Data Science Dojo

AUGUST 1, 2023

Challenges in traditional AI chatbot development: Role of LLMs The current practices of building AI chatbots have limitations when it comes to scalability. Building an efficient LLM application using vector embeddings Vector embeddings are a type of representation that can be used to capture the meaning of text.

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Iguazio

FEBRUARY 20, 2024

While building gen AI application pilots is fairly straightforward, scaling them to production-ready, customer-facing implementations is a novel challenge for enterprises, and especially for the financial services sector. What is the Potential Value of Gen AI and Analytics for Financial Services? View the entire webinar here.

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IBM Journey to AI blog

JANUARY 10, 2023

Grasping these opportunities at IBM, we’re increasingly building our specialism in process mining and data analysis tools and techniques we believe to be true ‘game changers’ when it comes to building cultures of continuous change and innovation. Starting from the top: Focusing on the right change and making it feel right.

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Data Mining Data Mining Data Mining Analytics 52

AWS Machine Learning Blog

MARCH 28, 2024

However, when building generative AI applications, you can use an alternative solution that allows for the dynamic incorporation of external knowledge and allows you to control the information used for generation without the need to fine-tune your existing foundational model. license, for use without restrictions. We use an ml.t3.medium

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IBM Journey to AI blog

JUNE 2, 2023

With the power of AI and machine learning, financial institutions can leverage predictive analytics, anomaly detection and shared learning models to enhance system stability, detect fraud and drive superior customer-centric experiences. This shift toward data-driven economies places embedded finance at the core of financial services.

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The MLOps Blog

OCTOBER 3, 2023

In this episode, Mikiko Bazeley shares her learnings from building the ML Platform at Mailchimp. Before that, she was building machine learning platforms at MailChimp. How to transition from data analytics to MLOps engineering Piotr: Miki, you’ve been a data scientist, right? Nice to have you here, Miki.

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ML ML Data Scientist Machine Learning 52

O'Reilly Media

NOVEMBER 28, 2023

A year after the first web servers became available, how many companies had websites or were experimenting with building them? Almost everybody’s played with ChatGPT, Stable Diffusion, GitHub Copilot, or Midjourney. A few have even tried out Bard or Claude, or run LLaMA 1 on their laptop. What’s the reality? Certainly not two-thirds of them.

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