Breakthroughs in artificial intelligence and machine learning have been two of the most exciting topics of the last two decades. Extensive research and hard work are necessary for machine learning and data science engineers to understand and run their models effectively.
While they may differ depending on different individuals, the traditional machine learning steps include:
H2O is, at its core, a platform for distributed, in-memory computing. On top of the distributed computation platform, machine learning algorithms are implemented. At H2O, we design every operation, be it data transformation, training of machine learning models, or even parsing to utilize the distributed computation model. In order to work with big data fast, it’s necessary.
However, a single operation usually can not utilize clusters’ computational resources to the very maximum. Data needs to be distributed across the cluster, and many operations require sequential execution of tasks, which, even if implemented in a distributed manner, follow after each other and require data exchange. These and many other smaller factors, if summed up together, may introduce a significant overhead.
