In the midst of this pandemic, what is allowing us unprecedented flexibility in making faster technological advancements is the availability of various competent cloud computing systems. From delivering on-demand computing services for applications, processing and storage, now is the time to make the best use of public cloud providers. What’s more, with easy scalability there are no geographical restrictions either.
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Machine Learning systems can be indefinitely supported by them as they are open-sourced and within reach now more than ever with increased affordability for businesses. In fact, public cloud providers are increasingly helpful in building Machine Learning models. So, the question that arises for us is – what are the possibilities for using them for deployment as well?
What do we mean by deployment?
Model building is very much like the process of designing any product. From ideation and data preparation to prototyping and testing. Deployment basically is the actionable point of the whole process, which means that we use the already trained model and make its predictions available to users or other systems in an automated, reproducible and auditable manner.
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While a lot of cloud providers have created customised and dedicated ML stacks, there are on-premise server services and Heroku, which provides a ready and secure environment that allows you to deploy faster. There are however, challenges cloud providers face collectively.
What are the challenges?
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Deployment is hard!
Contrary to general belief, you’re not only deploying code, you’re also, in essence deploying data that moves between various departments, in various formats, that change as the model changes, and there are a ton of moving variables in the system that can be vulnerable to that.
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There is no homogeneity
End-to-end ML applications are often full of components written in different programming languages. The choice of a programming language is dependent on the use case and Python, R, Scala, or any other language can be used to build different models.
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ML deployments aren’t monolithic
Machine learning model deployments are not necessarily self-contained solutions. They are commonly embedded or integrated into various business applications.
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Testing and validation pain points
Data changes result in an evolution process for models for which methods improve or software dependencies change. Every time such a change occurs, model performance needs to be re-validated.
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Complexity of release strategies
Depending on the use case, ML models need to be updated more frequently than regular software applications.
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Data security issues
With data being a vulnerable resource, the open-sourced nature of cloud providers does raise some eyebrows. A lot of banking sector companies have been apprehensive about using the cloud because of data security issues.
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The top three contenders
AWS, Google Cloud and Microsoft Azure which are the top three contenders in the cloud market, can be compared on a few important parameters to make the best choice.
AWS
According to Gartner’s Magic Quadrant report, AWS is ranked highest in terms of both vision and ability to execute. What makes it so is AWS’s approach that it truly democratises AI by delivering tools and services that enable all developers even those who have no prior experience of ML. It’s even attractive for small businesses as the pricing is based on usage not a blanket fee. Additionally there’s a lot of room for flexibility, customisation and support for third-party integrations.
Google Cloud
Google is committed to making AI accessible to all. Google has been open-sourcing its AI/ML tools and engineers have been actively putting out their research for everyone to access. Cybersecurity is a critical area where Google is employing AI/ML to solve business problems.
Chronicle, a subsidiary of Alphabet (Google’s parent company), is all set to leverage Google’s AI/ML expertise and provide near limitless computing power to develop a world-class security analytics solution. It can be easily integrated with other Google services. A really huge cost-saving discount that Google Cloud offers are SUDs or Sustained Use Discounts. These are automatic discounts that Google Cloud Platform provides for the period of time one uses the platform.
Microsoft Azure
As a public cloud, Microsoft Azure services make sure that no user has to buy any hardware or software to use it. Azure Machine Learning can be used for any kind of machine learning, from classical ML to deep learning, supervised, and unsupervised. Most languages are supported including Python or R code or zero-code/low-code options. It’s biggest plus point is its speed with a guaranteed downtime of less than 4.38 hours a year.
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Comparative study: AWS, Google Cloud and Microsoft Azure
Let’s see how well they perform on the following four parameters.
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Convenience of use and learning curve
The difference in progress between these companies can be measured by the level of investment and their failure/success in gaining knowledge. A steep learning curve makes for a slowdown in industry adoption and is directly proportional with the convenience it imparts to the user experience.
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Industry adoption
As you can see below, AWS has pretty much taken over the market share when it comes to measuring their adoption by various small and large businesses. It helps that it was one of the first ones to enter this market. The usage statistics are an indication of how easily they can be used as well as how quickly they allow users to reach the deployment stage and a proof of their consistency.
The more customers consider which of the clouds to use, the more possibility of them searching for it on Google to understand their offerings. According to Google Analytics, it’s evidently shown that popularity in terms of Google search for Amazon Web services has been consistently high. The more it is searched for, the more likely it is to be widely used.
An enterprise does have a choice to use multiple cloud providers to make the product deployment as smooth as possible. Also to avoid ‘vendor lock-in’, organisations are using different cloud providers to solve their business problems with as much flexibility as possible. The recent RightScale 2019 State of the Cloud shows that 84% of their sample size have adopted multi-cloud strategy.
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Cloud Infrastructure
Major public cloud providers offer services based on multi-tenant servers that are shared. The capacity required to compute and handle unpredictable changes is humongous and there is a need to optimise user demand across different servers. Although the popularity of serverless models is rising, there is still high density of work that needs to be processed.
According to Stack Overflow, a popular community of developers here we can gauge the share of usage of the three cloud systems through their analysis of patterns based on the percentage of questions they receive in a month.
