As new technologies advance, the world of AI has grown to give even small and medium-sized businesses the ability to use machine learning to gather powerful insights from their data. MLaaS or “Machine Learning as a Service” makes technology scalable and affordable, so you only need to pay for what you use.
Machine Learning as a Service (MLaaS) is machine learning technology that is conscripted from another business. This is similar to SaaS (Software as a Service) or PaaS (Platform as a Service), meaning you use the services of a company, rather than wholly create your own.
Using MLaaS means the computation is handled outside of your company – you pay for the machine learning services you need, and data storage may be handled on your personal servers or in the cloud (usually offered with MLaaS providers).
MLaaS software can be used to automatically analyze online product reviews and respond to customers, streamline processes by automating tasks like ticket routing, and even power self-driving cars.
If you’re a huge organization like Facebook or Tesla, it might make sense to develop your own machine learning tools and programs, but if you’re a medium or small business, MLaaS is probably the way to go. When deciding whether to building or buying your own machine learning process, you’ll need to consider how much time you have, your budget, and your team’s technical capabilities.
Building your own machine learning solution can definitely produce great results, but it requires a lot of time and money (not to mention whole teams of data scientists and developers). Buying an MLaaS platform, on the other hand, can produce the same results, be implemented immediately, and is usually scalable to any needs.
MLaaS offers a number of benefits, many of which can be implemented right away for data processing and analysis. Below are the main types and use cases of MLaaS.
Natural language processing (NLP) is a branch of machine learning designed to sort, process, and analyze human language. It uses advanced AI-enabled algorithms to break down text and understand it much as a human would. Although human language is loosely structured by grammar: subject, verb, object, etc., it is still unstructured data that must be deconstructed mathematically and then structured so machines can understand it.
NLP text analysis tools can automatically analyze all kinds of text for valuable insights: emails, social media, online reviews, customer service data, and much more. Topic analysis, for example, uses MLaaS to categorize text by topic, subject, or aspect. And tools like sentiment analysis can automatically analyze the same dataset even further to classify your comments, emails, reviews, etc, by polarity of opinion (positive, neutral, negative) to get to the feelings and emotions of the writer.
With the help of powerful algorithms and neural networks, image and video analysis has come a long way in recent years. Training deep learning models is a laborious task that takes many millions of datasets to allow the models to properly find patterns and deviations in data. MLaaS image and video analysis programs can be quite cost-effective, as most have taken many years and millions of dollars to train but can be purchased by consumers on a “pay only for what you use basis.”
CCTV surveillance is one common use case that can record accidents and crimes and use facial recognition image analysis to find criminals. But it can also be used to follow and analyze traffic patterns, in order to improve control and lessen overall traffic.
MLaaS video analysis programs are also beginning to become popular in retail settings to detect when shelves need to be replenished or to analyze proper product placement and enhance the customer experience.
Computer vision uses image and video analysis, but the goal is to emulate human vision by analyzing and reacting to data in real time. Computer vision technology is behind things like driverless cars that operate with machine learning programs trained on millions of miles of roads and highways.
Computer vision MLaaS is just beginning to become available but the applications are vast:
Speech recognition software uses NLP to understand regular human speech. Some of the most common uses are in smart devices or virtual assistants, like Siri and Alexa, but if you’re not a massive company like Apple or Amazon, it’s definitely not cost-effective to create your own.
Retail chains, airlines, and banks commonly use MLaaS speech recognition for phone-based customer support. And smartphone apps, video game consoles, and speech-to-text messaging and email programs use MLaaS speech recognition to enhance their services.
As machine learning technology grows and advances, so do the number of companies offering Machine Learning as a Service with a variety of machine learning tools for almost any data analysis need. Take a look at the top MLaaS platforms below.
Top MLaaS Companies
MonkyLearn is a powerful MLaaS platform with a suite of text analysis tools to get data-driven insights from all manner of text: documents, emails, social media sites, online reviews, customer support data, and all over the web. MonkeyLearn is completely scalable to handle massive amounts of data for impressive results with techniques like topic analysis, sentiment analysis, text extraction, and more.
