Data Modeling Database Webinar 
ODSC - Open Data Science
MARCH 23, 2023
Each month, ODSC has a few insightful webinars that touch on a range of issues that are important in the data science world, from use cases of machine learning models, to new techniques/frameworks, and more. So here’s a summary of a few recent webinars that you’ll want to watch. Watch on-demand here.
Hacker News
MAY 22, 2023
1 But inevitably, starting a new project involves lots of meetings with business stakeholders to hash out initial requirements and canonical data models. For example, you might need the data to be denormalized for performance, or the warehouse data might exist on a physically different system that can’t participate in a database join.
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Pickl AI
SEPTEMBER 20, 2024
Summary: Apache Cassandra and MongoDB are leading NoSQL databases with unique strengths. Introduction In the realm of database management systems, two prominent players have emerged in the NoSQL landscape: Apache Cassandra and MongoDB. Flexible Data Model: Supports a wide variety of data formats and allows for dynamic schema changes.
NOVEMBER 20, 2024
By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries. These steps will guide you through deleting your knowledge base, vector database, AWS Identity and Access Management (IAM) roles, and sample datasets, making sure that you don’t incur unexpected costs.
Pickl AI
JULY 3, 2023
It is the process of converting raw data into relevant and practical knowledge to help evaluate the performance of businesses, discover trends, and make well-informed choices. Data gathering, data integration, data modelling, analysis of information, and data visualization are all part of intelligence for businesses.
Iguazio
JULY 22, 2024
MongoDB for end-to-end AI data management MongoDB Atlas , an integrated suite of data services centered around a multi-cloud NoSQL database, enables developers to unify operational, analytical, and AI data services to streamline building AI-enriched applications.
Iguazio
MAY 29, 2024
In the training pipeline, teams can swap: The model itself, whether a version or a type. For example, based on user input or requirements, teams might switch from a full LLM to a smaller, more specialized model. In the application pipeline, teams can swap: Logging inputs + responses to various data sources (database, stream, file, etc.)
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