Think You Have A Good EDA Framework? Think Again.

Think You Have A Good EDA Framework? Think Again..

Overview

Author(s): Ryan Ueda Teo

Originally published on Towards AI.


Image by storyset on Freepik

A good data scientist knows their data inside out. To build a good model, you have to be truly connected to the data.

Starting and finishing a Machine Learning project is certainly exciting. However, bringing a Machine Learning product from start to finish is a task far more treacherous than one would imagine.

Building a comprehensive machine learning pipeline is akin to constructing a finely tuned symphony where every note, from data preprocessing to model evaluation, harmoniously contributes to the creation of predictive and intelligent systems. To do so, you must first hone the skill of understanding your… Read the full blog for free on Medium.

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Published via Towards AI

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AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.