AI is becoming widespread with new popular and proficient AI tools and many organizations are already started working on their own AI solutions. Those popular AI tools are using algorithms to analyze huge amounts of data to train on. They analyze the data to derive relevant information to be able to provide better results. While AI chatbots are training on thousands of online articles, music production AI tools are training on thousands of songs from different genres. This process provides a framework for artificial intelligence tasks. Machine learning is a branch of artificial intelligence and it requires huge amounts of computing power to analyze the dataset.
MLaaS, stands for “Machine Learning as a Service”, provides a collection of cloud-based machine learning tools, which are provided by cloud service providers. These tools can include machine learning algorithm training and tuning, data preprocessing, data visualization, facial recognition, natural language processing, predictive analytics, preventive analytics, and deep learning. As a cloud service, it eliminates the need to install software or provision their own servers for customers. MLaaS solutions focus on providing developers with easy access to data modeling APIs, machine learning algorithms, data transformations, and predictive analytics tools.
How does MLaaS work?
Similar to other cloud-based services, once a customer purchases the service, the cloud service provider creates the environment customer paid for. It consists of containers and Kubernetes that lay a foundation for FaaS and SaaS solutions. Then the provider provides the set of tools that are needed by the customer to create a fine-tuned machine-learning model. Users can access the system with a user interface to make changes or see the results.
MLaaS solutions cover the value chain of machine learning, including data storage, data processing, model creation, model deployment, model training, and quality control. Most vendors cover all of these segments and may include different functionalities according to the specific services they offer. MLaaS also uses different algorithms to provide a unique workflow according to the client’s needs. Those algorithms are:
- Convolutional neural networks
- Deep learning
- Probabilistic models
- Bayesian inference
What are the benefits of MLaaS?
Similar to other cloud solutions, MLaaS can allow organizations to save both time and money. By eliminating the installing software and hardware process, it allows organizations to begin quickly with ML without extra hustle. Also, it enables organizations to calculate the amount of computing power required for the MLaaS, preventing them to pay for more than they actually need. Organizations can also easily scale their MLaaS plans if there is a change in the plans.
Another important benefit of MLaaS solutions is easy-access tools, which can include APIs for healthcare, face recognition, sentiment analysis, and many more. Developers will also have access to prebuilt models and algorithms, instead of wasting their time on creating their own solutions.
Types of MLaaS
There are several MLaaS solutions that can be differentiated based on the types of services they are offering but in general, they all analyze large amounts of data to find patterns.
- Data labeling: Also known as data tagging, focuses on the process of labeling unlabeled data, which is then will be used to train supervised machine learning algorithms.
- Natural language processing: Focuses on computers’ ability to understand written and spoken language.
- Image recognition: Focuses on understanding the objects in an image or a video.
- Speech recognition: Focuses on converting spoken language into text.
Most major cloud service providers offer MLaaS solutions. Some of them are: