What is Machine Learning as a Service?

 

The evolution of a product into a full-fledged cloud service/s has resulted in the emergence of additional services such as Platform as a service (PaaS), Infrastructure as a service (IaaS), and Software as a service (SaaS). Their business expansion has resulted in a struggle in the cloud space industry. Machine Learning as a service (MLaaS) is joining these cloud-based businesses and gradually introducing new competitors. The rising trend of moving data storage to the cloud, managing it, and extracting the most value from it has found an ally in MLaaS, which offers these solutions at a lower cost.

What is Machine Learning as a Service?

Machine learning as a service (Machine Learning as a Service) is a collection of cloud-based machine learning tools offered by cloud service providers. Such tools provide frameworks for artificial intelligence tasks such as machine learning model training and tuning, face recognition, speech recognition, chatbots, predictive analytics, natural language processing, data preprocessing, forecasting, and data visualization.

Amazon Sagemaker (part of Amazon’s machine learning services), Microsoft Azure Machine Learning Studio, and IBM Watson Machine Learning are some examples of Machine Learning as a Service.

In other words, Machine Learning as a Service is a software licensing and delivery model in which the service provider hosts the machine learning tools, making it easier for multiple users to access them from different devices.

With machine learning as a service, businesses can use the services offered by the provider or vendor without creating their own. You can use Machine Learning as a Service to automate multiple tasks and increase the efficiency of workflows that involve humans.

How MLaaS Functions

Simply put, Machine Learning as a Service 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 Machine Learning as a Service 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.

Benefits of using MLaaS

MLaaS encourages small and medium-sized businesses (SMBs) to use machine learning and gather actionable insights from their data. MLaaS platforms eliminate the need to have a specialized, expensive infrastructure in place and make deploying the machine learning technology more approachable, scalable, and affordable.

The following are some of the notable benefits of using MLaaS.

Hosted by the vendor

SMBs don't have to worry about their in-house capabilities as the machine learning software is hosted by the vendor, just like cloud providers. With MLaaS, businesses can get started with machine learning without going through the software installation process or setting up their own servers.

More specifically, ML services streamline the processes associated with the machine learning lifecycle, including data cleaning and preparation, data transformation, model training and tuning, and model version control.

Data management

MLaaS platforms can help you with data management. Since MLaaS providers are essentially cloud providers, they also offer cloud storage and proper ways to manage data for machine learning projects. This makes it easier for data scientists to access and process data as many of them may not have engineering expertise.

Cost-efficient

Another advantage of using MLaaS services is cost efficiency. Setting up an ML workstation is expensive. You require top-tier hardware like high-end graphic processing units (GPUs), which are costly and consume large amounts of electricity. With MLaaS, you pay for hardware only when you use it.

Perform experiments without coding

MLaaS providers also offer tools for data visualization and predictive analytics and APIs for business intelligence and sentiment analysis. Interestingly, some MLaaS providers offer interfaces with drag-and-drop functionality, making it easier to perform machine learning experiments without coding.

Deliver responsible machine learning solutions

Evaluate machine learning models with reproducible and automated workflows to assess model fairness, explainability, error analysis, causal analysis, model performance, and exploratory data analysis. Make real-life interventions with causal analysis in the responsible AI dashboard and generate a scorecard at deployment time. Contextualize responsible AI metrics for both technical and non-technical audiences to involve stakeholders and streamline compliance review.

AI vs ML: What’s the difference between machine learning and artificial intelligence?

Functioning

Deep learning is a subset of machine learning that takes data as an input and makes intuitive and intelligent decisions using an artificial neural network stacked layer-wise. On the other hand, machine learning being a super-set of deep learning takes data as an input, parses that data, tries to make sense of it (decisions) based on what it has learned while being trained.

Feature Extractor

 Deep learning is considered to be a suitable method for extracting meaningful features from the raw data. It does not depend on hand-crafted features like local binary patterns, a histogram of gradients, etc., and most importantly it performs a hierarchical feature extraction. It learns features layer-wise which means that in initial layers it learns low-level features and as it moves up the hierarchy it starts to learn a more abstract representation of the data (as shown in the figure below). On the other hand, machine learning is not a good method for extracting meaningful features from the data. It relies on hand-crafted features as an input to perform well.

Data Dependency

 Machine learning algorithms often work well even if the dataset is small, but deep learning is Data Hungry the more data you have, the better it is likely to perform. It is often said that with more data the network depth (number of layers) also increase hence more computation.

Computation Power

 As you learned that deep learning networks are data dependent, they need more than what a CPU can offer. For the deep learning network training, you need a graphical processing unit (GPU) which have thousands of cores compared to a CPU that has very minimal cores. The computation power not only depends on the amount data but also on how deep (large) is your network, as you increase the amount of data or the number of layers, you need more and more computation power. On the other hand, a traditional machine learning algorithm can be implemented on a CPU with fairly decent specifications.

Training and Inference Time 

The training time of a deeplearning network can range from anywhere between a few hours to months. Yes, you read it right! The training can often last for months. If you have a vast number of data, training a network a more significant data usually takes time. Moreover, as you increase the number of layers in your network, the number of parameters known as weights will increase, hence, resulting in slow training.

 

 

The Saiwa machine learning services

Saiwa's machine learning services make it easy to create, train, deploy, and manage custom learning models. Saiwais a business-to-business and business-to-consumer service platform that offers artificial intelligence and machine learning as a service. At Saiwa, we have made it possible for people and organizations to have access to personalized artificial intelligence and machine learning services at a low cost and without the requirement for machine learning skills and expertise. Saiwa is an easy-to-use Internet service provider for a variety of artificial intelligence applications.

Saiwa has always strived to gather and use experimental data that has been properly validated and researched in laboratories, as an experienced and talented organization in the field of artificial intelligence and machine learning. Nonetheless, because of time and budget restrictions, the likelihood of implementing.

 

Making the Case for Machine Learning in Manufacturing

For manufacturing firms, the prospect of transforming business models, initiating new operating paradigms to support those models and monetizing information for new levels of productivity has made machine learning a top technology priority. This IDC report provides manufacturers with a pro forma business plan to implement machine learning: the why, what, who and how to help articulate the way forward.

Download your copy of IDC’s Making the Case for Machine Learning in Manufacturing to learn about:

•           The digital transformation revenue opportunity within manufacturing

•           Machine learning benefits across manufacturing value chain use cases

•           How to build financial justification and ROI expectations for machine learning