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Pricing
Lower cost enables start-ups also to adopt cloud services. All processes for a start-up have to be built from scratch. What public cloud computing can do for them is phenomenal in the sense that the capital required for investing in the pricing can be managed until they find a long term investor. The quality of the project can remain uncompromised. For each of the scenarios below, you can observe the hourly on-demand price and then the hourly price per GB of RAM for each.
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Key Cloud Tools
To break the competition between AWS vs Google vs Azure, they have started offering these services to meet the latest trends and customer demands and are expected to continue expanding them. If you are trying to decide between Azure vs AWS certification, then consider these tools as they are important for making the most suitable decision.
AWS Key Tools
SageMaker to Serverless
Among the long list of services AWS provides in AI and machine learning, the list also features AWS SageMaker, which trains and deploys machine learning models. Additionally, AWS provides the Lex conversational interface that powers Alexa services, as well as the Lambda serverless computing service and the Greengrass IoT messaging service.
Artificial Intelligence and Machine Learning
AWS offers a variety of AI services, including DeepLens, a camera that uses AI to develop and implement machine learning algorithms for optical character recognition, image recognition, and object recognition. Furthermore, AWS has unveiled Gluon, an open-source deep learning library that non-developers and developers can use to create neural networks easily and fast without prior knowledge of AI.
Google Cloud Key Tools
IoT to Serverless
Google Cloud has a wide range of advanced technologies, including APIs for natural language, translation, and speech. It also offers serverless services and IoT, currently in beta preview.
Big On AI
As a leader in artificial intelligence development, Google Cloud allows connection to Tensor Flow, an open-source library used to build machine learning applications. Extremely popular among developers, TensorFlow is mainly used to build models by using data flow graphs.
Azure Key Tools
Supporting MSFT Software
Azure provides various tools to support Microsoft software used on-premises. One such tool is Azure Backup, which connects Windows Server Backup in Windows Server 2012 R2 and Windows Server 2016. Additionally, Visual Studio Team Services enables the hosting of Visual Studio projects on Azure.
Cognitive Services
Microsoft is deeply committed to machine learning and AI and offers a bot service and machine learning service on Azure. Additionally, they have cognitive services, such as the Computer Vision API, Bing Web Search API, Face API, Text Analytics API, and Custom Vision Service. Microsoft also provides various management and analytics services for IoT. Their serverless computing service is called Functions.
AWS vs Azure vs Google Cloud: Advantages
It is important to look at the pros when determining which cloud service among the three is most suitable for use.
Amazon Web Services
- Offers a variety of services and has more computational capacity than Google Cloud and Azure.
- It has a global reach with worldwide data centres for low-latency access and improved performance.
- Prioritizes cloud security and offers various tools and applications for
Google Cloud
- It scales resources based on demand, ensuring flexible infrastructure.
- It offers powerful tools to facilitate big data, machine learning and advanced analytics.
- It prioritizes security by offering advanced security tools.
Microsoft Azure
- It offers diverse cloud solutions like VMs, databases, AI, analytics, and more, effectively addressing various needs.
- It integrates well with Windows Server, AD, and Office 365, simplifying management and promoting collaboration.
- It has hybrid capabilities which allow seamless integration of on-premises infrastructure with the cloud, leveraging existing investments and cloud scalability.
How can clouds help?
Major cloud computing systems like AWS, GCP, Heroku, Azure and IBM cloud are providing a safe haven for all data aspirants and companies with limited funding who’d like to explore machine learning models and efficiently deploy them. These systems are cheap to operate.
By paying a few dollars an hour on average you can drive your very own machine learning application almost instantly! Public clouds also provide cheap data storage. You can leverage true databases or storage systems as the input of the data into the machine learning-enabled applications.
They all provide software developer kits (SDKs) and application program interfaces (APIs) that allow one to embed machine-learning functionality directly into applications and they support most programming languages. The real value of machine-learning technology is the use from within applications, because the types of predictions that are made are more operations and transaction focused.
However, it would be a good strategy for companies to consider both on-premise and cloud, as clouds may cost a bit in the experimentation phase. The clouds also have their own tools created on top of the open-source tools like Kubernetes, Dockers, Tf etc. Kubernetes, being a popular Google product is an open-source system for automating deployment, scaling, and management of applications, but it would run better on GCP than on other provider platforms. Above all, it will be critical to know which tools one is equipped to use in order to choose the best cloud service for oneself.
Sources:
https://docs.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-ml
https://www.kdnuggets.com/2014/11/microsoft-azure-machine-learning.html
https://sada.com/blog/google-cloud/gcp-vs-aws-why-gcp-better-option-2019/
https://www.kdnuggets.com/2020/02/deploy-machine-learning-model.html
https://www.cbronline.com/news/aws-vs-azure-vs-gcp
https://kinsta.com/blog/google-cloud-vs-aws/
https://vianalabs.com/aws-vs-azure-vs-google-cloud-which-is-best-for-me/
https://www.flexera.com/blog/cloud/aws-vs-azure-vs-google-cloud-pricing-compute-instances/
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