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Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, IBM Watson, Power BI

Creating an artificial intelligence service in-house can cost your business quite a lot of money. It is also a fact that not all businesses can afford this type of expenditure. The solution to this problem is to totally outsource your artificial intelligence requirements and let someone else take the pain for you albeit for certain payments. Instead of spending thousands of dollars on an in-house product, it is cheaper and better, both for large organizations and small businesses, to use cloud computing services.

Which Cloud Computing Platform to Use?

Now the million-dollar question is which cloud computing platform you should use. There is no definite answer to this question, but the choice of a vendor can make or break your artificial intelligence endeavor. More importantly, you should not go for the first platform provider you see on the internet.

As a matter of fact, there are quite a large number of artificial intelligence solutions to choose from. However, it is always beneficial to start with a big provider, such as IBM Cloud or Azure Machine Learning. Such vendors are extremely reliable when it comes to API accessibility, uptimes, and customer support. In addition, these solutions offer the added advantage of having massive amounts of data you can use to train your algorithms.

In the following lines, we compare some of the best cloud-based platforms for machine learning.

Microsoft Azure

Microsoft Azure Machine Learning tool can tackle a myriad of tasks by providing visual tools for developers as well as data and non-data scientists. The browser-based, drag and drop environment is very easy to understand and simple to use. Microsoft Azure also offers the highest degree of openness, extensibility, and flexibility.

Azure also breaks down its artificial intelligence services into three categories, the AI Infrastructure, AI Tools and Frameworks, and AI Services.

Artificial Intelligence Services further has three subcategories that include Azure Cognitive Services, Azure Cognitive Search, and Conversational AI.

Azure Cognitive Services makes chat bots, Web apps, and other customer-facing services more intelligent.
Cognitive Search combines the Cognitive Services with the Azure Search.
Conversational AI offers various AI solutions, such as custom AI development and Azure Bot Service.

It is worth mentioning that Microsoft has totally overhauled its Bot framework. As a result, developers now find it easy to create the next generation of conversational bots with voice customization, full personality, and richer dialogs.

Some of the other Frameworks and tools Azure incorporates are as under.

Visual Studio Tools for AI
Data Science VMS
Azure Notebooks
AI Toolkit for Azure IoT Edge

Similarly, developers can also customize and modify applications in the Edge browser thanks to Azure Internet of Things Edge Runtime. Developers will develop all the Azure’s new AI-based applications in Azure Internet of Things Edge Runtime from now on as well.

Advantages

Azure ML works on the principle of detect, assess, diagnose, stabilize, and close (DADSC) security system. It is easier for you to obtain their multiple compliance certificate by pairing the built-in cybersecurity controls with this profile. The Azure covers both the end-user and the platform, reducing the risk of data loss.
Azure offers the highest level of scalability. It means you can upgrade your subscription when your data usage needs are high and remove them when you use a minimal amount of data.
Azure guarantees 99.5% of uptime. Some of its major competitors guarantee the uptime of as low as 90% to as high as 98%. Similarly, you will experience the downtown of only 4.5 hours on average during the entire year.
Azure ML is also quite cost-effective. You will only purchase features and upgrades which you need and only when you need them.
Azure Cloud also enables you to use any language be it is .Net, Node.js or Java to develop applications.

Disadvantages

It is slightly difficult to manage a cloud-based data center with Azure Cloud as it offers no help in this regard. You need to have someone on the ground to effectively manage your data.
Changing the hardware or computing power is also a problem with Azure. For example, your hardware will not be compatible with the cloud because of its higher computing power. You may end up spending thousands of dollars to have similar computing power.
Microsoft Azure employs a single vendor strategy, which increases risk despite increasing convenience. You may lose your entire data if Microsoft refuses to hold her end of the bargain due to any reason.

IBM Cloud

IBM Cloud is a new cloud computing service provided by IBM. It is actually a mixture of different IBM services such as Watson AI Group, Softlayer data centers, and BlueMix Cloud Service. In total, you can avail more than 170 services if you sign up for IBM Cloud. In this regard, the Watson AI Group offers 16 different services. It focuses heavily on analytics, including text, speech, and data. Similarly, no other cloud computing service can remotely match IBMs Global Services consulting business.

The Watson Studio enables you to prepare and perform analysis on data. You can also use IBM Watson to build and train artificial intelligence models. It also emphasizes heavily on the data discovery and processing because the searchable data in the world only amounts to 20%. For example, IBM Watson Services for Core ML runs either in the cloud, offline or on-premises. The service allows businesses to develop artificial intelligence-based apps that connect securely to their data. These apps adapt and improve user interaction by utilizing machine learning. More importantly, all of this is available in one integrated environment.

