Cloud computing has created an impact in the field of technology so great such that it literally simplified the need for computing purposes. The advantages also include faster access to data, monitoring, deployment of applications and more. As the common saying, Even though the cloud is the future, Intelligent or Smart clouds are the future of science.<\/p>\n
That\u2019s right you read it, Intelligent cloud. Machine systems that can think, predict, learn and make decisions like us, humans. So, how is it possible for machines to perform like this. Machine Learning is the sole dependent factor for this. Machine learning uses mathematical models and algorithms<\/p>\n
A General-purpose service<\/strong> AWS Sagemaker is a service that enables a data scientist\/developer to build and train machine learning models for analytical applications in the cloud platform. Sagemaker generally provides a framework for developers and data scientists to manage the machine learning model process. It is a fully managed end to end machine learning service. The interesting feature of Sagemaker is that it provides built-in and common machine learning algorithms, along with other tools, such that the data models can be used by IT professionals without any prior expertise.<\/p>\n Sagemaker uses a streaming algorithm that only makes a single pass over the data that is given as input. The streaming algorithms are infinitely scalable i.e, they can handle unlimited amounts of data. Sagemaker uses containers to spread the workload of the machine learning tasks across its network. This in turn, significantly increases the speed at which models are trained and deployed.<\/p>\n You must be wondering how does Sagemaker work. Isn\u2019t it? Well, you must wait for Part II. Stay tuned.<\/p>\n
\nIn order for machine learning to work flawlessly, it required machine data models which had to be built and deployed by data scientists but the one main challenge faced was computing power because data are usually not static. Data always keeps being accumulated and it is dynamic in nature. In order to address the issues in building and deploying machine data models, cloud computing provider Amazon launched the general purpose service called Sagemaker in 2017.<\/p>\n