Running data science workloads is a challenge regardless of whether you are running them on your laptop, on an on-premises cluster, or in the cloud. While buying 100% managed service is an option, these tools can be expensive and lack extensibility. Therefore, many companies opt for open source data science tools like scikit-learn and Apache Spark’s MLlib in order to balance both functionality and cost.
However, even if a project succeeds at a point in time with any set of tools, these projects become harder and harder to maintain as data volumes increase and a desire for real-time pushes technology to its limit. New projects also struggle as new challenges of scale invalidate previous assumptions.
In this latest Data Science Central Webinar, we will discuss some patterns we see that companies leverage to succeed with their data science projects.
Key takeaways will be:
Strategies for removing cognitive load for you and your team
How to execute a program that is simple and effective
How to best use the ecosystem of tools to be successful
Bill Chambers, Data Scientist - Databricks
Rafael Knuth, Contributing Editor - Data Science Central