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• performing basic statistical analysis such as drawing graphs (or any other visual representation) and comparing
different properties of the data set are carried out. The initial insights gained help to get an understanding of the
data and later on, help in algorithm selection, metrics choice, etc. This complete procedure is called “Exploratory
Data Analysis”. It is useful to see which elements are more essential and what the overall trend of the data is.
The quality of the data being used by the AI model is an important driver of a good model. Hence, the data collection
and data exploration stages should be carried out with utmost care. These stages also consume the most time.
Phase II: Desig n and T esting the A I Model
Phase II is also divided into two stages: modelling and evaluation. Let us discuss about them in detail.
Modelling
Every AI model relies on the ability to quantitatively characterise the relationship between parameters. Thus, when we
talk about constructing AI models, we are referring to the mathematical approach to data analysis.
Modelling is the process through which several models based on graphical data can be constructed and even tested for
advantages and disadvantages. engineers go through multiple models to determine the best model configuration.
ence, the design phase is an iterative process. yperparameter fine tuning provided by most frame orks helps to
narro do n the number of feasible solutions. hese approaches assess performance for many configurations, compare
them, and inform of the best ones.
It is vital to the success of the AI project that all of the various individuals engaged have proper access to data, tools,
and processes to collaborate across different phases of model creation. During this stage, you must assess the various
AI development platforms which are commonly used to build and run models are given below:
• Open languages: Python, R, and Scala
• Open frameworks: Scikit-learn is the most popular, XGBoost, TensorFlow, etc.
• Approaches and techniques: Classic ML techniques from regression, Reinforcement Learning, Generative adversarial
networks (GAN) framework
• Productivity- increasing capacities: Visual modelling (graphic representation of objects), Automated Machine Learning
(AutoAI) to help with feature engineering, selection of appropriate algorithm and hyperparameter optimisation
• Tools to help in the development process: DataRobot, H2O, Watson Studio, Azure ML Studio, Sagemaker, Anaconda, etc.
Various AI development platforms provide substantial documentation to assist development teams. Depending on the
AI platform chosen, you must go to the following web pages for this documentation:
• Microsoft Azure AI Platform
• Google Cloud AI Platform
• IBM Watson Developer platform
• BigML
• Infosys Nia resources
Introducing various AI Platforms and Links to Respective Platforms
Listed below are links to the different platforms studied above,
IBM Watson I atson is an AI platform developed by I . It combines various AI technologies
and services to provide solutions for a wide range of industries and applications.
Link: https://cloud.ibm.com/login
oogle Dialogflo ( ssentials) oogle Dialog o is a conversational AI platform that allo s developers to build
chatbots, virtual assistants, and other conversational interfaces. It provides essential tools and features for creating
natural language understanding ) models and designing conversational o s.
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