What is Machine Learning as a Service? Benefits And Top MLaaS Platforms

Machine learning uses statistical analysis to generate predictive output without the need for explicit programming. It uses a chain of algorithms that learn to interpret the relationship between data sets to achieve its goal. Unfortunately, most data scientists are not software engineers, which can make it difficult to scale to meet the needs of a growing company. Data scientists can easily handle these complications thanks to Machine Learning as a Service (MLaaS).

What is MLaas?

Machine Learning as a Service (MLaaS) has recently gained a lot of attention for its benefits to data science, machine learning engineering, data engineering, and other machine learning professionals. The term “machine learning as a service” refers to a broad cloud-based platform that uses machine learning techniques to provide answers.

The term “machine learning as a service” (MLaaS) refers to a set of cloud-based offerings that make machine learning resources available to users. Customers can reap the benefits of machine learning with MLaaS without having to build an in-house machine learning team or assume the associated risks. A variety of services are available from a variety of providers, including predictive analytics, deep learning, application programming interfaces, data visualization, and natural language processing. The service provider’s data centers take care of all the IT.

Although the concept of machine learning has been around for decades, it has only recently entered the mainstream, and MLaaS represents the next generation of this technology. MLaaS aims to reduce the complexity and cost of implementing machine learning within an organization by enabling faster and more accurate data analysis. Some MLaaS systems are designed for specialized tasks, such as image recognition or text-to-speech synthesis, while others are designed for broader cross-industry uses, such as sales and marketing.

How does MLaaS work?

MLaaS is a collection of services that provides general, pre-built machine learning tools that each company can adapt to their needs. Data visualization, multiple APIs, facial recognition, NLP, PA, DL and more are on the menu here. Data pattern discovery is the primary application of MLaaS algorithms. These regularities are used as the basis of mathematical models, which are then used to generate predictions based on new information.

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In addition to being the first full-stack AI platform, MLaaS integrates multiple systems including mobile applications, business data, industrial automation and control, and cutting-edge sensors such as LiDar. In addition to pattern recognition, MLaaS also facilitates probabilistic inference. This provides a comprehensive and reliable ML solution, with the added benefit of allowing organizations to choose from multiple approaches when designing a workflow tailored to their unique requirements.

What are the benefits of MLaas?

The main advantage of using MLaaS is not having to worry about building your infrastructure from the ground up. Many companies, especially small and medium-sized enterprises (SMEs), do not have the resources and capacity to store and manage large amounts of data. Adding to the expense is the need to buy or build massive storage space to store all this information. Here, the MLaaS infrastructure covers data storage and administration.

Because MLaaS platforms are cloud providers, they offer cloud storage; they provide the means to properly manage data for machine learning experiments, data pipeline, and more, making it easy for data engineers to access and analyze data.

Companies can use predictive analytics and data visualization solutions from MLaaS providers. In addition, they provide application programming interfaces (APIs) for many other uses such as sentiment analysis, facial recognition, credit risk assessment, corporate intelligence, healthcare, etc.

With MLaaS, data scientists can start using machine learning right away instead of waiting for long software installations or waiting for servers, as is the case with most other cloud computing services. With MLaaS, the actual computing is done in the provider’s data centers, making it very useful for businesses.

Major MLaaS platforms

1. AWS Machine Learning

When it comes to cloud services, AWS Machine Learning can do it all. It gives businesses access to virtually unlimited resources, including computing power and data storage. Even more advanced technologies are available, like MLaaS.

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The machine learning solutions offered by AWS Machine learning are: Amazon Polly, Amazon Lex, Amazon Sagemaker, Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe.

2. Google Cloud Machine Learning

Developers and data scientists can use the Google Cloud Machine Learning (GCP) AI platform to create, run, and manage machine learning models. The Tensor Processing Unit, a chip developed specifically by Google for machine learning, is a key differentiator of this service.

GCP’s machine learning solutions include: Built with AI, Conversational AI, and Dialogflow CX

3. Microsoft Azure ML Studio

Microsoft Azure ML Studio is an online developer interface that data scientists can use when developing, rapidly training, and deploying machine learning models. Although it started in the offline world, Microsoft has made great strides to reach the main web players.

Sci-kit learns that TensorFlow, Keras, MxNet, and PyTorch are popular frameworks that can be used with Azure Machine Learning Studio.

4. IBM Watson Machine Learning

Machine Learning models can be created, trained and released with IBM Watson Machine Learning. Popular frameworks such as TensorFlow, Caffe, PyTorch, and Keras provide graphical tools that make model building a breeze.

5. BigML

BigML is a complete machine learning platform with many methods for managing and creating machine learning models. The tool supports application predictions in many fields, including aviation, automotive, energy, entertainment, finance, food and agriculture, healthcare, and the Internet of Things. BigML provides its services through a web interface, a command line interface, and an application programming interface.

Global market and impact so far

ReportLinker, a market research provider, forecasts that the machine learning as a service market will reach $36.2 billion globally by 2028, expanding at a compound annual growth rate (CAGR) of 31.6% between 2018 and 2028.

Key factors in the growth of machine learning as a service business include interest in cloud computing and developments in AI and cognitive computing. The need for effective data management is growing as more companies move their data from on-premise to cloud storage. Because MLaaS platforms are essentially cloud providers, they make it easier for data engineers to access and process data for machine learning experiments and data pipelines.

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The global economy and financial institutions are in shambles after Covid-19 killed millions of people. With the rise of this COVID-19 pandemic, it can be assumed that artificial intelligence technologies will help in the fight against it. Using population monitoring strategies enabled by machine learning and artificial intelligence, cases of COVID-19 are being monitored and traced in many nations.

Below are the drivers driving the MLaaS industry:

  • Machine learning as an agent of artificial intelligence
  • The rise of Big Data and the need for cloud computing

To summarize:

There are many different tools to help you create ML. Machine learning development environments can be found with specialized tools that take care of automation, allow multiple releases, and provide a comprehensive ML research and development setting. Because it can scale to infinity and then scale back to the size of a current computer with just a few clicks, MLaaS is the right solution for the complexity and dynamics of the modern world.

If you’re a data scientist or engineer, you know how hectic your days can be. MLaaS provides a wealth of resources to help you do more in less time. The key advantage is that you won’t spend money on new infrastructure, computers, setup or maintenance.

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Dhanshree Shenwai is a Consulting Content Writer at MarktechPost. He is an IT engineer and works as a delivery manager in a major global bank. He has good experience in FinTech companies covering Finance, Cards & Payments and Banking domain with strong interest in AI applications. He is passionate about exploring new technologies and advancements in today’s evolving world.


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