# Chalk

> Chalk is the AI/ML data platform: define features and models in Python, serve them fresh at inference time, and run the whole thing on infrastructure you control.

## Introduction

- [What is Chalk?](https://docs.chalk.ai/docs/what-is-chalk): Chalk is the AI/ML data platform, serving fresh contextual data to your models and agents and running them in your own cloud.
- [Platform Architecture](https://docs.chalk.ai/docs/architecture): How it all fits together.

## Feature Engine

- [Installation](https://docs.chalk.ai/docs/setup-guide): Install the Chalk CLI, create a project, connect your data sources, and define, deploy, and query your first features.
- [File Structure](https://docs.chalk.ai/docs/configuration): Configure your Chalk project and organize your features and resolvers.
- [Best Practices](https://docs.chalk.ai/docs/best-practices): Learn our best practices for building and maintaining your Chalk solution
- [Common Errors](https://docs.chalk.ai/docs/debugging-errors): Learn techniques for debugging different elements of Chalk.
- [Debugging Queries](https://docs.chalk.ai/docs/debugging-queries): Learn techniques for debugging online and offline queries
- [Development with LLMs](https://docs.chalk.ai/docs/development-with-llms): Write Chalk code with LLMs using Chalk's agent prompts and skills.
- [Frequently Asked Questions](https://docs.chalk.ai/docs/faq): A collection of questions frequently asked by customers
- [Features Overview](https://docs.chalk.ai/docs/features): Define features for training and inference.
- [Feature Types](https://docs.chalk.ai/docs/feature-types): Define features for training and inference.
- [Discovery](https://docs.chalk.ai/docs/feature-discovery): Declare owners and metadata for features.
- [Feature Caching](https://docs.chalk.ai/docs/feature-caching): Cache pre-computed feature values in the online store
- [Has One](https://docs.chalk.ai/docs/has-one): Define one-to-one relationships between feature classes.
- [Has Many](https://docs.chalk.ai/docs/has-many): Define one-to-many and many-to-many relationships between feature classes.
- [Composite Keys](https://docs.chalk.ai/docs/composite-keys): Model composite identifiers and joins in Chalk, including filter pushdown, SQL resolver outputs, and materialized aggregations.
- [DataFrame](https://docs.chalk.ai/docs/dataframe): Describe and fetch rows of features.
- [Validation](https://docs.chalk.ai/docs/validation): Validate features and resolvers
- [Versioning](https://docs.chalk.ai/docs/feature-versions): Rigorously update your features as their meaning changes.
- [Embeddings](https://docs.chalk.ai/docs/embeddings): Automatically calculate embeddings from existing features
- [Vector Search](https://docs.chalk.ai/docs/vector-search): Find the nearest neighbors across a vector relationship
- [Named Prompts](https://docs.chalk.ai/docs/prompts): Define templated LLM interactions as microservices
- [Windowed Aggregations](https://docs.chalk.ai/docs/aggregations): Define features as aggregations of data over sliding time ranges.
- [Materialized Windowed Aggregations](https://docs.chalk.ai/docs/materialized_aggregations): Cache and materialize feature aggregations
- [Training Sets with Materialized Aggregations](https://docs.chalk.ai/docs/training-sets-materialized-aggregations): Leverage pre-materialized aggregate tiles to efficiently build training sets for windowed features.
- [Versioned Windowed Aggregations](https://docs.chalk.ai/docs/versioned-windowed-aggregations): Define multiple versions of a windowed aggregation with independent expressions, time windows, and materialization configs.
- [Streaming with Materialized Aggregations](https://docs.chalk.ai/docs/windowed-streaming): Computing aggregate functions on streams.
- [Stream Resolvers](https://docs.chalk.ai/docs/streams): Define streaming resolvers with Chalk expressions.
- [Stream Sources](https://docs.chalk.ai/docs/stream-sources): Consuming Kafka streams.
- [Testing Stream Resolvers](https://docs.chalk.ai/docs/streaming-testing): Locally verify stream resolver parsers and end-to-end scenarios.
- [Webhook Sources](https://docs.chalk.ai/docs/webhook-sources): Handling webhooks with Chalk.
- [Resolvers Overview](https://docs.chalk.ai/docs/resolver-overview): Create resolvers to compute feature values.
- [Online / Offline](https://docs.chalk.ai/docs/resolver-online-offline): Eliminate train-serve skew with shared logic.
- [SQL Resolvers](https://docs.chalk.ai/docs/sql-resolvers): Define feature resolvers as a SQL query.
- [Expressions](https://docs.chalk.ai/docs/expression): Using Chalk expressions to define features
- [Python Resolvers](https://docs.chalk.ai/docs/python-resolvers): Specify dependencies and results of feature resolvers.
- [Environments](https://docs.chalk.ai/docs/resolver-environments): Change resolver behavior by deployment.
- [Routing tags](https://docs.chalk.ai/docs/resolver-tags): Select eligible resolvers and SQL source routes within an environment.
- [Errors](https://docs.chalk.ai/docs/resolver-errors): Surface and handle failed feature values.
- [Scheduling](https://docs.chalk.ai/docs/resolver-cron): Automate resolver runs.
- [Reverse ETL](https://docs.chalk.ai/docs/reverse-etl): Move data from offline to online.
- [Triggered Runs](https://docs.chalk.ai/docs/runs): Execute resolver runs via API
- [Timeout](https://docs.chalk.ai/docs/timeout): Maximum execution time for resolvers
- [Integrations Overview](https://docs.chalk.ai/docs/integrations): Integrate any API, 3rd-party client or data source without needing to orchestrate data pipelines
- [Configuring Data Sources](https://docs.chalk.ai/docs/configuring-data-sources): Configure connections between Chalk and your data sources using the Chalk dashboard.
- [Docker](https://docs.chalk.ai/docs/docker): Customize the base image for your pipelines.
- [Amazon Web Services](https://docs.chalk.ai/docs/aws): Setup your AWS integration
- [S3 / Object Storage](https://docs.chalk.ai/docs/s3): Integrate with S3 compatible data sources.
- [Google Cloud Platform](https://docs.chalk.ai/docs/gcp)
- [SQL Integration](https://docs.chalk.ai/docs/sql): Integrate with SQL-like sources.
- [Custom Data Source](https://docs.chalk.ai/docs/generic): Integrate with any data source you use.
- [Datasource Management API](https://docs.chalk.ai/docs/datasource-api): Programmatically manage data sources using the Python SDK
- [Test Integrations Locally](https://docs.chalk.ai/docs/local-development): Execute resolvers that connect to external systems
- [Secrets and Engine Configuration Variables](https://docs.chalk.ai/docs/env-vars): Manage secrets and engine configuration variables for your Chalk deployments.
- [Unit Tests](https://docs.chalk.ai/docs/unit-tests): Unit tests for Chalk resolvers
- [Integration Tests](https://docs.chalk.ai/docs/integration-tests): Integration tests for Chalk resolvers
- [Snapshot Tests](https://docs.chalk.ai/docs/snapshot-tests): Snapshot testing with Chalk
- [Time Overview](https://docs.chalk.ai/docs/time): Manage feature timestamps and point-in-time query timestamps.
- [Temporal Consistency](https://docs.chalk.ai/docs/temporal-consistency): Point-in-time queries for training data sets.
- [Backfills](https://docs.chalk.ai/docs/backfilling-data): Chalk makes it easy to batch ingest historical feature data from datasets and bulk data sources.

