Jobs Configuration Reference

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 this job when you want to compute user recommendations or item similarities using a Bayesian Personalized Ranking (BPR) recommender algorithm.

Use this job to build training data for query classification by joining signals data with catalog data. The output of this job can be used as input for the Classification job.

This job analyzes how your existing documents or signals are categorized and produces a classification model that can be used to predict the categories of new documents or queries.

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 when you want to compute item similarities based on their content, such as product descriptions.

Creates indexes for specified collections in Milvus.

Creates collections with specified parameters in Milvus.

Deletes specified indexes in Milvus.

Deletes specified collections in Milvus.

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.

Compute user recommendations based on a pre-computed item similarity model.

Use this job when you only want to compute item-to-item similarities. This method is more lightweight than the generic Recommendations job.

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.