS sector, AWS is at a clear advantage here with names such as Netflix, Astrazeneca, Newscorp, Airbnb, Nike, Lonely Planet and Pfizer amongst an extensive list of customers who have chosen AWS as their preferred partner.

Azure has also got reputed customers in its kitty, which includes Ford, NBC News, Easyjet, Pearson, Wallmart, Twitter, Verizon, to name a few.

Conclusion

The choice of the ideal IaaS provider really depends on your needs, and there is no one-size-fits-all solution here. Both the solutions are offered by world-renowned companies who provide a high-level of security, free look-in services for you to try, excellent support and pay-as-you-use pricing.

AWS will suit your company if you want to go with a company which has the highest experience with cloud. AWS also has a more significant global reach than Azure. AWS offers better flexibility and a more comprehensive range of services than Azure. AWS is especially cos-effective and suitable if you are a large organization.

Azure will prove to be a good fit for companies who have most of their apps and platforms on Microsoft products like Windows and if you are a startup who is migrating to cloud for the first time.

The post The Cloud infrastructure race who is better: AWS or Azure? appeared first on Artificial Intelligence.

• In 2017 Google outspent Microsoft, Apple, and Facebook on R&D spending with the majority being on AI and machine learning.
• Google needs new AI- and machine learning-driven businesses that have lower Total Acquisition Costs (TAC) to offset the rising acquisition costs of their ad and search businesses.
• One of the company’s initial forays into AI and machine learning was its $600M acquisition of AI startup DeepMind in January 2014.
• Google has launched two funds dedicated solely to AI: Gradient Ventures and the Google Assistant Investment Program, both of which are accepting pitches from AI and machine learning startups today.
• On its Q4’17 earnings call, the company announced that its cloud business is now bringing in $1B per quarter. The number of cloud deals worth $1M+ that Google has sold more than tripled between 2016 and 2017.
• Google’s M&A strategy is concentrating on strengthening their cloud business to better compete against Amazon AWS and Microsoft Azure.

These and many other fascinating insights are from CB Insight’s report, Google Strategy Teardown (PDF, 49 pp., opt-in). The report explores how Alphabet, Google’s parent company is relying on Artificial Intelligence (AI) and machine learning to capture new streams of revenue in enterprise cloud computing and services. Also, the report looks at how Alphabet can combine search, AI, and machine learning to revolutionize logistics, healthcare, and transportation. It’s a thorough teardown of Google’s potential acquisitions, strategic investments, and partnerships needed to maintain search dominance while driving revenue from new markets.

Key takeaways from the report include the following:

• Google needs new AI- and machine learning-driven businesses that have lower Total Acquisition Costs (TAC) to offset the rising acquisition costs of their ad and search businesses. CB Insights found Google is experiencing rising TAC in their core ad and search businesses. With the strategic shift to mobile, Google will see TAC escalate even further. Their greatest potential for growth is infusing greater contextual intelligence and knowledge across the entire series of companies that comprise Alphabet, shown in the graphic below.

CB INSIGHT’S REPORT, GOOGLE STRATEGY TEARDOWN

• Google has launched two funds dedicated solely to AI: Gradient Ventures and the Google Assistant Investment Program, both of which are accepting pitches from AI and machine learning startups today. Gradient Ventures is an ROI fund focused on supporting the most talented founders building AI-powered companies. Former tech founders are leading Gradient Ventures, assisting in turning ideas into companies. Gradient Venture’s portfolio is shown below:

HTTPS://GRADIENT.GOOGLE/

• In 2017 Google outspent Microsoft, Apple, and Facebook on R&D spending with the majority being on AI and machine learning. Amazon dominates R&D spending across the top five tech companies investments in R&D in 2017 with $22.6B. Facebook leads in percent of total sales invested in R&D with 19.1%.

CB INSIGHT’S REPORT, GOOGLE STRATEGY TEARDOWN

• Google AI led the development of Google’s highly popular open source machine software library and framework Tensor Flow and is home to the Google Brain team. Google’s approach to primary research in the fields of AI, machine learning, and deep learning is leading to a prolific amount of research being produced and published. Here’s the search engine for their publication database, which includes many fascinating studies for review. Part of Google Brain’s role is to work with other Alphabet subsidiaries to support and lead their AI and machine learning product initiatives. An example of this CB Insights mentions in the report is how Google Brain collaborated with autonomous driving division Waymo, where it has helped apply deep neural nets to vehicles’ pedestrian detection The team has also been successful in increasing the number of AI and machine learning patents, as CB Insight’s analysis below shows:

CB INSIGHT’S REPORT, GOOGLE STRATEGY TEARDOWN

• Mentions of AI and machine learning are soaring on Google quarterly earnings calls, signaling senior management’s prioritizing these areas as growth fuel. CB Insights has an Insights Trends tool that is designed to analyze unstructured text and find linguistics-based associations, models and statistical insights from them. Analyzing Google earnings calls transcripts found AI and machine learning mentions are soaring during the last call.

CB INSIGHT’S REPORT, GOOGLE STRATEGY TEARDOWN

• Google’s M&A strategy is concentrating on strengthening their cloud business to better compete against Amazon AWS and Microsoft Azure. Google acquired Xively in Q1 of this year followed by Cask Data and Velostrata in Q2. Google needs to continue acquiring cloud-based companies who can accelerate more customer wins in the enterprise and mid-tier, two areas Amazon AWS and Microsoft Azure have strong momentum today.

CB INSIGHT’S REPORT, GOOGLE STRATEGY TEARDOWN

The post Google Needs To Make Machine Learning Their Growth Fuel appeared first on Artificial Intelligence.