## Deployment

- [Feature Development Lifecycle](https://docs.chalk.ai/docs/lifecycle): Developing, testing, deploying, and backfilling a new feature
- [Branches](https://docs.chalk.ai/docs/branches): Make rapid changes and explore features with branches.
- [Python Acceleration](https://docs.chalk.ai/docs/static-resolver-optimization): Accelerate Python resolvers using Chalk's symbolic interpreter for columnar execution.
- [Tasks](https://docs.chalk.ai/docs/tasks): Run arbitrary Python scripts in your Chalk deployment
- [GitHub Actions](https://docs.chalk.ai/docs/github-actions): Deploy feature pipelines in GitHub Actions
- [GitLab CI/CD](https://docs.chalk.ai/docs/gitlab-cicd): Deploy feature pipelines in GitLab CI/CD

## Queries

- [Queries Overview](https://docs.chalk.ai/docs/query-overview): Fetch feature values via queries.
- [Online Queries](https://docs.chalk.ai/docs/query-online): Fetch feature values via online queries
- [Uploading Features](https://docs.chalk.ai/docs/upload-features): Write feature values directly to Chalk's online store.
- [Scheduled Query](https://docs.chalk.ai/docs/scheduled-query): Run feature pipelines on a schedule
- [Named Queries](https://docs.chalk.ai/docs/named-query): Track and manage named queries in Chalk
- [Authentication](https://docs.chalk.ai/docs/online-authentication): Access the Chalk API.
- [Query Caching](https://docs.chalk.ai/docs/query-caching): Override default max-staleness for individual requests.
- [Offline Queries](https://docs.chalk.ai/docs/query-offline): Fetch offline feature values.
- [Datasets](https://docs.chalk.ai/docs/datasets): Persist and evolve offline queries over time
- [Pytorch Integration with Datasets](https://docs.chalk.ai/docs/pytorch-datasets): Create PyTorch Datasets from Chalk Datasets
- [Metaplanning](https://docs.chalk.ai/docs/metaplanning): Automated offline query sharding
- [SQL Interface](https://docs.chalk.ai/docs/sql-interface): Querying Chalk with SQL.
- [Chalk Clients](https://docs.chalk.ai/docs/query-basics): Fetch feature values via online query.

