Model deployment in Machine Learning refers to the process of making models available in a production environment where they can be used to provide a prediction that is needed for business operations or in any other software requirements. The result of Machine Learning can bear fruits only after it gets deployed as this is when the models actually add any sort of value. Deploying an ML system is based upon what architecture is used. PaaS and IaaS are preferred for working in a business environment with lesser traffic. If the requirement is widespread, then cloud technologies from providers such as Amazon, Google, and Microsoft are used. If the application is containerized, then deployments can be done easily using a container orchestration platform such as Kubernetes as well.
If you are looking for an online course to learn Machine Learning, I recommend this Machine Learning Online Course by Intellipaat.
Back