Data-driven businesses are always looking for new ways to manipulate and query data, and Databricks is leading the pack in aiding their efforts. The California-based company has recently announced two updates which are designed to make it easier for enterprises to utilize SQL (structured query language) on large language models (LLMs) with MLflow 2.3. MLflow is a Databricks-led open source effort that simplifies the life cycle of many machine-learning (ML) projects, from experimentation to deployment.
SQL is a well-known query language used for data analysis, but it hasn’t been easy to employ SQL with ML projects in the past. Thankfully, Databricks is bringing SQL and ML closer together with its latest update, making it possible to execute sentiment analysis, text summarization, and other common tasks with SQL. Additionally, MLflow 2.3 extends support for transformer-based models hosted on Hugging Face, allowing users to easily package and recover models from their data-lakehouse platform.
The company is also known for Dolly ChatGPT, its open-source project that makes creating data-driven applications such as chatbots easier. With the help of MLflow 2.3, it’s now easier to manage and deploy ML models, which should pave the way for more businesses to benefit from AI. Databricks cofounder and VP of engineering Patrick Wendell says the company’s objective is to do “interesting things” with enterprise data, and their latest endeavor should certainly help.
At its core, Databricks is focused on managing data and making it easier to do some analytical processing and predictive modeling with your dataset. With their latest updates, they are looking to bridge the gap between enterprises data and ML models, giving businesses an opportunity to benefit from AI. Databricks is an important player in the data science sector, and they are working hard to continue democratizing AI and giving enterprises the resources they need to be successful.