## Model Platform

- [Model Registry](https://docs.chalk.ai/docs/model_registry): Learn how to track, version, and run ML models in Chalk
- [Model Deployments](https://docs.chalk.ai/docs/model_deployments): Learn how to deploy and manage machine learning models in Chalk
- [Model Training](https://docs.chalk.ai/docs/model_training): Learn how to train ML models in Chalk
- [Model Gateway](https://docs.chalk.ai/docs/model-gateway): Route LLM traffic to multiple providers through one OpenAI-compatible router.
- [MCP Gateway](https://docs.chalk.ai/docs/mcp-gateway): Register, secure, and govern the external MCP servers your agents connect to.
- [LLM Toolchain](https://docs.chalk.ai/docs/chalk-for-ai-engineers): Easily integrate unstructured data, build context-aware prompts, and run LLM evaluations at scale with Chalk.

## Chalk Notebooks

- [Overview](https://docs.chalk.ai/docs/chalk-notebooks): Mix and match SQL, Python, and Markdown cells to explore, analyze and productionize features.
- [Python cells](https://docs.chalk.ai/docs/notebook-python): Run Python, query Chalk features, and install packages in a notebook.
- [SQL cells](https://docs.chalk.ai/docs/notebook-sql): Query Chalk features, configured datasources, and notebook DataFrames with SQL.
- [Text and Markdown cells](https://docs.chalk.ai/docs/notebook-text): Write notebook narrative as plain text or Markdown and interpolate values from other cells.
- [External Notebooks](https://docs.chalk.ai/docs/notebook-development): Use Chalk from the external notebook of your choice.
- [Authentication for External Notebooks](https://docs.chalk.ai/docs/notebook-setup): Set up Chalk in the external notebook of your choice.

## Developer Interface

- [Getting Started](https://docs.chalk.ai/docs/chalkdf/getting-started): Install and run your first chalkdf commands
- [Installation](https://docs.chalk.ai/docs/chalkdf/installation): Install chalkdf to access DataFrame utilities.
- [What is Chalk SQL](https://docs.chalk.ai/docs/chalksql/what-is-chalk-sql): A SQL dialect for federated queries across everything in a Chalk environment.
- [Example Workflows](https://docs.chalk.ai/docs/chalksql/example-workflows): Common ways to run Chalk SQL queries, from introspecting datasets to joining across sources.
- [SQL Explorer](https://docs.chalk.ai/docs/chalksql/sql-explorer): Chalk's built-in console for Chalk SQL
- [SQL as Input](https://docs.chalk.ai/docs/chalksql/sql-as-input): Compute the inputs to a bulk online or offline query dynamically with Chalk SQL.
- [Chalk Catalog Components](https://docs.chalk.ai/docs/chalksql/chalk-catalog-components): How features, resolvers, datasets, and other Chalk concepts are exposed to Chalk SQL.
- [Chalk Assistant](https://docs.chalk.ai/docs/chalk-assistant): An in-dashboard AI assistant that runs on your AI provider.
- [MCP Server](https://docs.chalk.ai/docs/mcp-server): Connect AI agents to your Chalk deployment over the Model Context Protocol.

## Observability

- [Observability Overview](https://docs.chalk.ai/docs/observability): Monitor the execution of your feature pipelines & alert you when problems arise.
- [Dashboards](https://docs.chalk.ai/docs/dashboards): Combine metrics, logs, and events from your feature pipelines into a single custom view.
- [Monitors](https://docs.chalk.ai/docs/overview/monitors): Continuously evaluate metrics, platform health, or log queries, and open incidents when thresholds are met.
- [Metrics Monitor](https://docs.chalk.ai/docs/metricmonitor): Define and monitor metrics
- [Charts with Code](https://docs.chalk.ai/docs/charts): Define charts and alerts with code.
- [Feature Drift](https://docs.chalk.ai/docs/featuredrift): Detect and setup alerting for drift in feature values
- [Metrics Export](https://docs.chalk.ai/docs/metricexport): Export Chalk metrics to other monitoring systems.
- [Alert Configuration](https://docs.chalk.ai/docs/alertconfig): Configure Alerts
- [Log Export](https://docs.chalk.ai/docs/logexport): Export Chalk logs to other monitoring systems.
- [Search Syntax](https://docs.chalk.ai/docs/log-search-syntax): Search Chalk logs and similar pages (access logs, kube events, traces, and spans) with text terms, fields, Boolean operators, wildcards, and grouped values.
- [Search Aggregations](https://docs.chalk.ai/docs/search-aggregations): Summarize search results from logs, access logs, kube events, and spans with counts, percentiles, and other measures.
- [Tracing](https://docs.chalk.ai/docs/tracing): Use tracing to debug and optimize query performance.
- [Telemetry FAQ](https://docs.chalk.ai/docs/telemetry-faq): Troubleshoot missing logs and traces, and other telemetry pipeline issues.
- [Introspection](https://docs.chalk.ai/docs/introspection): View metadata about your deployed Chalk features and resolvers.

