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      Jobs

      Table of Contents

      These reference topics provide complete information about the configuration properties of jobs for which the subtype is "task" or "spark".

      Jobs with the subtype "datasource" have configuration schemas that depend on the connector type; see Connectors Configuration Reference.

      For conceptual information and instructions for configuring and scheduling jobs, see Jobs and Schedules.

      Tasks

      Spark jobs

      • Aggregation

        Define an aggregation job.

      • Custom Python job.

        The Custom Python job provides user the ability to run Python code via Fusion. This job supports Python 3.6+ code.

      • Script

        Run a custom Scala script as a Fusion job.

      • ALS Recommender

        Unresolved directive in <stdin> - include::/fusion/reference/config-ref/jobs/als-recommender.asciidoc[tag=intro]

      Use the BPR Recommender instead.
      • Cluster Labeling

        Use this job when you already have clusters or well-defined document categories, and you want to discover and attach keywords to see representative words within those existing clusters. (If you want to create new clusters, use the Document Clustering job.)

      • Create Seldon Core Model Deployment Job

        Use this job to deploy a Seldon Core Model into the Fusion cluster.

      • Delete Seldon Core Model Deployment Job

        Use this job to remove a Seldon Core deployment from the Fusion cluster.

      • Document Clustering

        Cluster a set of documents and attach cluster labels.

      • Evaluate QnA Pipeline

        Evaluate the performance of a Smart Answers pipeline.

      • Ground Truth

        Estimate ground truth queries using click signals and query signals, with document relevance per query determined using a click/skip formula.

      • Head/Tail Analysis

        Perform head/tail analysis of queries from collections of raw or aggregated signals, to identify underperforming queries and the reasons. This information is valuable for improving overall conversions, Solr configurations, auto-suggest, product catalogs, and SEO/SEM strategies, in order to improve conversion rates.

      • Logistic Regression Classifier Training

        Train a regularized logistic regression model for text classification.

      The Classification job, introduced in Fusion 5.2.0, provides more options and better logging.
      The Classification job, introduced in Fusion 5.2.0, provides more options and better logging.
      The Classification job, introduced in Fusion 5.2.0, provides more options and better logging.
      • Ranking Metrics

        Calculate relevance metrics (nDCG and so on) by replaying ground truth queries against catalog data using variants from an experiment.

      • SQL Aggregation

        A Spark SQL aggregation job where user-defined parameters are injected into a built-in SQL template at runtime.

      • Synonym Detection Jobs

        Use this job to generate pairs of synonyms and pairs of similar queries. Two words are considered potential synonyms when they are used in a similar context in similar queries.

      • Token and Phrase Spell Correction

        Detect misspellings in queries or documents using the numbers of occurrences of words and phrases.

      • Word2Vec Model Training

        Train a shallow neural model, and project each document onto this vector embedding space.

      Word2Vec Model Training job is deprecated as of Fusion 5.2.0.