Combining AI/ML and Data Systems in the Cloud Architecture- Chirin Dirani

When it comes to cloud computing, most of the readings for this week refer to the fact that there is an uncertainty in the definition of this term. This uncertainty is intentional as Professor Irvine mentioned in his presentation Introduction: Topics and Key Concept of the Course. Cloud is based on an old engineering metaphor and means “a blackbox of connections in a network.” Our readings for this week indicate that the National Institute of Standards and Technology (NIST) defines cloud computing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction.” When cloud computing is combined with AI/ML and data systems, the outcome of the three computing trends will not only be a gigantic network that is able to learn, improve and store enormous amounts of data but also a cost effective and environmentally friendly one. If we think of the way these three trends work in combination, it seems to be a complex mechanism. In this assignment, I will try to deblackbox how AI/ML and Data systems are implemented in the Cloud architecture by revealing the key design principles and main architecture of cloud computing system and list some points of its convergence with AI/ML and data systems. 

In his book, Cloud Computing, Ruparelia said that the cloud computing model “promotes availability and is composed of the following:

  1. Five essential characteristics (ubiquitous access, on demand availability, pooling of resources, rapid elasticity and measured service usage).
  2. Three service models (Infrastructure as a service; IaaS, platform as a service; PaaS and software as a service; SaaS) 
  3. Four deployment models (public cloud, private cloud, community cloud and hybrid cloud).

This structure of characteristics, deployments and services is what makes Cloud computing a beneficial network, as it provides agility, elasticity, cost saving and fast global deployment. Cloud computing relies on two basic virtualization technologies; server virtualization and application virtualization. This virtualization enables everything we can do in computing to be virtual and scalable. 

At first glance, AI/ML, data systems and cloud computing look like working separately but in fact, they are proactively linked to each other. While AI/ML and data systems work together in an inseparable way, the vast amount of rich output data needs the scalability and extensibility offered by cloud computing, in the shape of cheap extensible storage memories. On the other hand, blending AI/ML solutions as a service with cloud computing, improves the already existing cloud solutions and takes it to another level of efficiency. This unique combination of the three computing trends encourages organizations of every type, size, and industry to shift to cloud computing for a wide variety of use cases. This is due to fact that this combination “offers huge advantages in terms of installation, configuration, updating, compatibility, costs and computational power.” The best example, I can think of, to demonstrate the convergence between AI/ML, data systems and cloud computing is Amazon Web Services (AWS). This tool is currently the leading platform in the world (according to AWS website). AWS’s ML service provides the broadest and deepest set of machine learning services in one cloud platform. AWS enables data scientists and developers to “create faster solutions and add intelligence to applications without needing ML expertise,” as the platform facilitates using pre-trained data through AI services to many applications such as creating more intelligent contact centers, improving demand forecasting, detecting fraud, personalizing consumer experience and more.” The following diagram illustrates how AWS’s machine learning is used to build, train, and deploy models faster with less effort and at a lower cost.

With the increasing number of cloud computing platforms users in the last few years, a number of risks surfaced. These risks hold some users back from adopting Cloud computing service. The risks include but are not limited to ambiguity of what cloud is, concerns over maturity to meet organization’s needs, security issues caused by lack of direct control over systems and data, and corporate policies permit moving to the cloud, and flexibility in choosing a suitable provider. Given the fact that there are“big four” cloud computing providers, the question, raised by Professor Irvine here, is what are the consequences of merging these big four in one provider? An answer to such an important question requires a separate study. However, I can say that there will be a maximization in both advantages and disadvantages of Cloud computing. The four bodies will grow into one incredible network that is able to gain access to massive economies of scale. On the other hand, this gigantic network will be monopolized by one provider that will control the access of millions if not billions of global users to services by one provider.


Amazon Web Services (AWS) [main site]: browse services and Machine Learning products.

AWS Machine Learning.

Boudewijn de Bruin and Luciano Floridi, “The Ethics of Cloud Computing,” Science and Engineering Ethics 23, no. 1 (February 1, 2017).

Derrick Roundtree and Ileana Castrillo. The Basics of Cloud Computing: Understanding the Fundamentals of Cloud Computing in Theory and Practice.

Nayan B. Ruparelia, Cloud Computing (Cambridge, MA: MIT Press, 2016).

Professor Martin Irvine Irvine, Introduction: Topics and Key Concepts of the Course.