How DataOps Accelerates Snowpark Deployments for Production AI and ML Workloads
Snowpark is a quantum leap in the ability for people to efficiently and securely push data-intensive workloads into Snowflake. However, as a new technology, what is missing are the tried and tested ways of developing, deploying, testing, and executing Snowpark workloads that impose no additional overheads on developers. This becomes especially true for data engineers who are starting to get into Python development to augment what they can already do.
In this session we will be unveiling a new approach that allows Snowpark (Python) to be developed and deployed in a way that is already familiar to Snowflake customers. This removes virtually all the barriers to adopting Snowpark, and will accelerate the ability of deploying advanced data engineering, machine learning, and advanced analytics workloads into Snowflake. In addition, this approach will natively add capabilities around automated testing, environment management, and repeatable deployment to Snowpark.
In partnership with: