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Scale Data Science by Pandas API on Spark
Rui Wang is currently a senior software engineer at Databricks in the greater Seattle area who is working on Apache Spark and its ecosystem. Prior to this experience, Rui worked at Google on big data backend (execution engines) and big data frontend (SQL, Java, Python SDKs). Rui has built expertise on the area of big data computing and has been working on projects that help scale data science applications. Rui is also a active open source contributor and has been serving as a committer for Apache Software Foundation projects.
With Python emerging as the primary language for data science, pandas has grown rapidly to become one of the standard data science libraries. One of the known limitations in pandas is that it does not scale with your data volume linearly due to single-machine processing. Pandas API on Spark overcomes the limitation, enabling users to work with large datasets by leveraging Apache Spark. In this talk, I will introduce Pandas API on Spark and help you scale your existing data science workloads using that. Furthermore, I will share the cutting-edge features in Pandas API on Spark.
Using Deep Learning to Discover New Molecules
Rahul Baboota, I am a huge data geek and currently an Applied Scientist at Microsoft. I have been fascinated with the world of data and machine learning since I first learned about it and have been extremely fortunate enough to have worked alongside great people and projects including working at the top NeuroImaging lab in US at my alma matter USC as well as working on developing Drug Discovery Deep Learning models at NVIDIA.
This talk will go into how Deep Learning is changing the world of Cheminformatics. We will dive deep into how we can leverage traditional NLP Transformer models can enable us to performing a totally uncorrelated task such as Drug Discovery. This talk will give a brief introduction to the field of Cheminformatics and then go into detail as to how and what kind of Transformers can be utilized for the task at hand.