Jobs

Delete old log messages from system logs collection.

A versatile job type that runs an arbitrary REST/HTTP/Solr command.

Define an aggregation job.

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

Run a custom Scala script as a Fusion job.

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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.)

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

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

Cluster a set of documents and attach cluster labels.

Evaluate the performance of a Smart Answers pipeline.

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

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.

Train a regularized logistic regression model for text classification.

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

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.

Identify multi-word phrases in signals.

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.

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.

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.

Train a random forest classifier for text classification.

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

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

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.

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

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