Introducing
Your new presentation assistant.
Refine, enhance, and tailor your content, source relevant images, and edit visuals quicker than ever before.
Trending searches
By Saanvi Yentrapati 9C
The AI Project Cycle is a cycle of an AI Project which defines every step an organization must take to get value from that AI Project to get more Return on Investment.
The various stages of an AI project cycle are as follows:
1.Problem Identification
2. Problem Scoping
3. Data Acquisition
4. Data Exploration
5. Data Modelling
6. Evaluation
7. Deployment.
Problem scoping is the process by which we figure out the problem that we need to solve.
Data Acquisition means Acquiring Data needed to solve the problem.
Data Visualization is a part of this where we visualize and present the data in terms of tables, pie charts, bar graphs, line graphs, bubble chart, choropleth map etc.
An AI model is a program or algorithm that utilizes a set of data that enables it to recognize certain patterns.
There are 2 Approaches to make a Machine Learning Model.
The method of understanding the reliability of an API Evaluation and is based on the outputs which is received by the feeding the data into the model and comparing the output with the actual answers.
Deployment is the method by which you integrate a machine learning model into an existing production environment to make practical business decisions based on data.