ML-as-a-Service (MLaaS)
- Lets organizations use machine learning without purchasing expensive hardware or hiring specialized expertise.
- Delivered as cloud-hosted managed services and platforms (examples: Amazon SageMaker, Google Cloud AutoML).
- Enables quick experimentation and provides access to current machine learning algorithms and technologies.
Definition
Section titled “Definition”MLaaS, or Machine Learning as a Service, is the practice of providing machine learning capabilities through the cloud, allowing organizations to leverage ML without the need for expensive hardware and specialized expertise.
Explanation
Section titled “Explanation”MLaaS provides cloud-hosted machine learning services and platforms that organizations can integrate into existing systems and processes. It enables a range of applications by making ML accessible without large upfront investments in hardware or specialized personnel. Organizations can experiment with machine learning, quickly test and evaluate potential value, and—when ready—deploy models without building all infrastructure in-house.
MLaaS also grants access to the latest machine learning algorithms and technologies maintained by cloud providers, which can improve model accuracy and performance for organizations that lack resources to continuously update in-house ML capabilities. Overall, MLaaS offers a flexible and cost-effective path to incorporate advanced ML features into business workflows.
Examples
Section titled “Examples”Amazon Web Services (AWS)
Section titled “Amazon Web Services (AWS)”AWS offers a suite of machine learning services, including Amazon SageMaker, a fully managed platform for training and deploying machine learning models. AWS also provides services such as Amazon Rekognition for image and video analysis, and Amazon Lex for natural language processing.
Google Cloud Platform
Section titled “Google Cloud Platform”Google Cloud Platform provides a range of machine learning services such as Google Cloud AutoML, which allows organizations to train and deploy custom machine learning models without the need for specialized expertise. Google also offers services such as Google Cloud Vision for image analysis and Google Cloud Natural Language for text analysis.
Use cases
Section titled “Use cases”- Improving customer experience through personalized recommendations.
- Optimizing business operations through predictive maintenance.
Related terms
Section titled “Related terms”- Machine learning
- Cloud computing
- AutoML
- Managed ML platforms
- Amazon SageMaker
- Amazon Rekognition
- Amazon Lex
- Google Cloud AutoML
- Google Cloud Vision
- Google Cloud Natural Language