Join our upcoming webinar
Accelerating XGBoost With Python
Join us for an interactive discussion with Aaron Richter, Senior Data Scientist at Saturn Cloud, and Mike McCarty, Director of Software Engineering at Capital One. We will be discussing all things XGBoost along with packages, methods, tips for accelerating XGBoost performance in Python, and more.
Workshop: Scaling Machine Learning in Python
In this hands-on workshop, you'll have the opportunity to see how a standard data science and machine learning workflow, using pandas and scikit-learn, can easily be parallelized using Dask clusters. Instructors will walk step-by-step through how to migrate existing Python code to Dask, an open-source framework enabling parallelization of Python.
Next-Generation Big Data Pipelines with Prefect and Dask
Data pipelines are crucial to an organization’s data science efforts. They ensure data is collected and organized in a timely and accurate manner, and is made available for analysis and modeling. In this talk, we’ll introduce the next-generation stack for big data pipelines built upon Prefect and Dask, and compare it to popular tools like Spark, Airflow, and the Hadoop ecosystem.
Data & AI Accessibility: The Democratization of Data Science
Join Saturn Cloud's Senior Data Scientist, Aaron Richter, and Travis Oliphant, CEO of OpenTeams and Quansight for an interactive discussion covering: The creation of NumPy and the start of the OSS PyData community and projects, how the data/AI ecosystem has changed over the last 10-20 years, Dask and Numba, how OSS tools will continue to be well-maintained moving forward, and more.
The Future Of High Performance Computing
Meet the engineering leads behind RAPIDS through an interactive discussion and an opportunity to ask questions during the event
100x Faster Compute: Scaling Python For Data Science on AWS
Learn how to run up to 100x faster data science workloads in Python with Dask and RAPIDS and understand the infrastructure that’s necessary to launch high performance clusters and GPU machines in AWS.