I found it useful that Ray is capable of scaling machine learning projects across several machines, which has been vital for Ray’s utility in my study. Distributing work and using parallelism effectively saves a lot of time and enables me to work with more complicated models. Review collected by and hosted on G2.com.

Tracking mistakes in the code that executes in a number of computers can be a cumbersome affair. This has hampered my capacity to effectively and rapidly prototype with my machine learning endeavors. Review collected by and hosted on G2.com.