Artificial intelligence and machine learning technologies have recently gained ground in the corporate sector. Still, most companies don’t yet understand how to use their data properly and what benefits can be derived from this.
Forbes Insights, in collaboration with Dell Technologies and Intel, has interviewed more than 700 top managers about plans for artificial intelligence and machine learning use. While 3 out of 4 CxO (Chief Experience Officer) say AI is a key component of their digital development plans, less than 25% have implemented it in their organization. Only 11% of respondents have implemented an enterprise-wide data processing strategy, and only 2% say they have a reliable data management process. Earlier, we wrote about why AI and machine learning should be implemented in your business, and our position on this issue remains unchanged. Using advanced technology is a sure way to success. However, many companies do not have the infrastructure to deploy such solutions, not to mention the ability to develop their own one.
At the same time, in the software market there is a tendency for developing products as a full-fledged services in the cloud. You may have heard of things like platform as a service (PaaS), infrastructure as a service (IaaS), and software as a service (SaaS). Their growth as a market has led to a battle in the field of cloud space. The advent of Machine Learning as a Service (MLaaS) caught up shortly after. The growing tendency to transfer data storage to the cloud, to maintain it and to extract the best information from it made up an excellent tandem with machine learning technology (ML), making it possible to provide such solutions at affordable prices.
What is MLaaS?
Machine Learning as a Service (MLaaS) is a set of services offering the implementation of cloud-based machine learning tools. MLaaS helps customers benefit from machine learning without the associated costs, time and risk of creating an internal machine learning team. Infrastructure problems, such as data preprocessing, model training, model assessment, and ultimately forecasts, can be mitigated with MLaaS.
Service providers offer tools including predictive analytics and deep learning, APIs, data visualization, natural language processing, and more. The computing aspect is handled by service provider’s data center.
How does MLaaS work?
To put it simply, MLaaS is a set of services that offer off-the-shelf, one-of-a-kind, universal machine learning tools that can be adapted by any organization as part of their work needs. These services range from data visualization, multiple application programming interfaces, face recognition, to natural language processing, predictive analytics and in-depth study. MLaaS algorithms are used to search for patterns in data. Mathematical models are constructed with these templates, and the models themselves are subsequently used for forecasting with the help of the new data obtained.
There are 3 main levels of cloud AI settings. The choice depends on your use case, machine learning experience and budget.
Packaged machine learning as a service is the lowest level of MLaaS settings. At this level, the provider handles you a packaged, ready-to-use model with a predefined set of artificial intelligence options, such as face recognition, object detection, and text extraction.
This is the easiest thing to do, as the AI provider is 100% responsible for data provisioning, training, testing and deployment, so you don't need any machine learning skills. After connecting to your library, you can immediately start using the model to label visual content. Packaged MLaaS is implemented when an organization has a relatively basic use case for AI, where more general auto-tagging terms are acceptable to their business.
Example: A travel company uploads thousands of vacation photos to its library every week for future use in marketing purposes. They don’t have time or manpower to manually tag photos on their own, so they connect the Packaged MLaaS model to their library to tag them automatically. Now, when they need an image for the honeymoon webpage, they can search for “Couple,” “Beach,” and “Sunset,” and the library will display photos that were automatically tagged with the Packaged MLaaS model.
Packaged MLaaS Examples: Microsoft Azure Computer Vision, Google Cloud Vision, Amazon Rekognition.
This type of MLaaS is more complex and is used to solve specific business problems. With this level of customization, the machine learning vendor provides you with a space where you can classify your own media, add images, and label them appropriately to train the model until you are satisfied with its level of confidence. As you will conduct training and testing, you need to understand basic statistics and levels of accuracy. Model training takes some time - for example, Microsoft offers about 30 images for each tag to get an acceptable level of confidence. This form of MLaaS is used if there is a business problem that cannot be solved with common auto-tags.
Example: A telecommunications company has a library for storing images of the phones they offer. As they sell phones of different brands and generations, the “Cell Phone” auto-tag is too versatile to make their visual search easy to use. They decide to introduce the Guided MLaaS model to teach the model to distinguish between different brands and generations (for example, iPhone 8 or Samsung Galaxy S9). After bringing the model to a confidence level of 99%, they link it to their library so that it can automatically mark new downloaded images with the appropriate phone tags.
Examples of Guided MLaaS: Microsoft Azure Custom Vision, Google AutoML, IBM Watson Visual Recognition.
Specialized machine learning as a service is the model that provides the most flexibility in terms of tools, platforms, and infrastructure, but also requires a thorough understanding of machine learning models and system integrations. Using Specialized MLaaS, the vendor provides you with a virtual machine that is pre-packaged with standard machine learning software and add-ons so that the internal IT team or third-party developers can code, train, package and deploy the model. In this case, you will need a data specialist and a developer or technical integrator to provide data, training, testing and deployment. Specialized MLaaS is used only for very specific cases and requires the largest investment of resources from all MLaaS models. But it's worth it.
Example: A pharmaceutical company conducts a scientific experiment that requires the identification and labeling of various strains of bacteria and viruses. Since the differences between the bacteria are so insignificant that they require in-depth knowledge to identify them, the scenario for using the company is too complex for Guided MLaaS. They hire a data specialist and programmer (or third-party company) to create a fully customizable AI model using the machine learning service. After developing, training, and testing the model, they can start automatically tagging each specific strain of bacteria.
Examples of specialized MLaaS: Azure Machine Learning Service, Google Cloud ML Engine, Amazon Sagemaker.
Leading Machine Learning as a Service providers
.1 Microsoft Azure Machine Learning Studio
Microsoft Azure offers scalable machine learning services for businesses of all sizes. Azure supports a set of platforms, programming languages, databases, operating systems and devices. It provides interoperability between devices supporting all major mobile platforms.
.2 AWS Machine Learning
AWS stands for Amazon Web Service. Amazon Machine Learning is highly automated. Without creating code, it helps companies create machine learning models. AWS makes machine learning available to developers without learning complex algorithms and technologies. Amazon ML is based on a pay-as-you-go pricing model.
.3 IBM Watson Machine Learning (WML)
WML runs on IBM Bluemix. Both scientists and data developers use WML for training and assessment. WML is designed to answer questions about the operationalization, deployment, and derivation of business values from ML models. WML also uses visual modeling tools that help users gain understanding, make decisions faster, and quickly identify patterns.
.4 Google Cloud Machine Learning Engine
The scope of software from Google is almost unlimited. Google's machine learning engine is based on TensorFlow. This ML engine is integrated with all other Google services such as Google Cloud Storage, Google Cloud Dataflow, Google BigQuery and others. Google's machine learning engine provides users with a replacement for creating ML models for data. Data can be of any size and type.
How to integrate machine learning as a service into your business?
How to take full advantage of MLaaS? To achieve this, a solution needs to be implemented in software, and a company that has ML and AI technologies in its technological stack will help you do this. The MassMedia Group, for example, is actively involved in introducing advanced technologies into the software and has already brought several businesses to a whole new level thanks to this.
Implementation of MLaaS can be a great help for business, however, the main task is to find experts who will integrate it into the company's business processes. If you are reading this article, then you have already found such experts and all you need is to click the ‘Contact us’ button in the corner.