article thumbnail

10 Ways to Use Generative AI for Database

Analytics Vidhya

Generative AI for databases will transform how you deal with databases, whether or not you’re a data scientist, […] The post 10 Ways to Use Generative AI for Database appeared first on Analytics Vidhya. Though it appears to dazzle, its true value lies in refreshing the fundamental roots of applications.

Database 317
article thumbnail

Top 10 YouTube videos to learn large language models

Data Science Dojo

Any serious applications of LLMs require an understanding of nuances in how LLMs work, embeddings, vector databases, retrieval augmented generation (RAG), orchestration frameworks, and more. Vector Similarity Search This video explains what vector databases are and how they can be used for vector similarity searches.

Database 370
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

HIVE: INTERNAL AND EXTERNAL TABLES

Analytics Vidhya

INTRODUCTION Hive is one of the most popular data warehouse systems in the industry for data storage, and to store this data Hive uses tables. Tables in the hive are analogous to tables in a relational database management system. Each table belongs to a directory in HDFS. By default, it is /user/hive/warehouse directory.

article thumbnail

Data preprocessing

Dataconomy

By improving data quality, preprocessing facilitates better decision-making and enhances the effectiveness of data mining techniques, ultimately leading to more valuable outcomes. Key techniques in data preprocessing To transform and clean data effectively, several key techniques are employed.

article thumbnail

The ultimate guide to the Machine Learning Model Deployment

Data Science Dojo

The following steps are involved in pipeline development: Gathering data: The first step is to gather the data that will be used to train the model. For data scrapping a variety of sources, such as online databases, sensor data, or social media. This involves removing any errors or inconsistencies in the data.

article thumbnail

How Dataiku and Snowflake Strengthen the Modern Data Stack

phData

This accessible approach to data transformation ensures that teams can work cohesively on data prep tasks without needing extensive programming skills. With our cleaned data from step one, we can now join our vehicle sensor measurements with warranty claim data to explore any correlations using data science.

article thumbnail

Big Data vs. Data Science: Demystifying the Buzzwords

Pickl AI

Key Takeaways Big Data focuses on collecting, storing, and managing massive datasets. Data Science extracts insights and builds predictive models from processed data. Big Data technologies include Hadoop, Spark, and NoSQL databases. Data Science uses Python, R, and machine learning frameworks.