Machine Learning Service Comparisons Among the Main Cloud Vendors

Machine learning services are used to define the various infrastructure support and services required to run operations like data processing, model training, predictive analysis, model evaluation, and more. Using these services over the cloud enables the user to save on the otherwise expensive hardware, software, and licensing costs for on-premises installation. Machine learning (ML) services have enabled everyone from individual users to small business teams to corporate organizations to easily incorporate machine learning-based solutions. 

The field of artificial intelligence is experiencing exponential growth across the globe. Companies are adopting ML to derive required knowledge from large amounts of data to predict the outcomes of various conditions. The global machine learning market is projected to grow from $15.50 billion in 2021 to $152.24 billion in 2028 at a compound annual growth rate of 38.6%, according to Fortune Business Insights. 

When used effectively, ML services can help you transform your customer experience by using virtual assistants and chatbots to answer frequently asked questions by customers, process data in real-time and recognize patterns to streamline daily business operations. ML services also help companies implement optimized business strategies, such as analyzing customer browsing habits, targeting particular customers, and improving automation.

Every industry, be it health, manufacturing, finance, or any other, stands to benefit from the right implementation of ML to modernize and add intelligence to their operations. 

All Big Three cloud vendors — AWS, MS Azure, and Google Cloud — offer a range of machine learning services. 

This article will give you an overview of the major cloud vendors’ machine learning services.  

AWS Machine Learning Services

AWS - Low-Level Services (E.G., Amazon Rekognition, Amazon Comprehend, Etc.) Vs Heavy-Duty Amazon SageMaker

AWS is the clear leader in Cloud Computing and many AWS customers often consider using the ML services that AWS provides because that’s where their data already resides. AWS has a wide footprint in almost all aspects of ML-based solutions. Below are some of the popular ML services offered by AWS. 

AWS SageMaker 

AWS SageMaker gives Data Scientists and ML practitioners all the necessary tools to build, train and deploy their own ML models and to manage the environment. SageMaker can be used for image processing, text analysis, and time series forecasting, and it can easily integrate with all of the AWS data sources such as Amazon RDS, Glue, and RedShift. 

AWS SageMaker Neo

The Neo version of the AWS SageMaker also lets you automatically scale your ML solutions. It is often used when there is little time to train your algorithms. You also get access to pre-trained ML models so that you can get started with turning the algorithms and building your models in a shorter period. 

Amazon SageMaker RL (Reinforced Learner)

This service is specifically offered to cater to building reinforced learning algorithms. It is often used to build robotic systems in which the robot learns by performing a number of actions in an environment. Each action is rewarded or not so the robot learns over multiple sessions by trial and error. Over time, the model learns how to make complex decisions. An RL environment is created to represent real-world conditions and mimic the interactions of a user or computer program. The robot is dynamic and updates itself based on the interactions and programmed behavior.  

Amazon Textract 

If your machine learning solution is catered to text analysis and large amounts of data capture from images and paper documents, Textract can help you digitize information from unstructured data files easily as it automates converting these age-old documents to accurate digital data that can be easily managed. 

Amazon Forecast 

This service allows you to work with time-series data with the help of deep neural networks. 

• Amazon Transcribe - This is an ML service that helps you automatically transcribe audio files to text files with the help of neural networks. 
• Amazon Translate - Amazon translation service can give you accurate translations of any textual information based on its language models, thus eliminating any kind of manual translation work. 
• Amazon Comprehend - This service essentially helps your systems understand the human language used in the various internet media and analyze the data so you can better understand your brand power and customer sentiments. 
• Amazon Lex helps you build intelligent chatbots that interact with your customers on a voice-based conversation. 

There are many more AWS services such as Elastic Inference, Augmented AI, Fraud Detection, Rekognition and more, each providing a specific use case that can be well utilized to make your solutions more intelligent, automated, and efficient. 

AWS also allows you to make use of frameworks like TensorFlow and PyTorch to build your own models and seamlessly integrate your existing workflow into the AWS network.  

The major difference between the heavy-duty service like AWS SageMaker and light-weight services like Amazon Comprehend is the scope of the solutions supported. Services such as Comprehend, Transcribe, and Forecast have a narrow set of features catered explicitly around a particular functionality. These services open up the world of Machine Learning to a larger set of developers. In contrast, SageMaker can create more custom and powerful ML models, but it requires a lot more data science expertise. 

Microsoft Azure Machine Learning Services

MS Azure low-level services (Azure Cognitive Services) vs. heavy-duty Azure Machine Learning

Similar to the AWS services, Azure cloud services provide a set of generic services under the Azure Cognitive Services umbrella and a more powerful Azure Machine Learning service that can be used to build, train, deploy and manage your own ML models. 

