AI Application Life Cycle
Want to build your very own AI Application? Read to learn about the steps that go into building it from scratch!
Building an AI Application is just as exciting as it sounds! From auto-recommendation systems in Netflix, Amazon, Flipkart to blurring backgrounds in meetings, the applications of AI is widespread.
Developing AI applications is a cyclic process. Breaking it down in steps it could be summarized as follows:
Prepare: To prepare data, one must go through various forms of data. This step is mainly concerned with concepts such as ETL(Extract Transform Load), ELT(Extract Load Transform), data pipelines and so on. The output is a data set that is meaningful in a given context.
Experiment: Concerned with building, training and testing models. The model evolves over time. The best model is registered to be deployed soon.
Deploy: The point where you ship the ML model. Commonly, this is done by packaging it in the form of a flask API. This is followed by the process of containerization. The end point after deployment can be listed as a web-service. Post-deployment comes the crucial step of monitoring.
Understanding the AI Application Life cycle is core to developing an application with ease. Better the understanding of how these specific steps work, the easier it becomes to develop the application.
𝗟𝗶𝗸𝗲, 𝗦𝗵𝗮𝗿𝗲, 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲
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