Functions, Users, and Comparative Analysis
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We’re super excited to share that Aporia is now the first ML observability offering integration to the Databricks Lakehouse Platform. This partnership means that you can now effortlessly automate your data pipelines, monitor, visualize, and explain your ML models in production. Aporia and Databricks: A Match Made in Data Heaven One key benefit of this […]
Fundamentals of ML observability
Metrics, feature importance and more
We’re excited 😁 to share that Forbes has named Aporia a Next Billion-Dollar Company. This recognition comes on the heels of our recent $25 million Series A funding and is a huge testament that Aporia’s mission and the need for trust in AI are more relevant than ever. We are very proud to be listed […]
NumPy is a library for Python that supports adding large, multidimensional arrays and matrices, along with a wide set of mathematical functions for analyzing array data.
Python has lists that serve as arrays, but they are slow to process. A NumPy array object is designed to be up to 50x faster than traditional Python lists. In NumPy, the array object is called ndarray. It provides many functions that make working with ndarray very easy. Data science is heavily reliant on arrays due to the importance of speed and resources.
Essentially NumPy is known to provide direct python interfaces for accessing and manipulating the data. NumPy is a long-standing industry standard that is well known for its efficiency and power.
Learn how to convert DMatrix to NumPy format for your machine learning model.
