Devashree Madhugiri — Published On January 10, 2022 and Last Modified On March 15th, 2022
Beginner Cloud Computing Machine Learning

This article was published as a part of the Data Science Blogathon.

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Cloud computing is an important term for all Data Science and Machine Learning Enthusiasts. It is unlikely that you may not have come across it, even as a beginner. But why is it important, one might ask. A simple explanation for this is that as the dataset expands, i.e., more samples and features are added, the machine learning model becomes more complex. Such models then demand more computational power, which is why most of us have encountered the famous ran-out-of-memory error for some of our notebooks.

Then, how do we deal with this limitation? A high-end piece of hardware like the expensive machines specifically for Machine Learning and Deep Learning might sound worth the investment, though always not possible. Well, Cloud Services is the answer. An easy solution to increased computational needs for everyone. Yes, that is right! Both companies and individuals can avail these cloud services at affordable prices for their requirements. There are various options available which we would discuss in the latter part of this article.

Before we delve into the details of how to select a Cloud Service for Machine Learning, let us first understand what exactly are cloud services, especially cloud computing, and why it exists.

What exactly is Cloud Service?

Cloud services are currently a trend in the IT industry that has come to stay. These provide an option of storing data on remote servers connected to the Internet. With cloud services, one can benefit from cloud computing, which offers a host of services to the user. These services range from server access, more storage for Big Data with better back-ups, ability to run high-end analytical tools for AI & BI – all over the Internet. Additionally, these services are far more reliable, faster, affordable, more flexible, and scalable as per user needs. Users can optimize their costs through the efficient use of these services.

So, simply put, in Cloud Computing, a user does not buy new server hardware but rents it for as long as desired. It is possible to rent cloud computing even for single runs, which sometimes are only a few minutes. Further, users do not need to manage the operating systems and the allied web services; the cloud service providers manage them instead.

Types of Cloud Computing

There are various cloud services available in the market today and each of them offers an advantage that might appeal to different users. To choose the right solution, users can select from several models, types, and services. The selection is based on the type of cloud deployment required referring to the location where these services would be implemented. The models could be deployed in three different ways by using either a Public Cloud, a Private Cloud, or a Hybrid Cloud.

Public Cloud Services are cloud services shared among a wide range of people. This type of service is commonly used for public access and the data is stored on a remote server. Whereas, Private Cloud or On-premise Cloud Services are typically reserved for organizations that want more control over the security, availability, and performance of their data. Private clouds allow companies to store their data on a local server as it gives them complete control over the security of their data and accessibility. A Hybrid Cloud combines the best of both Public and Private clouds and allows data sharing for applications. This makes a hybrid cloud more flexible as compared to the other two.

Types of Cloud Services

There are three main types of cloud computing services that are important to learn.

  1. Infrastructure-as-a-Service (IaaS): Here it is possible to rent the IT infrastructure like servers, virtual machines with storage, networks, and as well as operating systems completely provided and managed by the cloud provider. Users can pay for what they use.

  2. Platforms-as-a-Service (PaaS): This service is an on-demand style of service where users can get a complete development environment required for software applications. PaaS could be used by developers to quickly create, test, and run web or mobile apps.

  3. Software-as-a-Service (SaaS): Using SaaS, it is possible to deliver software applications over the Internet, like the ‘on demand’ or ‘subscription’ services. Here, the cloud providers are responsible for hosting, managing the software applications as well as the infrastructure. They also look after all the required maintenance activities for the applications, like upgrades and security patches. Typically, users can simply log in to the application on their phones or laptops with a web browser.

Should you use Cloud Services for Machine Learning?

Yes, indeed. Cloud services are a good option for anyone looking to train and deploy memory-intensive, complex Machine Learning/Deep Learning models. Cloud services are a cost-effective solution for both, individual users as well as companies. The cloud allows employees to access files on any device. This gives them more freedom and mobility without worrying about data storage. An important fact to consider here is that these also provide a better security system for machine learning models to prevent hacking and data breaches. Thus, without much-needed expertise to set up the infrastructure for AI stack, users and businesses can use cloud computing web services for Machine Learning at a nominal fee while focusing on their relevant core objectives.

The Current Scenario and Comparison of some Leading Cloud Services Providers

The cloud services market is currently dominated by four major players -Google, Microsoft, Amazon, and IBM since they offer the required web services for machine learning. These are AWS(Amazon Web Services), Azure (Microsoft), Google Cloud, and IBM Cloud. These established platforms aim to equip all levels of users with different Machine Learning tools and Deep Learning.

AWS or Amazon Web Services (2006), offered by Amazon, is one of the most recognized cloud computing platforms for Machine Learning. This platform includes products like Amazon SageMaker, Amazon Augmented AI, Amazon Forecast, Amazon Translate, Amazon Personalize, AWS Deep Learning AMI, and Amazon Polly for various Machine Learning requirements.

Similarly, Microsoft Azure (2010), as the name suggests is a service offered by Microsoft. It is quite a popular choice for machine learning and data analytics needs. This service includes products like Microsoft Azure Cognitive Service, Microsoft Azure Azure Databricks, Microsoft Azure Bot Service, Microsoft Azure Cognitive Search, Microsoft Azure Machine Learning for creating, training, and deploying machine learning models on the Cloud.

