Fusion

Version 5.1

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

  • SQL Aggregation job

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

  • 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

    Use this job when you want to compute user recommendations or item similarities using a collaborative filtering recommender. You can also implement a user-to-item recommender in the advanced section of this job’s configuration UI. This job uses SparkML’s Alternating Least Squares (ALS).

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

    The Document Clustering job uses an unsupervised machine learning algorithm to group documents into clusters based on similarities in their content. You can enable more efficient document exploration by using these clusters as facets, high-level summaries or themes, or to recommend other documents from the same cluster. The job can automatically group similar documents in all kinds of content, such as clinical trials, legal documents, book reviews, blogs, scientific papers, and products.

  • Evaluate QnA Pipeline

    Evaluate the performance of a Smart Answers pipeline.

  • Smart Answers

    Fusion Smart Answers brings the benefits of a versatile, scalable Semantic Search platform to provide cutting edge relevancy for your applications. This system makes use of advanced Deep Learning techniques to provide the power of Neural dense vectors search. Semantically rich models encode queries and documents into vectors in the same digital vector space in such way that query and the most relevant information are located near each other. It pushes search boundaries beyond classical token-based matching mechanisms by leveraging the contextual information and query understanding.

  • 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. IMPORTANT: This job is deprecated in Fusion 5.3.x. The Classification job, introduced in Fusion 5.2.0, provides more options and better logging.

  • Outlier Detection

    Use this job when you want to find outliers from a set of documents and attach labels for each outlier group.

  • Parallel Bulk Loader

    The Parallel Bulk Loader (PBL) job enables bulk ingestion of structured and semi-structured data from big data systems, NoSQL databases, and common file formats like Parquet and Avro.

  • Phrase Extraction

    Identify multi-word phrases in signals.

  • QnA Supervised Training

    Train a Smart Answers model on a supervised basis, with pre-trained or trained embeddings, and deploy the trained model to the ML Model Service.

  • QnA Coldstart Training

    Train a Smart Answers model on a cold start (unsupervised) basis, with pre-trained or trained embeddings, and deploy the trained model to the ML Model Service.

  • Query-to-Query Session-Based Similarity

    Train a collaborative filtering matrix decomposition recommender using SparkML’s Alternating Least Squares (ALS) to batch-compute query-query similarities. This can be used for items-for-query recommendations as well as queries-for-query recommendations.

  • Query-to-Query Similarity

    Train a collaborative filtering matrix decomposition recommender using SparkML’s Alternating Least Squares (ALS) to batch-compute query-query similarities. This can be used for items-for-query recommendations as well as queries-for-query recommendations.

  • Random Forest Classifier Training

    Train a random forest classifier for text classification.

  • Ranking Metrics

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

  • Synonym and Similar Queries 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.

This job is deprecated in Fusion 5.2.0.