As the amount of ingested data increases for companies, different businesses need to convert these ever-increasing sources of data into new insights that help their users in different ways — but there is no one-size-fits-all solution. In this series, we explore you, as a Head of AI, Data Science, VP of Data Science, or similar can think about approaching this problem.
In the wild, we always hear many people say “data is the new oil”. Data is the new oil, but only the data through which insights can be extracted. Recently, the cost of storage and compute has become cheaper…
The goal of this blog is to cover the key topics to consider in operationalizing machine learning and to provide a practical guide for navigating the modern tools available along the way. To that end, the subsequent blogs will include further detailed architecture concepts and help you apply them to your own model pipelines.
This blog series will not explain machine learning concepts but rather to tackle the auxiliary challenges like dealing with large data sets, computational requirements and optimizations, and the deployment of models and data to large software systems.
Software 1.0 vs Software 2.0
Most classical software applications…
Director of Data Science at One Concern, Previously Co-Founder at Datmo, Previously at Stanford AI Lab