10 MLops platforms to manage the machine learning lifecycle

For most professional software developers_ using application lifecycle treatment ALM is a given. Data scientists_ many of whom do not have a software outgrowth background_ frequently have not<_em> used lifecycle treatment for their machine acquireing measures. Thats a problem thats much easier to fix now than it was a few years ago_ thanks to the approach of “MLops” environments and frameworks that support machine acquireing lifecycle treatment.

Machine acquireing lifecycle treatment methods try to rank and keep track of all your trials over time. In the most advantageous instrumentations_ the treatment method also sums with deployment and advisering.