And with MonkeyLearn Studio you can combine all of your analyses to work together seamlessly, to take you from data collection to analysis to striking visualization all in a single, easy-to-use interface. Bring together all of your results in MonkeyLearn Studio’s dashboard to easily understand and illuminate your data.
Below is a MonkeyLearn Studio analysis of online reviews of Zoom:
The above shows an aspect-based sentiment analysis. The Zoom reviews are categorized by aspects (topics or subjects) Reliability, Usability, Functionality, etc. and then sentiment analyzed (opinion mined) to show polarity of opinion (positive, neutral, negative). Once this process is set up, you can automatically find the feelings and emotions within reviews and understand which aspects are positive and which are negative.
On the top you’ll see intent classification that shows the purpose behind each text. As these are reviews, most of them are simply classified as Opinion, but intent detection works great on marketing email responses, for example, to automatically classify emails as Interested or Not Interested. Also, on the bottom right are word clouds that show the most used and most important words and phrases by sentiment.
You can play around with the MonkeyLearn Studio public dashboard and manipulate the data by date, category, etc. to see how it works. MonkeyLearn offers a number of free tools or, if you have a lot of data that you regularly need to analyze, see plans and pricing.
BigML aims to bring together all of your data streams to simplify collaboration and analysis sharing within your company. They’re focused on a number of industries: aerospace, automotive, energy, entertainment, financial services, food, healthcare, IoT, pharmaceutical, transportation, telecommunications, and more.
You can use pre-trained analysis models or train your own with classification and regression and time series forecasting.
TensorFlow is a free open-source library for creating machine learning models originally created for internal use at Google but made available to the public. It offers flexibility in terms of machine learning tasks with a focus on creating deep neural networks.
Using intuitive APIs, like Keras, TensorFlow is a great asset for model building if you’re a data scientist or have a fair amount of computer engineering experience.
IBM Watson offers proven adaptability to a variety of industries and the ability to build to massive scale across any cloud.
Watson Speech-to-Text is the industry standard for transforming spoken language into text in real time, and Watson Language Translator is one of the best text translation tools on the market.
Watson Studio is great for data preparation and analysis for any business or field, and their Natural Language Classifier makes building advanced MLaaS analysis models easy.
Check out the products page for pricing.
Apache Mahout is part of the Apache Software Foundation aimed at producing free implementations of machine learning algorithms. The distributed linear algebra framework allows mathematicians, statisticians, and data scientists to implement their own algorithms to create machine learning frameworks.
Microsoft Azure’s Stream Analytics offers real-time text processing for huge datasets with pre-trained models and custom-created analytics that integrate directly into existing systems.
Microsoft Azure is fast, flexible, and scalable with end-to-end analytics that can be set up with custom code. Azure Resource Manager makes tailoring models easy and you can move existing models to Azure Analysis Services to bring all of your analyses together.
Google Cloud ML is a MLaaS solution for both text and image analysis that allows you to connect with all of Google’s tools: Gmail, Google Sheets, Google Slides, Google Docs, and more.
Google AutoML Natural Language is one of the most advanced text analysis tools on the market, and AutoML Vision allows you to automate the training of custom image analysis models for utmost accuracy built to your needs.
As machine learning technology advances, it’s probably worth purchasing MLaaS for whatever your needs, rather than building your own tools and models, unless you have a data science and coding background, of course.
If you want to use AI-powered machine learning on any form of text data to understand your customers’ needs or perform market analysis, and more, MonkeyLearn’s suite of text analysis tools can be put to work for immediately actionable insights.
MonkeyLearn Studio is easy to set up and offers integrations with many of the tools you already use, like Excel, Google Sheets, SurveyMonkey, Zapier, Zendesk, and more. You can even train models to the language of your business and your criteria and simple APIs help make data mining easy, 24/7 and in real time.
November 23rd, 2020