Top 4 Cloud-Based Workflows for Machine Learning

Among the top skills requested for technology jobs, you’ll find two that are overlapping more than ever: machine learning and cloud solutions. As computing, programming, and technology rapidly evolve and new applications are discovered every day, computer science professionals who understand cloud machine learning are increasingly valuable to the marketplace.

But which cloud machine learning workflows should computer scientists use? What do some platforms offer that others do not? This blog will explore four cloud-based workflows to help you answer those questions and more.

What Is Machine Learning?

In order to choose the best cloud-based workflow for machine learning, it is important to have an understanding of what machine learning is and how it works. Machine learning is a branch of artificial intelligence and computer science. As a method of data analysis, machine learning emphasizes data and algorithms that mimic human learning without reliance on human involvement.

Here are a few examples of machine learning:

• Image recognition: One of the most ubiquitous usages of machine learning in the everyday world, this machine learning technology can identify cancer in x-rays, correctly tag faces in photos on social media, recognize handwriting, and perform facial recognition for law enforcement purposes.

• Stock market trading: Futures trading companies use machine learning to look for the types of trading practices that could pique the interest of regulators.

• Fraud detection: Machine learning algorithms can study patterns of fraud that have already occurred and notice when those patterns recur.

These represent a fraction of machine learning usage in the marketplace and people’s everyday lives. Consider the following cloud-based workflows that will be part of the ongoing success and discovery of machine learning.

Alibaba Cloud’s Machine Learning Platform for AI

Alibaba Cloud’s Machine Learning Platform for AI is an end-to-end platform that mines and analyzes data through machine learning algorithms. This platform features a visualized interface with drag-and-drop features, over one hundred algorithm components, and an impressive computing capability that can handle a high number of concurrent tasks.

Features

Machine Learning Platform for AI offers several services, including:

• PAI-Studio: A visual modeling experiment platform praised for its drag-and-drop features, abandoned algorithm components, and AutoML methods that reduce the need for, and time spent on, manual adjustments

• PAI-DSW (Data Science Workshop): A cloud-based deep learning Integrated Development Environment (IDE) that works with an open-source interactive program environment to support developers at every level

• PAI-EAS (Elastic Algorithm Service): A model online service that empowers developers to set up experiments, deploy models online in one click, and apply their models to business in the real world

Case Study

Alibaba collaborated with technology company Intel to explore the capabilities of the 3rd Gen Intel® Xeon® Scalable Processors, particularly as those capabilities relate to AI Applications. Through Alibaba Cloud’s Machine Learning Platform for AI, the technologists were able to improve processor performance by 1.35x for the FP32 and 1.42x for the INT8 when compared to Intel’s previous processors.

Conclusion

Alibaba Cloud is the third largest cloud provider in the world, coming in beneath only Amazon Web Services and Microsoft Azure, both of which we will review later in this post. Experts say that Alibaba Cloud’s cost efficiencies, range of offerings, and impressive computational storage may make it worth a second look for those looking for a cloud machine learning solution.

Amazon SageMaker

Amazon SageMaker is one of Amazon Web Services’ 200 products and services that range from developer tools to RobOps. Described as “machine learning for every data scientist and developer,” Amazon SageMaker is known as the easiest cloud machine learning workflow to use.

Features

Here are some of the features that Amazon SageMaker offers to facilitate the management of machine learning:

• Ground Truth: A data labeling service that simplifies the process of labeling various types of data, including video, images, and text, so that they can be immediately processed into analyzable data

• Data Wrangler: A tool and data pipeline that drastically cuts down on time spent aggregating and preparing data

• Training Compiler: A feature that speeds up deep learning model training by up to 50% through a more strategic approach to GPU instances

Case Study

ReliaQuest, the technology company that built GreyMatter, a cloud-native Open XDR platform integrated with cloud security, used Amazon SageMaker to enhance their AI capabilities and launch new features more quickly. Through Amazon SageMaker, Amazon Elastic Container Registry, and AWS Step Functions, the timeframe for ReliaQuest to deploy and test GreyMatter AI capabilities shrank from a year-and-a-half to two weeks, increasing the innovation of GreyMatter AI by 35x.

Conclusion

As the largest cloud provider in the world, Amazon Web Services is used by some of the world’s largest companies like Pfizer, the Walt Disney Company, and General Electric. Companies and individuals who use Amazon Web Services like SageMaker cite user-friendliness, flexibility, security, scalability, and elasticity among their endorsements.

Google Cloud’s Vertex AI

Google Cloud’s Vertex AI is a unified machine learning program that facilitates building, deploying, and scaling AI models. Vertex AI supports efficient data preparation, the capacity to scale data, opportunities for training and experimentation, and model deployment. Built on AutoML, the Google Cloud AI & Machine Learning Products feature pay-as-you-go pricing so that users only pay for what they need.

