Machine Learning as a Service: Definition, Examples, and How to Apply It in Your Business

The service handles deployment, operationalization and machine learning models which can create value for businesses. Most MLaaS service providers offer ascendable and customised technologies to companies and provides them with the advantage of choosing specific services that are ideal for them. The biggest benefit that MLaaS offer is the freedom from the burden of building in-house infrastructure from scratch. Many companies, especially small and medium sized businesses (SMBs), lack the infrastructure to store massive volumes of data and the internal resources to manage them. The investment in storage facilities for all this data is also a costly affair. This is where the MLaaS platform takes responsibility for management and storage of data.

Areas of use of MLaaS

The process will be more time-effective and will need less human supervision when working with structured data than when importing unstructured data. However, Cloud AutoML deploys highly accurate models and predictions with either kind of dataset. Despite these limitations, this MLaaS could be ideal for optimizing your processes if your needs stay within its boundaries.

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Machine Learning as a Service is a program licensing model in which the service provider hosts the machine learning tools, allowing many customers to access them from different devices. Businesses that employ machine learning as a service might leverage the services provided by the provider or vendor instead of developing their own. Machine learning as a service may be used to automate various operations and boost the efficiency of human-assisted workflows. Both for ethical and sometimes regulatory reasons, we need to be able to explain how our machine learning model makes its decisions. Fortunately, our cloud providers have tools to help us out in this area. In other words, machine learning is one method we can use to try to achieve artificial intelligence.

  • After reading this article you shouldn’t need much more than an access call with your provider of choice to find out the specific ways in which Machine Learning can help your business.
  • With MLaaS, the provider’s data centres handle the actual computation, so its ease of convenience at every turn for businesses.
  • Text-to-speech and speech-to-text services are cloud services for converting text to audible speech and vice versa.
  • I red this joke on internet “no data in, no science out” but unfortunately its the truth of todays time.
  • However, this becomes a bit more complicated when deploying your model on the cloud.

This platform is a free open-source library for building machine learning models that was originally created for internal use at Google but later made available to the public. It offers flexibility in terms of machine learning tasks by focusing on building deep neural networks. Adding to its extensive SaaS range, Google has taken another giant step further into cloud service dominance by creating a sophisticated MLaaS platform. Building on its existing SaaS offerings, Google provides machine learning services for natural language processing and APIs for speech and translation, as well as for video and image recognition. With the development of data science and AI, the power of ML has improved significantly, and businesses are now more aware of the potential benefits, which has increased the use of MLaaS.

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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. Apart from the numerous benefits MLaaS provides, one of the primary attractions of these services is the fact that businesses are able to get started quickly with ML. They don’t have to endure the laborious and tedious software installation processes, or provide their own servers as they would with most other cloud computing services.

Areas of use of MLaaS

This is achieved by using algorithms which narrow down and specify common ‘if-then’ programs, resulting in more granular outcomes, widening the scope of its findings, and creating more possible outcomes. You may utilize Cloud AutoML if you are a novice user and Google Cloud ML if proficient, depending on your degree of expertise. Also, the client may combine the application with all Google services and transfer information to the cloud. It makes use of Google’s cutting-edge neural architecture-based information transport and search capabilities. Consequently, creating, enhancing, and setting models with the tool has advantages for proficient users and novices. The system’s visual computing features enable users to see patterns in the data swiftly, get insightful knowledge from it, and reach conclusions.

Characteristics of the technology

For example, if the company deploys event-driven machine learning, it might need a specific data management framework to align online and offline data, and this is almost impossible with MLaaS. Next, when using ready-made solutions provided by MLaaS vendors, a company doesn’t develop its in-house expertise, resulting in a lack of strategic advantage. Finally, with MLaaS, you are heavily dependent on the external provider, which can change its product lists, pricing options, and product or service characteristics with a detrimental effect on the activities of your company. If you want to develop an ML model for solving a very specific task (e.g., analyzing the impact of potential drugs), MLaaS platforms can assist you in building, training, and deploying your own machine learning model. Efficient training, automated model tuning, one-click deployment, scalability – all these can be ensured by MLaaS providers. While knowledge is still important, cloud providers have created some turnkey services that let us make use of very powerful machine learning technology through a simple API call.

Using an MLaaS provider means doing the work that goes into building, training, and deploying ML models outside your company. In such a case, you only have to pay for the ML services you use and data storage in the cloud (if you can’t handle it on your servers). Service providers offer tools such as predictive analytics and deep learning, APIs, data visualisation, natural language processing and more. The computation aspect is handled by the service provider’s data centers.

ML requires lots of data

The various data and KPIs at your fingertips, regardless of your industry, are gold you can use to obtain more accurate business forecasting. Because of its enhanced capacities to be precise, scale, adapt to variable behavior, and provide results in real-time, Machine Learning can independently fuel these forecasts. You may start uncovering trends and determining if a given option is worth exploring—or if the information is less valuable—by taking the time to explore the information you have with data visualization tools. For example, when you’re searching for a specific term on Google, under the first result, Google shows you a list of questions related to this term.

Instead, MLaaS can step in, trying to create models that could benefit a particular customer pool. These MLaaS companies can do the hard work of creating the model, training, and setting up the endpoint. MLaaS hangs out under the umbrella of microservices architecture, so customers use an API to access the machine learning model. The microservices architecture piecemeals services together, granting the company the capacity—the machine learning services agility—to respond if one of their services becomes incredibly popular. The key is in the fact that the users (in this case, organisations who purchase MLaaS) do not need to handle the actual computation. MLaaS is also the only full-stack AI platform that consolidates systems ranging from mobile application, enterprise information, industrial automation and control, as well as advanced sensors such as LiDar, among others.

How Does MLaaS Benefit SMBs:

Google Cloud AutoML is composed of a variety of category branches, each depending on the use case involved when adopting this MLaaS provider. This way, you get the Machine Learning models and processes that best suit your needs. Let’s now make an MLaaS comparison of the most prominent service providers. If you find yourself in either of these situations and you end up deciding to DIY your own Machine Learning software, your main priority should be having all the right resources.

SMBs don’t need to worry about their own internal capabilities because the machine learning software is hosted by the vendor, such as cloud providers. Businesses may begin learning machine learning with MLaaS without having to set up their own servers or install software. The owner of the restaurant wishes to increase sales by leveraging machine learning. However, the restaurant business is unlikely to have the in-house talent to apply machine learning models. As a result, they are relying on a third-party supplier that provides machine learning as a service is preferable.

ML vs MLaaS: which to use?

A pay-as-you-use business model is also being offered by enterprises, making ML solutions more readily available to customers. Many organisations have accelerated their migration to public cloud solutions as a result of the COVID-19 epidemic, in order to provide flexibility to meet unforeseen spikes in customer demand for services. As a result, the need for AI services, which are now widely available from many cloud providers, has grown. MLaaS providers offer tools for data visualization and predictive analytics and APIs for business intelligence and sentiment analysis.

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