Optimize Your Machine Learning Development And Serving With The Open Source Vector Database Milvus - Episode 313

The optimal format for storage and retrieval of data is dependent on how it is going to be used. For analytical systems there are decades of investment in data warehouses and various modeling techniques. For machine learning applications relational models require additional processing to be directly useful, which is why there has been a growth in the use of vector databases. These platforms store direct representations of the vector embeddings that machine learning models rely on for computing relevant predictions so that there is no additional processing required to go from input data to inference output. In this episode Frank Liu explains how the open source Milvus vector database is implemented to speed up machine learning development cycles, how to think about proper storage and scaling of these vectors, and how data engineering and machine learning teams can collaborate on the creation and maintenance of these data sets.

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Interactive Exploratory Data Analysis On Petabyte Scale Data Sets With Arkouda - Episode 311

Exploratory data analysis works best when the feedback loop is fast and iterative. This is easy to achieve when you are working on small datasets, but as they scale up beyond what can fit on a single machine those short iterations quickly become long and tedious. The Arkouda project is a Python interface built on top of the Chapel compiler to bring back those interactive speeds for exploratory analysis on horizontally scalable compute that parallelizes operations on large volumes of data. In this episode David Bader explains how the framework operates, the algorithms that are built into it to support complex analyses, and how you can start using it today.

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