Data Refinery and Deep Learning are other data discovery apps included in IBM Cloud. The earlier is a self-service data preparation app for business analysts and data science engineers. The latter app enables developers to use neural networks, with hundreds of training runs, to design and deploy deep learning models.

For developing virtual assistants, chat bots and other AI platforms, you will have access to Watson Assistant. On the other hand, the Watson IoT platform is a cloud hosting service for data storage and rapid visualization. However, you can also use it for device registration, connectivity, and control.

Advantages

IBM Cloud ensures flexibility and interoperability as it is built on open technologies.
It enables hybrid integration which means you can use integrated hybrid controls to unlock existing data.
IBM cloud has advanced, reliable, powerful, and extremely fast analytics features, providing you with deeper insights into your data.
IMB cloud offers a variety of cognitive solutions and helps you learn and make better decisions.
It fosters enterprise innovation through DevOps. Now you can develop AI-powered applications quickly, experiment with them and iterate faster.
It is possible to access stored data from different locations and platforms.

Disadvantages

As Watson is only available in English, language limitation is a major problem.
Watson takes a lot of time to collect a sufficient amount of data for processing.
IBM Watson is also slow to integrate with your current data analytics systems.
It can store a tremendous amount of data which increases the need for upgrades and maintenance.

Power BI

Power BI is yet another brilliant machine learning platform by Microsoft. It allows analysts and businessmen to create and use machine learning apps even if they have minimal coding or development skills. More importantly, Power BI also provides easy access to machine learning models, such as Azure Cognitive Service, Azure ML, and Azure DataBricks available with Microsoft Azure Cloud.

The Power BI is a much simpler and broadly accessible cloud computing tool compared to Azure. It is now possible for the same scientist to integrate machine learning models built by Microsoft or their own data scientists and perform self-service analysis simultaneously using the Power BI. In addition, they can also create their own ML models.

Microsoft introduced Power BI with four highly advanced machine learning solutions that are as under.

It is possible to integrate Azure Cognitive Services.
You can also integrate machine learning models hosted by Azure ML, such as those hosted in DataBricks.
You can also use Azure Automated ML to build and utilize machine learning models.
A new Key Driver Analysis visualization tool to measure Key Performance Indicators.

The best thing about Power BI is the ability to seamlessly integrate with your current business environment. Moreover, you can also adopt or import your reporting and analytics capabilities. Power BI also allows you to leverage Microsoft Azure Consultants to easily add interactive visuals to your apps.

The icing on the cake is rich personalized information dashboards. You can even customize these dashboards to meet your own requirements. Furthermore, creating a unified user experience incorporating BI reports and dashboards in the applications is also a breeze.

Advantages

All the users can have access to the latest information as you publish reports securely and set up automatic data refresh.
There are no speed and memory constraints even if you transfer your existing BI system to the Power BI cloud computing environment. It also ensures you can quickly retrieve and analyze data.
There is a minimum need for technical support as the Power BI comes with its own agile analysis and inquiry.
You can transform your company’s data into rich visuals, resulting in enhanced decision making.
It also offers an exquisite balance between performance and simplicity thanks to DAX scripting language and in-memory data analysis.

Disadvantages

The user interface is somewhat bulky. The formula help window and sidebar in particular cover parts or components of the dashboard.
Another shortcoming includes rigid formulas. For example, the concatenate statement only concatenates two elements. You require to nest concatenating statements if you want to combine more than two statements.
The data limit in the free version is just 2GB which is insufficient for larger organizations. You will have to purchase upgrades if you reach this data limit.

Amazon Web Services

Introduced in 2004, Amazon Web Services is still the leading cloud computing platform in the world. It provides highly reliable, scalable, and cost-effective AI or machine learning solutions to the businesses. Amazon Web Services also cover almost all major regions in the world, effectively protecting the system against glitches and outrages.

The most notable services included in Amazon Web Services are as under.

Amazon Simple Storage Services for scalable cloud storage.
Amazon EC2 for elastic virtual services.
Amazon Aurora which is a high-performance database.
Amazon VPC for isolated cloud storage.
Amazon SageMaker for building, training, and deploying machine learning models.

Among all these services, Amazon SageMaker is the most popular. Data scientists, non-data scientists, and analysts use Amazon SM to build and deploy machine learning models for easier production at mass scale level.

Amazon SM offers multiple AI solutions for all the stages of data collection, storage, and analysis. It also lets you clean and prepares data for future use. It is useful for training and tuning machine learning models as well as deploying them to the cloud. You will accomplish all these tasks using Jupyter Notebook. That is why experienced scientists who are already familiar with Jupyter Notebook love using Amazon SM. The tool also gives you a lot of flexibility for novices who don’t know much about coding.

