Search Results for "model train design"

...With SageMaker Spark you construct Spark ML Pipelines using Amazon SageMaker stages. These pipelines interleave native Spark ML stages and stages that interact with SageMaker training and model hosting. With SageMaker Spark, you can train on Amazon SageMaker from Spark DataFrames using Amazon-provided ML algorithms like K-Means clustering or XGBoost, and make predictions on DataFrames against SageMaker endpoints hosting your trained models, and, if you have your own ML algorithms built into SageMaker compatible Docker containers, you can use SageMaker Spark to train and infer on DataFrames with your own algorithms -- all at Spark scale. ...

SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. SynapseML builds on Apache Spark and SparkML to enable new kinds of machine learning, analytics, and model deployment workflows. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit,...

...It implements classic abstractions such as Functor, Applicative, Monad, Monoid, Foldable, and Traverse, along with powerful transformers (ReaderT, StateT, WriterT, OptionT, and more) to structure effects. The library offers rich data structures—\/ (disjunction), Validation, NonEmptyList, IList, and Free—that help model errors, invariants, and interpretable programs. Its type class–oriented design lets you write generic algorithms over capabilities rather than concrete types, improving reuse and testability. Scalaz also contributes optics, equality/ordering abstractions, and lawful instances with property-based tests to ensure algebraic laws hold. ...