Types of MLaaS
MLaaS solutions can be differentiated based on the kind of services they offer. In essence, these solutions analyze large volumes of data to discover hidden patterns. The difference in the type of input data, the algorithms used, and how the output is used give rise to different kinds of MLaaS.
Data labeling :Data labeling, also known as data annotation or data tagging, is the process of labeling unlabeled data. Labeled data is used to train supervised machine learning algorithms. Data labeling software differs based on the type of data they support.
Natural language processing : A branch of computer science and artificial intelligence called "natural language processing" (NLP) gives computers the ability to comprehend spoken and written language. Due to the rapid development of deep learning, more notably deep neural networks, NLP has made great progress in recent years.
Sentiment analysis or opinion mining is a popular application of NLP that helps determine the social sentiment of products, services, or brands by analyzing customer feedback, reviews, and social media posts.
Another usage of NLP is text mining, which allows users to extract useful information from both organized and unstructured text. Data from various sources, like as emails, polls, and customer reviews, can be ingested by text analysis software, which then provides visuals and useful insights.
Image recognition : A computer vision task called image recognition aims to comprehend the information of pictures and movies. With the aid of computer vision algorithms, picture recognition software applies a bounding box or label to the input image.
With the advent of IoT devices, collecting image data is effortless, making it easier to train algorithms. Object recognition, image restoration, and facial recognition are all made possible by image recognition software.
Speech recognition : Text is generated from spoken language using speech recognition. Voice recognition software facilitates the text conversion of audio and video files as well as the handling of customer care phone queries. Voice recognition technology is used by virtual assistants like Siri and Google Assistant to convert your speech into a format that computers can understand.
How MLaaS works
MLaaS is built on cloud infrastructure and resembles many of the features of a SaaS solution. Instead of offering a buffet of tools, an MLaaS provider may offer only a single service, for example, a perfectly tuned machine learning model.
With MLaaS, all aspects of the machine learning process are handled by a single provider, ensuring maximum efficiency. The features of MLaaS platforms will vary depending on the provider you choose. Still, in most cases, you'll get a cloud environment on which you can prepare data, train, test, deploy, and monitor machine learning models.
To better understand how MLaaS works, let's consider a simple example of a coffee shop.
he coffee shop owner aspires to increase revenue by using the power of machine learning. However, it's improbable that the coffee shop business will have the needed in-house talent to deploy machine learning models. Therefore it's better to rely on a third-party provider that offers machine learning as a service.
The MLaaS provider may install several IoT devices to collect data about footfall trends and also collect data from the POS machine. Doing so allows the service provider to better understand the peak timings, the flavors customers like the most, and frequently bought together items.
The MLaaS provider will employ data scientists and engineers to work on the collected data. They may also offer web-based applications with a drag and drop interface that the business owner can use without needing expertise in machine learning.
The MLaaS provider help transform the collected data into useful information, helping the business owner to make precise decisions about marketing and sales strategies. The data collected can also help predict what combos customers are more likely to purchase.
MLaaS can also enable businesses to run sentiment analysis and understand how customers perceive them by analyzing social mentions, posts, and reviews. In short, companies, regardless of their size, can apply machine learning with the help of MLaaS.
When to use MLaaS
Assume you are already aware with an MLaaS provider's offerings, such as those of Amazon Web Services (AWS) or Google Cloud Machine Learning Engine. In that situation, integrating their services with your current system will be simpler.
MLaaS can assist with effective service management if your company employs a microservices-based architecture. Let's say you want to include machine learning into a program you're creating. In this situation, MLaaS will be a wise decision because, in most circumstances, it can be integrated utilizing APIs.
If your own team is quite small and lacks ML skills, MLaaS will also be helpful. Even if they lack the requisite technology, this service can let them use machine learning and supplement their efforts. Consider aspects like time available, price, and technical skills of your team while selecting the best MLaaS service.
When not to use MLaaS
If the amount of training required is significantly high, building an in-house infrastructure may be a cheaper option. Likewise, if the amount of training data involved is gigantic, the development process with MLaaS solutions might be slower as data is stored and accessed from the cloud.
If you deal with highly sensitive data, you may have to heavily scrutinize your MLaaS provider. Of course, cloud platforms have remarkable end-to-end security features. But anytime data moves from one place to another, there's always a risk factor involved.
Furthermore, if you wish to perform several customizations on complex ML algorithms, it’d be better to opt for on-premise infrastructure.
Top machine learning software
Machine learning software enable you to make predictions and data-driven decisions. They can provide automation and AI features to your applications and help solve classification and regression problems.