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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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