A Databricks Brickbuilder Solution For Generative AI Excellence
We are thrilled to announce the launch of Konverto AI, a cutting-edge Databricks Generative AI Brickbuilder Solution designed to redefine enterprise efficiency and unlock the transformative potential of Gen AI.
Read MoreKonverto AI augmented by Databricks Compound AI Systems: Simplifying Generative AI for businesses
As enterprises strive to innovate and maintain a competitive edge with generative AI, they encounter various data and business-related challenges. Fragmented workflows and data silos create operational inefficiencies, as data and insights are scattered across departments, limiting seamless collaboration and timely decision-making
Read MoreEnhancing RAG Systems with Fine-Tuned Language Models on Databricks
In today's competitive business environment, delivering exceptional customer experiences is essential. Unfortunately, long hold times, generic responses, and a lack of personalization can undermine customer satisfaction. To address these challenges, we've embarked on a journey to enhance our customer support systems using advanced language models.
Read MoreJoin the Databricks Rebellion: The Significance of Databricks Explained!
In this blog post, we'll explore why Databricks migration holds immense importance in today's ever-evolving data landscape. So, get ready for a data-driven revolution as we unveil why businesses choose Databricks as their preferred tool.
Read MoreChapter 1: Notebooks vs Worksheets
Celebal Technologies is a leading software services company specializing in Data Science, Big Data, and Enterprise Cloud solutions. Through our work at Celebal and our extensive industry experience, we enable customers to unleash their full potential in their digital transformation initiatives and turn their aspirations into reality.
Read MoreChapter 2: Databricks ML Runtime vs Snowpark ML
We initiated the comparison between DBR ML runtime and Snowpark ML by employing an openly accessible dataset from Kaggle. The data setup process will mirror the steps elucidated in the preceding chapter, including the exploratory data analysis (EDA) procedures. We would try to cover all possible aspects of a production-ready machine-learning pipeline.
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