Three challenges that get in the way of solving data science problems
Silos: Data Science grows organically in most organizations. This means fragmented infrastructure and disconnected teams that often have no visibility or understanding into what other data scientists are working on inside the business. You end up wasting time duplicating efforts and dealing with inefficient processes. The same thing happens in every team across the organization.
Friction in the data science lifecycle: You need the freedom and flexibility to explore, experiment, and ultimately solve your companies biggest challenges. You shouldn't be spending more time on technical problems related to consistent access to data, compute and production equipment than on solving the business challenge at hand.
Chaotic infrastructure: When you have a wild west of data science tools and infrastructure you are forced to do DevOps work for much of your day. It is impossible to collaborate with others or quickly get models into production. And, when new packages and libraries become available, its a struggle to get them integrated into your analytic infrastructure.
Introducing Domino 5.0
The latest release of our Enterprise MLOps platform accelerates the end-to-end data science lifecycle – unleashing model velocity to grow revenue, improve the customer experience, and outcompete your peers.Learn More about 5.0
Resources for data scientists
Beyond Spark: Dask and Ray as Multi-node Accelerated Compute Frameworks
We explore the capabilities of Spark, Ray & Dask in the context of GPU-accelerated computing.
Increasing Model Velocity for Complex Models
Model Monitoring Best Practices