Provenance Tracking for End-to-End Machine Learning Pipelines

Reinforcement Learning (RL) algorithms seek to maximize some notion of reward. There are two categories of RL agents, model-based or model-free agents. In the case of model-free learning, the algorithm learns through trial and error in the target ...

Reinforcement Learning (RL) is a general framework concerned with an agent that seeks to maximize rewards in an environment. The learning typically happens through trial and error using explorative methods, such as $ϵ$-greedy. There are two ...

End-to-end machine learning pipelines that run in shared environments are challenging to implement. Production pipelines typically consist of multiple interdependent processing stages. Between stages, the intermediate results are persisted to reduce ...