- Published on March 17, 2019
- In AI Features
Can Data Lakes Solve Machine Learning Workload Challenges?
- By Harshajit Sarmah
Year after year, the field of ML is progressing at break-neck speed, and new algorithms and techniques are entering the space at a high frequency. Also, machine learning workloads are becoming increasingly more prevalent. However, there are significant challenges in democratizing machine learning and reliably scaling and deploying ML workloads.
In this article, we will have a look at some of the ML workload challenges and how data lakes can help overcome them.
Challenges In ML Workloads
Data Collection
ML workloads typically benefit from data — the more data is put into these workloads the better they become. So in order to make the most of the ML workloads, organisations across the world are looking for ways to collect data. However, the cost data collection and storage has to be lo
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Harshajit Sarmah
Harshajit is a writer / blogger / vlogger. A passionate music lover whose talents range from dance to video making to cooking. Football runs in his blood. Like literally!
He is also a self-proclaimed technician and likes repairing and fixing stuff. When he is not writing or making videos, you can find him reading books/blogs or watching videos that motivate him or teaches him new things.
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