Spark MLlib.
Spark MLlib is Spark's distributed machine learning library, providing algorithms and pipeline abstractions that run across a cluster on data too large to fit on one machine.
Where Spark MLlib fits on a long engagement.
MLlib is justified by data that genuinely does not fit on one machine. Its algorithm selection is narrower than single-node libraries and its implementations are usually less refined, so the distributed version is a trade accepted for scale rather than a better option.
The pipeline abstraction is the more durable contribution. Feature transformations and the model as one fitted object means the same preprocessing applies at training and at scoring, which removes a common source of skew.
What an assigned team does with Spark MLlib.
Sampling is the underused alternative. A representative sample that fits on one machine often trains a model as good as the full dataset would, in a fraction of the time and with access to better libraries.
Testing that before committing to distributed training is basic diligence, and it is the sort of judgement assigned under managed data services alongside the modelling work.
What we use Spark MLlib for.
- Training on data that will not fit Distributed learning where sampling genuinely loses information.
- Pipelines that prevent skew Transformations and model fitted together and applied identically at scoring.
- Sampling tested first Establishing whether the full dataset is needed before accepting the trade.
How Spark MLlib capacity is assigned.
Distributed training is assigned inside data and AI capacity, after establishing that sampling does not suffice.
Tell us what your roadmap needs Spark MLlib for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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