Google Cloud or Google Cloud Platform GCP (2008) is a cloud computing platform provided by Tech Giant Google. GCP offers various products for machine learning like Google Cloud AutoML, Google Cloud AI Platform, Google Cloud Speech-to-Text, Google Cloud Vision AI, Google Cloud Text-to-Speech, Google Cloud Natural Language for all Individual and Enterprise level Machine Learning Projects.

Finally, IBM Cloud service is provided by IBM. It includes various cloud delivery models that are public, private, and hybrid models. IBM Cloud offers various products for machine learning such as IBM Watson Studio, IBM Watson Speech-to-Text, IBM Watson Text-to-Speech, IBM Watson Natural Language Understanding, IBM Watson Visual Recognition, and IBM Watson Assistant to assist all Machine Learning needs.

The following table presents a quick overview of the services offered by these service providers and might be of interest to an ML beginner.

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This in no way is complete or exhaustive information of the plethora of services these companies provide. There is immense competition amongst these top providers making this a dynamic market. Hence, the services and their benefits are likely to change over a period of time from when this article has been written. It is also likely that these vendors offer different pricing based on geographical location. This also indicates that the current services offered can only be confirmed by visiting the vendor website whenever such cloud computing services are required.

What are the Benefits of using AI And ML Cloud Services?

Machine Learning is the most sought-after technology today. With available data, a lot of business decisions could be smartly made for the best outcomes. The Research and Technology domains can receive a boost with Machine Learning and Deep Learning Models. Naturally, there is a great deal of interest between individuals and companies to try out Machine Learning.

However, earlier there was a need to invest a lot of money in Machine Learning to develop a stack for specific use cases. This was due to the fact that Machine Learning required a huge infrastructure, expert programmers familiar with ML, expensive Data Analytics tools, and a lack of data available to train the models. But with the advancement of cloud services, this has become easier. It is possible to use third-party vendor services for accessing Machine Learning algorithms and technologies while customizing them to suit the individual/ company requirements. This major benefit of Cloud Services and the ease of computing in the cloud is what makes it attractive to Machine Learning Enthusiasts.

Do I need Cloud Computing if I am an ML Beginner?

Well, it depends. If you are just starting with Machine Learning and if your machine learning model runs fairly quickly on your local machine (laptop/desktop), then you probably do not need one just yet. As you advance in this Machine Learning journey, you’ll soon start to work with larger datasets and build models that require both CPU+GPU power while taking hours to train and require cloud deployment. Then you surely need to use Cloud Computing.

How to choose a suitable Cloud Service for Cloud Computing?

By now, you as a user might have got a basic understanding of the Cloud Services through this article. Also, if you can build, train, tune, evaluate and deploy a machine learning model or at least have a fairly good understanding of how this works, then this should be easy. You first need to finalize your model training and deployment requirements. If your dataset is too complex or you wish to run multiple Deep Learning models in parallel using images, then, by all means, Cloud Computing is for you. So, next, you can choose a cloud service provider (CSP). Then, decide what are your constraints. For example, how many hours of usage typically you would require, how soon do you expect your models to train and optimize, which frameworks (TensorFlow, Keras, Theano, etc.) do you need, accessibility, pricing plans. That’s it! You can find more information on the pricing options on the individual cloud service provider website.

Training a Machine Learning Model in the Cloud

I’ll outline the steps briefly here to keep the article concise. The steps remain the same but the navigation and user interfaces would vary from service provider to service provider.

For training a model in the Cloud, you’ll need an account with the Cloud Services Provider along with the dataset you plan to use and your end goal of building and training the model. Also, you can use Cloud ML Engines using different libraries like Keras, TensorFlow, and other Python ML libraries (such as sci-kit learn) directly on the Cloud Service Provider platform to train your models. So, you’ll need to create an account with the Cloud Service Provider first. Then, you log in to your account to create a project, prepare your data, write your code in a notebook, train and evaluate your model, rerun & tune it and finally deploy your trained model to get predictions. You can also deploy different versions of your trained models and monitor them.


This article presents a comprehensive overview of what Cloud Services are, why these are essential for AI and Machine Learning requirements, and suggests an approach to selecting a service if you are a beginner. Although most of these vendors provide platforms for general-purpose AI and ML needs, a beginner still needs to choose a platform that is easy to use, requires no Cloud expertise to set up & run, offers better support and tools for Machine Learning including NLP, chatbots, or service bots as well as Neural Networks for Deep Learning.


Author Bio

Devashree has an M.Eng degree in Information Technology from Germany and a Data Science background. As an Engineer, she enjoys working with numbers and uncovering hidden insights in diverse datasets from different sectors to build beautiful visualizations to try and solve interesting real-world machine learning problems.

In her spare time, she loves to cook, read & write, discover new Python-Machine Learning libraries or participate in coding competitions.

You can follow her on LinkedIn, GitHub, Kaggle, Medium, Twitter.

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