The services that come under the Azure Cognitive Services are listed below:

• Decision - Used to build apps based on making decisions based on context. 
• Language - Used to analyze text, human language and figure out the underlying sentiment
• Search - Used to embed the Bing Search API to your apps and web pages so that you can easily integrate the powerful search engine capabilities to your apps. 
• Speech - Used for automatic speech to text conversion and vice versa. Also includes features to translate text from one language to another automatically. 
• Vision - Allows you to process images, videos, and digital ink content so that you can identify, caption, and moderate these digital media content. 

As you can see, these cognitive services have a narrow scope of usage and can be effectively applied to specific use cases. They provide pre-trained models that can readily be used for the specific functionality they are intended for. These are easy to employ and require little technical knowledge, and thus can be used by anyone without extensive knowledge of data science. 

Google Cloud Machine Learning Services

Google has two service offerings that provide ML infra support:

• Vertex AI 
• AutoML 

Vertex AI is a complete ML service that allows you to develop custom models in the most efficient way possible and deploy and scale them with fewer steps than you would have if you had to manage the infrastructure yourself. One major advantage of using Vertex AI from Google is that it requires little coding knowledge compared to other custom Model building services. 

AutoML can be used alongside Vertex AL to further automate the model-building tasks and train the models with ease. Google also provides AI infrastructure support to develop deep learning projects at a lowered cost. 

While Vertex AI and AutoML can be used to build custom models, Google also provides many pre-trained models for generic use under services like Conversational AI and Document. 

Conversational AI provides you with the following features:

• Speech-to-text conversion 
• Text-to-speech conversion
• Natural Language Processing to help analyze human language and identify the sentiment and meaning
• Virtual Agents to implement intelligent chatbots
• Agent Assist to help human agents get access to the relevant information quickly and accurately
• Dialogflow to create a natural conversational experience across various kinds of apps and platforms
• Contact Center AI (CCAI) and CCASI Insights to improve call center efficiency by providing better insights to customer-facing agents

Document AI helps you make faster decisions using data from your documents and provides the following services:

• Computer vision (including Optical Character Recognition) to scan documents for text
• Natural language processing to get better insights from unstructured text
• Translation of the text from one language to another. This service can also automatically detect a language from an unstructured text
• Document API platform is an end-to-end document solution that allows you to perform custom document processing workflows and get better insights from documents. 
• Invoice parser to aid in automated data collection from invoices 
• Form parser to extract meaningful information from documents. 

AI for Industries provides the following services:

• Media translation for real-time transcript generation from audio info
• Healthcare Natural language to help parse and derive insights from medical documents
• Recommendations AI to get personalized suggestions and recommendations based on the context, customer data, and more
• Lending DocAi caters explicitly to the loan process where it can be used to automate the mortgage document processing
• Procurement DocAI helps improve the procurement process by automating data capture from unstructured documents like forms, invoices, bills, and receipts.

Cloud GPUs and TPU

Google Cloud provides a number of basic building blocks to allow heavy-duty data scientists to provision the specialized infrastructure they need to build and train complicated models. Graphical Processing Units (GPUs) have long been considered the pinnacle of performance for Ml workloads. Google Cloud has a variety of such offerings, as do the other cloud vendors. Google Cloud also provides the Cloud Tensor Processing Unit (TPU), which is the custom-designed machine learning ASIC that powers Google products like Translate, Photos, Search, Assistant, and Gmail. Cloud TPU is designed to run cutting-edge machine learning models with AI services on Google Cloud. There are significant benefits to using Cloud TPU when building and training TensorFlow models. TensorFlow is a very popular open-source library for machine learning that was developed by Google. Since Google developed this popular library, many data scientists turn to Google Cloud as the best place to run such workloads.

Building ML from Scratch

IT engineers who have an extensive knowledge of data science and wish to build ML models from scratch can use frameworks such as Tensorflow, Pytorch, and scikit-learn on any of the public cloud platforms. Some organizations choose to forgo the ML platform services that the cloud vendors offer and run everything on the most basic virtual machines available. This approach may make their infrastructure a little easier to port between vendors, but it means that their ML practitioners are spending a lot more time on basic infrastructure provisioning. These days, most organization tend to make use of the ML services from their primary cloud vendor. But there are some cases where specific ML workloads might be better suited in a second cloud vendor. It is not uncommon to see an organization that primarily runs workloads in AWS, but exports data to Google Cloud to make use of specific ML services.

ExitCertified provides dozens of courses in the basics of ML, as well as authorized courses in Machine Learning from AWS, Google Cloud and Microsoft Azure.