A Comparison of the Top Four Machine Learning Platforms
Welcome to the Age of Analytics — a time where data drives decision-making and inferences are made by interpreting mounds of data no human can sift through. Enter the Machines. Machine Learning has grown in relevance over the past few years with its ability to sieve through and analyze large sets of data to give predictions or find useful patterns. As many companies join the Machine Learning bandwagon to increase sales and reduce churn, find out if your business can benefit from the same.
Is Machine Learning for you?
Not every business needs the powerful computing abilities of machine learning. Machine Learning gains relevance only when there are business critical decisions – which are currently being made on assumptions – to be made on the basis of data analysis.
The four main vendors for cloud based Machine Learning are Amazon, Google, Microsoft and IBM. Each have their own strengths and weaknesses and can enhance the quality of your decision making.
Democratizing Machine Learning – Amazon
Amazon Machine Learning platform offers ready-made and easily accessible prediction models for any developer, even if they do not have a data science background. Fueled by technology that powers its internal algorithms, these models can generate millions of predictions either in batches or in real-time. In addition to this, in the 2016 re:Invent developer conference, it announced additional offerings in Image Recognition, Text-to-Speech Service and Speech Recognition, bringing its offerings at par with its competitors. A pay-as-you-go model, requiring little investment in hardware or software, has made Amazon one of the best ML platform providers a newbie can sign up for.
- It uses the Amazon Machine Learning Console and Amazon Command Line Interface.
- Data needs to be stored in an AWS account such as S3, Redshift and RDS.
- It works on a pay-as-you-go model, and for a thousand batch predictions it costs as little as 10 cents.
- It has pre-built algorithms trained to perform regression analysis and classification (binary and multiclass).
As Joe Emison of BuildFax says, Amazon Machine Learning “democratizes the process of building predictive models. It’s easy and fast to use, and has machine-learning best practices encapsulated in the product, which lets us deliver results significantly faster than in the past.”
Open Sourcing Machine Learning – Google Cloud
Google prides itself on being an AI-first company. Almost of all of Google’s marquee products use advanced machine learning models and AI capabilities, including speech recognition, image recognition and natural language processing. This makes Google Cloud Machine Learning platform a powerful tool for the beginner as well as the expert. It sports a mix of pre-trained models, besides allowing users to build their own models. It supports video analysis, image recognition, text analysis and translation services.
- It uses the Google Cloud ML Engine Interface.
- Tensor Flow is the machine learning library of Google, an open source platform that lets more serious developers create their own models.
- For faster deployment of simpler models, Google offers a prediction API through the REST API interface.
- A Google Cloud account is required to store the data.
- It also uses a pay-as-you-go model and charges about 10 cents for a thousand batch predictions.
“In addition to scale, speed, and stability, Google will also give Evernote access to some of the same deep-learning technologies that power services like translation, photo management, and voice search,” said Ben McCormack, VP Operations of Evernote, speaking about the benefits of Google Cloud ML Platform.
A Comprehensive Package – Microsoft Azure
Offering a plethora of options to choose from, Microsoft Azure, along with the Cortana Intelligence Suite, is the most comprehensive of Machine Learning Packages suitable for both novices and experienced data scientists. The learning curve is steep, requiring the user to manually clean, compile, process and validate the data. While it can take some time to get used to, mastering Azure would result in gaining a comprehensive understanding of most major techniques in the field of Machine Learning.
- It uses the Azure Machine Learning Studio as its interface, letting you build models in a drag-and-drop environment.
- It provides automated algorithms to run decision trees, deep neural networks, classification and regression.
- While large data sets (of over 2 GB) must be housed in the Azure Cloud, it does allow smaller data sets to be uploaded from other service providers.
- While there is a free version with limited features for personal use, the standard version comes at $9.99 per user and there is a $1 fee per hour of experimentation .
“The Microsoft Azure platform makes it a lot easier for us to deliver on our vision without getting stuck on the individual IT components. We can focus on our end solution and delivering real value to customers rather than on managing the infrastructure,” says Richard Beesley of Rolls Royce.
The Celebrity ML – IBM Watson Machine Learning
Named after the company’s founder, Thomas J Watson, the IBM Watson achieved fame and limelight with its 2011 Jeopardy win against two of its greatest champions. From there, the process had begun to turn it into the machine learning behemoth it is today. Watson allows a user to search for algorithms and queries, use a prediction tool to give predictions, and assemble tool to create workbooks. It enables powerful data visualizations and allows easy creation of models with its drag and drop interface.
- It uses the SPSS Graphical Analytics Software as a front-end interface.
- The data must be housed and predictions run in IBM Bluemix.
- Focused on its enterprise clients, the service enables creating ML based applications through API connectors.
- There are paid as well as free versions available.
IBM views AI and machine learning as ‘augmented intelligence’ to enhance quality decision-making. IBM’s APIs are being put to use in areas such as retail or finance, but their core area of focus is in medicine. While it has deep learning capabilities with data visualizations, it is primarily meant for large organizations.
So there you have it. The big four of Machine Learning – each have their own markets to which they cater. If you would like to know more about machine learning and its applications to your business, write to email@example.com.
About the Author
S. Karthikeyan, or SK as he is better known, has 19 years of experience in designing, leading and delivering world-class software solutions. His specialties include Product Ideation, Innovation & Strategy, Enterprise & Solution Consulting, Data Science Solutions, and Digital Transformation. As Chief Innovation Officer, SK ensures that experimentation and innovation continues unfettered at Suyati Technologies. He leads the Mekanate team that is developing a Digital Transformation platform using AI, ML, IoT and Big Data technologies. He holds a Masters Degree in Computer Application, and Advanced Certificate in Information Technology Management from IIM, Kozhikode. The opportunity to build technically complex solutions is what runs through his mind all day, and probably keeps him awake at night! Connect with him on LinkedIn.