Amazon SageMaker is, in fact, a self-service machine learning platform for businesses of all sizes and budgets. It also gets you covered if you want to use third-party libraries such as Keras to create and tune ML apps. It also supports popular frameworks, including Tensor Flow, mxnet, Apache Spark, and Pytorch, etc.

Advantages

Amazon Web Services or Amazon SageMaker is extremely easy to use. Both the experienced and novice scientists can utilize it without any real problem, thanks to well-documented web services and Amazon Management Console.
Amazon SageMaker provides increased capacity and memory at the minimum cost compared to other popular machine learning solutions. You can upgrade capacity when you need it and downgrade when you no longer need so much storage.
Amazon Web Services provides you with your cloud computing server within a few minutes, unlike other companies that usually take at least a week. It helps you quickly store your data and build ML models.
Tools like Auto Scaling, Elastic Load Balancing, and AWS Tools reduce the time you spend on creating and training machine learning models and other tasks.
Amazon SageMaker is an extremely safe and reliable cloud-based artificial intelligence service.

Disadvantages

There are some security limitations such as the EC2-VPC and EC-2 classic that can only have up to 100 security groups per VPC which are way less when you compare it with other AI services.
Amazon Web Services including Amazon SM charges you for immediate technical support.
There are general cloud computing problems in the likes of downtime, backup protection, and limited control.

Google Cloud AI

Google has also made its grand entry by introducing its own Cloud Artificial Intelligence service. Launched at Cloud Next 2019, Google Cloud AI is actually the fusion of many of the Google’s other projects in the space of artificial intelligence and machine learning. Some of these ventures include TensorFlow, Cloud ML Engine, and Kuberflow.

Google has actually brought all of its AI services under one roof by launching the Cloud AI Platform. This particular platform incorporates a complete range of machine learning services such as data preparation, tuning, and training. The Cloud AI further helps data scientists to deploy and share machine learning models and collaborate with each other about how to improve them.

The Cloud Al consists of quite a few components in the likes of AI Hub, Deep Learning VMs, Kuberflow Pipelines, AI Frameworks, and AI Notebooks.

The Artificial Intelligence (AI) is the place where data scientists can discover already existing and reusable models. Similarly, they can also share and deploy these models to one of the AI Platform’s executions environments. It is actually a sort of catalog which contains a large number of popular models created in PyTorch, TensorFlow, Keras, and XGBoost, etc. You can deploy each of these models to deep learning VMs, Kuberflow, Google’s AI APIs and even Jupyter Notebook.

On the other hand, it is has become super convenient to instantiate a VM image through Cloud AI Deep Learning VM. It contains VM images from some of the most popular ML and DL platforms on Google Compute Engine instance. These images are available with pre-installed third-party drivers and dependencies including but not limited to Jupyter Lab, Project Jupyter’s latest web-based interface, and latest TPU and GPU frameworks.

The third component of Cloud AI is Kuberflow pipelines. It makes it easy, scalable and portable to deploy machine learning workflows on Kubernetes. As a result, it also becomes easier to deploy the best open-source machine learning systems to diverse infrastructure. You can also build and deploy scalable and portable MI models using Kuberflow Pipelines. Kuberflow is also extremely portable as it runs on Kubernetes.

Data Scientists can also create and manage Virtual Machine (VM) instances pre-installed with Jupyter Lab using AI Platform Notebooks. The Notebooks also make creation and development of VMs more iterative as Google has synchronized them with GitHub. Google has also put all the important packages required to work in PyTorch and TensorFlow environments in the Notebooks.

The Cloud AI Frameworks actually further augment Google Cloud ML engine introduced a couple of years ago. These frameworks offer a myriad of new features related to custom container-based training and built-in algorithms. More importantly, they also supports PyTorch, one of the most popular frameworks to develop ML models.

Advantages

Cloud AI Notebooks make it easy to work with GPU-enabled instances as they come pre-installed with the latest Nvidia drivers.
The Cloud AI has a futuristic, sleek and fast user interface.
There is no chance of data breach as Cloud AI offers Google-grade security.
You only pay for compute minutes you use instead of paying for unused minutes as well.
It offers fast, scalable and consistent performance with the help of Google cutting-edge caching service and advanced global software-defined networking.

Disadvantages

Cloud AI has poor UI support.
The product is still in its development phase, to say the least. Therefore, it looks more experimental than a finished product.
Global outages are still pretty common. But, Google will definitely find a solution to this problem sooner rather than later.

There you have it. These are the top five cloud computing artificial intelligence services in the world. Which of these services you should go for actually depends on what kind of services you are looking for. For example, you must choose Amazon Web Services if your main concern is API connectivity and performance. On the other hand, IBM Watson is a good option if you want control over data flow and user-friendly interface.

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