Machine learning, one of the spearheads of artificial intelligence, opens unimaginable perspectives in the current digital era. Within the context of the great data, it is bringing great advances in the most different areas, in a sum and continues what does not seem to have an end.
As machine learning takes precedence over other technologies, machine learning-as-a-service (MLaaS) has come up to meet the growing demands of data-driven industries. MLaaS is a set of services which are offered to companies so that they can access and obtain the benefits of Machine Learning without having to hire a data scientist to do the necessary footwork. As cloud technology is gaining momentum, more companies are outsourcing their data needs to be able to benefit from the advantages of MLaaS.
Machine learning as a service (or MLaaS) refers to the wide range of machine learning tools offered as services from cloud computing providers.
Similar to cloud service models such as SaaS (software as a service) or PaaS (platform as a service), using machine learning as a service means getting instant access to powerful tools over the internet without the money or expertise needed to create them yourself.
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.
With so much riding high on this technology, there is a lot of scope for machine learning to progress within the near future. The capacity for expansion is limitless, which means companies are becoming more competitive in the market. MLaaS helps small and medium-sized business improve their technology, enhance their services, and lower their overall operational costs.
MLaaS is a set of services that offer ready-made, slightly generic machine learning tools that can be adapted by any organisation as a part of their working needs. These services range from data visualisation, a slew of application programming interfaces, facial recognition, natural language processing, predictive analytics and deep learning, among others. The MLaaS algorithms are used to find pattern in data. Mathematical models are built using these patterns and the models are used to make predictions using new data.
Machine learning as a service has various conspicuous advantages, for example, quick and low-cost compute options, independence from the weight of building in-house infrastructure from scratch, no compelling reason to put intensely in storage facilities and computing power, and no compelling reason to recruit costly ML architects and data scientists.
The MLaaS platforms can be the most ideal decision for freelance data scientists, new businesses, or organizations where machine learning isn’t a fundamental part of their operations. Large organizations, particularly in the tech business and with a heavy spotlight on machine learning, will in general form in-house ML infrastructure that will fulfill their particular necessities and prerequisites.
Best Practices to use with ML
The MLaaS market includes a wide range of ML services providers, including Google Cloud machine learning, Microsoft Azure machine learning, IBM Watson machine learning, and Amazon machine learning tools like Amazon Sagemaker, Amazon Rekognition, and Amazon Web Services. The functionality of these MLaaS providers’ machine learning solutions varies, but they generally cover the AI workflow from data visualization and data preprocessing to model training to real-time deployment.
For more complex functionality, like on-premises solutions or dedicated data centers, the pricing of any cloud provider will increase. Since cloud computing services have to manage massive GPU computation pipelines, and are priced accordingly, some startups opt for open source solutions, although that requires significant technical expertise.
Google has a huge MLaaS offering, but it’s not as easy to use as you might think. You’ll need to know programming and software engineering concepts, particularly for deployment, which requires managing configuration files and running a series of commands.
Microsoft’s Azure ML offerings are similarly complex, involving tools like the Azure CLI, which is a command-line interface for managing the Azure machine learning studio. If you’re not familiar with the CLI, it can take some time to get up and running.
Finally, Amazon’s MLaaS is built for technical experts as well. You’ll need to know a lot about programming, as well as how the various AWS tools work together, which can be difficult for less experienced users.
Summary of Benefits
1. Improved Data Management
Good data is key to effective model training and subsequent performance. But data preparation, labeling, and management can eat up a good chunk of productive time. Especially, when the most valuable records are stored deep within on-premise systems.
2. Ready-to-use ML toolkit
The newer breed of ML platforms come pre-furnished with a staunch range of tools, libraries, notebooks, and frameworks for running machine learning projects. Certain providers also have pre-made APIs for common ML use cases such as predictive analytics, image recognition, and sentiment analysis among others.
3. Faster Time-to-Productivity
Machine learning as a service platforms enable teams to get down to business faster. With suitable infrastructure pre-provisioned and pre-configured, sufficient GPU allocated and necessary pipelines set up, data scientists can focus on what matters most — training, validation, and successful deployments.
4.Lower Total Cost of Ownerships for ML Projects
Computing power is a hot commodity, especially when you constantly need to purchase new and new GPUs to scale your delivery capabilities.