Features

Some of the Vertex AI machine learning features are:

• Pipelines: Portable, scalable, and container-based machine learning workflows that empower users to automate, monitor, and experiment with interdependent components of a machine learning workflow

• Feature Store: A fully managed, centralized repository that organizes and stores machine learning features, allowing organizations to share and discover features easily

• Workbench: A notebook-based environment that enables users to access and explore data, automate recurring model updates, process data efficiently, and run a notebook as a step in a Vertex AI Pipeline

Case Study

Satair provides services and solutions to the civil aerospace industry worldwide. Interested in better customer service, Satair partnered with leading digital solutions provider Atos to produce an automated solution through Vertex AI. The result was Lilly, an online assistant that has automated 58% of the quotes the Satair customer services team would otherwise have to send themselves. Lilly’s quotes are exactly right 98% of the time, and since Lilly is available 24/7, company lead time has greatly improved.

Conclusion

Vertex AI is considered efficient, affordable, and easy-to-use. In addition to Satair, other major corporations are utilizing Vertex AI, including personal care brand L’Oréal , which used Vertex AI to train the artificial intelligence models of its augmented reality AI platform called ModiFace. Those who are familiar with Google’s services and find themselves frequently using Gmail, Google Calendar, or Google Docs may also appreciate the familiar style and feel of Google Cloud and Vertex AI.

IBM Watson Machine Learning

As part of the IBM Cloud Platform, IBM Watson Machine Learning is a full-service cloud offering. The service is a set of REST APIs that will respond to any programming language. Users can choose from a variety of supported machine learning frameworks: TensorFlow, Keras, Caffe, PyTorch, Spark MLlib, scikit learn, xgboost and SPSS.

Features

Features of the IBM® Watson® Machine Learning service include:

• Smart Document Understanding: A tool that uses visual machine learning to label text once a user has annotated just a few pages themselves

• Watson Discovery: A search service that provides users with specific passages containing relevant information, as well as source documents, rather than just returning links as search results

• IBM Watson® Assistant: A chatbot integration mechanism that provides Watson Discovery answers to virtual agents who can then share relevant passages of text with customers

Case Study

Geisinger Health System collaborated with IBM to mine electronic health record data to discover insights on diagnosing and treating sepsis, which contributes to 250,000 deaths in the United States every year. IBM® Watson® analyzed thousands of patient records and medical journals. The result? A thorough approach to identifying factors associated with sepsis risk and mortality, more personalized care plans, and better-informed researchers.

Conclusion

Other companies that work with IBM Watson include Highmark Health, FleetPride, and JP Morgan Chase. IBM continues to release new capabilities for Watson, such as a Data Privacy Management module in IBM OpenPages with Watson and improved explainability for planning forecasts through IBM Planning Analytics with Watson. Users cite great technical support, scalability, and ease of use as advantages to the IBM Watson Machine Learning platform.

Microsoft Azure

The Microsoft Azure Machine Learning cloud service empowers machine learning and computer science professionals, data scientists, and engineers to train and deploy models and manage machine learning operations (MLOps) in their day-to-day workflows. The service allows users to create models or use models built from open-source platforms. Azure Machine Learning uses familiar interfaces like Python SDK, Azure Resource Manager REST APIs, and CLI v2.

Features

Azure Machine Learning features include:

• Studio UI: The Azure Machine Learning studio, which includes a drag-and-drop interface, pipeline visualizations, and notebooks

• Azure Storage: A range of services, including a data lake store, that users can access from the UI or through Python code

• Azure ML Designer: An interactive graphical user interface (GUI) that enables users to specify and create machine learning models

Case Study

PepsiCo wanted to empower its frontline sales force to more effectively and efficiently handle store inventories and displays. Through Azure Machine Learning’s MLOps capabilities, PepsiCo developed Store DNA, which provides workers with a tailored list of priorities for store visits. PepsiCo estimates that Store DNA has shifted 4,300 days of work per year from “tedious tasks” to “value-added activities.”

Conclusion

Azure is the second largest cloud-platform in the world. More than 95% of Fortune 500 companies use the Azure platform, including Chevron, J.B. Hunt, and AllScripts. Those who already use Microsoft products and services may especially like Azure. Users cite competitive pricing, comprehensive algorithm support, and seamless web service deployment as advantages of Microsoft Azure.

Gain Machine Learning Skills to Advance Your Career

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This 30-credit hour, 100% online program features ten graduate courses including built-in bridge courses and four specialization tracks: Computer Systems, Cybersecurity, AI & Machine Learning, and Big Data Analytics.

Develop the knowledge you need to design and deploy real-world marketplace solutions. Learn more about the Master of Computer Science Online program at WPI.