What I Learned After One Year of Building a Data Platform From Scratch
The cornerstone of a successful RAG implementation is the quality of your data
DataStax • Retrieval Augmented Generation (RAG) Explained: Understanding Key Concepts
Some organizations already use it as a first “preprocessing” step for data transformation, exploration, and discovery of patterns and trends, though there are other possibilities as well for this discovery platform, such as the Teradata Aster appliance.
Thomas H. Davenport • Big Data at Work: Dispelling the Myths, Uncovering the Opportunities
However development time, and maintenance can offset these savings. Hiring skilled data scientists, machine learning engineers, and DevOps professionals can be expensive and time consuming. Using available resources for “reimplementing” solutions hinder innovation and lead to a lack of focus. Since You not longer work on improving your model or pro... See more
Understanding the Cost of Generative AI Models in Production
Traditional ETL solutions are still quite powerful when it comes to:
- Common connectors with small-medium data volumes : we still have a lot of respect for companies like Fivetran, who have really nailed the user experience for the most common ETL use cases, like syncing Zendesk tickets or a production Postgres read replica into Snowflake. The only