Redshift ML & Generative SQL

Moving data out of the warehouse to train a model, then moving predictions back in, is the most expensive way to use machine learning on warehouse data. Redshift now supports training and inference in SQL, generative SQL for analysts inside the query editor, and direct paths to Amazon Bedrock for generative AI applications. We help you pick the use cases where this pays off, build them with the right guardrails, and keep the bill predictable.

Redshift ML: CREATE MODEL and in-warehouse inference

With Redshift ML you write CREATE MODEL ... FROM (SELECT ...) and Redshift hands the training job to Amazon SageMaker AI, then compiles the resulting model into a SQL function you call in any query. Typical fits are churn and propensity scoring, demand forecasting, anomaly detection on operational metrics, and classification of free-text fields. You can also bring an existing SageMaker endpoint or model artifact and expose it as a function for remote or local inference.

What we do:

• Use-case selection: pick the two or three problems where a model inside SQL beats a separate ML platform, and say no to the rest
• Feature design in dbt or materialized views so training and inference read the same definitions
• Training cost control: Redshift ML bills the underlying SageMaker usage; we set the training budget and max runtime on every model and use SHOW MODEL to audit what was produced
• Evaluation: holdout validation in SQL, with the metrics kept in a table your team can chart
• Retraining cadence and monitoring so a model that drifts gets noticed

Amazon Q generative SQL in Query Editor v2

Query Editor v2 includes Amazon Q generative SQL: analysts describe a question in plain English and get a SQL query against your schema, refined in a chat. It is a real productivity gain for analysts who know the business but not the table layout, and a governance question for everyone else.

We configure it properly: which databases and schemas Amazon Q can see, what metadata and sample queries improve its accuracy, how generated queries are reviewed before they land in dashboards, and how row-level security and masking policies still apply to whatever gets generated. We also train analyst teams on how to phrase questions and how to verify answers.

Feeding Bedrock and RAG pipelines from the warehouse

Generative AI applications need grounded data, and the warehouse is where the grounded data is. We build the pipelines that publish curated, permission-checked datasets from Redshift to the retrieval layer (Bedrock Knowledge Bases, OpenSearch, or a vector store), handle refresh cadence, and make sure no customer data reaches a model endpoint without the same controls it has in the warehouse. Where Redshift is also the target, we use Lambda UDFs to call Bedrock models from SQL for classification, summarization and entity extraction at batch scale, with throttling and cost caps built in.

Guardrails and cost

Every AI engagement we deliver has a written budget, usage limits on the Redshift and SageMaker side, and logging of every model invocation. Generative features are rolled out to a pilot group first and measured against a defined outcome before wider rollout.

Deliverables

• Use-case shortlist with expected value and cost per use case
• Working models, SQL functions and pipelines, with evaluation results
• Query Editor v2 and Amazon Q configuration and analyst enablement session
• Monitoring dashboards for model usage, cost and drift

Related: AWS Ecosystem Development for the SageMaker, Bedrock and Amazon Q services around Redshift.

Talk to us

Tell us what your cluster looks like today and what it costs, and we will come back with a written assessment and a fixed-scope proposal. Contact us or call +1 (726) 227-3497.