## Infrastructure

- [Choosing a store](https://docs.chalk.ai/docs/choosing-online-offline-stores): Selecting an online and offline store for your environment
- [Capacity](https://docs.chalk.ai/docs/online-store-capacity): Understand how your online store fills up, how eviction affects Chalk, and how to add headroom.
- [Survey](https://docs.chalk.ai/docs/online-store-survey): Use SQL Explorer to analyze online store key distribution and memory utilization
- [Overview](https://docs.chalk.ai/docs/offline-stores): Historical feature storage and retrieval.
- [Snowflake](https://docs.chalk.ai/docs/snowflake-offline-store): Understanding Snowflake offline store architecture and multi-environment deployments.
- [Private EKS API Server Connectivity](https://docs.chalk.ai/docs/private-eks-api-server): How Chalk reaches a fully private EKS API server from the metadata plane.
- [Resource Configuration](https://docs.chalk.ai/docs/resource-configuration): Configure your Chalk Kubernetes cluster
- [Chalk Machine Types](https://docs.chalk.ai/docs/chalk-machine-types): Configure your Chalk Kubernetes cluster
- [How `chalk apply` Works](https://docs.chalk.ai/docs/chalk-apply): How `chalk apply` packages a Chalk project, builds container images, and rolls them out into the Data Plane.
- [Building Images Without Deploying](https://docs.chalk.ai/docs/chalk-deploy-build): Build a Chalk engine image and push it to a registry you own, without creating a deployment — and mirror it into a separate cloud partition such as AWS GovCloud.
- [GitHub App Setup](https://docs.chalk.ai/docs/github-app-setup): Configure a GitHub App for a self-hosted Chalk metadata plane.
- [Blue-Green Deployments](https://docs.chalk.ai/docs/blue-green-deployment): Set up blue-green deployments for reliability.
- [Multi-Region Failover](https://docs.chalk.ai/docs/multi-region-failover): Configure multi-region failover for high availability on AWS.
- [Artifact Repository](https://docs.chalk.ai/docs/artifact-repositories): Configure private Python package indexes (AWS CodeArtifact, Google Artifact Registry, preauthenticated) for Chalk deployments.
- [Debugging Persistence](https://docs.chalk.ai/docs/debug-persistence): Learn how to debug and troubleshoot feature persistence issues in Chalk
- [Metadata Plane & Data Plane Communication](https://docs.chalk.ai/docs/metadata-plane-data-plane-communication): What data flows between the Chalk Metadata Plane and Data Plane, and what the security implications are.
- [Performance Profiling](https://docs.chalk.ai/docs/profiling): Collect CPU profiles and system traces from engine nodes and upload them to cloud storage for performance analysis.

## Compute

- [Overview](https://docs.chalk.ai/docs/compute/overview): Run sandboxes, agents, and inference workloads on managed or self-hosted infrastructure.
- [Compute Setup](https://docs.chalk.ai/docs/compute/compute-setup): Configure a Chalk cluster and environment to enable Compute.
- [Sandbox](https://docs.chalk.ai/docs/compute/sandbox): Architecture, isolation model, and security controls for Chalk compute sandboxes.
- [Scaling Groups](https://docs.chalk.ai/docs/compute/scaling-groups): Deploy autoscaling HTTP services with routing.
- [Images](https://docs.chalk.ai/docs/compute/images): Build and cache container images for compute workloads.
- [Volumes](https://docs.chalk.ai/docs/compute/volumes): Persistent, versioned file storage for compute workloads.
- [Secrets](https://docs.chalk.ai/docs/compute/secrets): Inject secrets into sandboxes, functions, and scaling groups without exposing them in source or images.
- [Functions](https://docs.chalk.ai/docs/compute/functions): Define and call remote functions that run on Chalk Compute.
- [Evaluations](https://docs.chalk.ai/docs/compute/evaluations): Evaluate any LLM workload against historical production data and compare outcomes based on criteria you define.
- [Host Pools](https://docs.chalk.ai/docs/compute/host-pools): Configure the hosts that back Chalk sandboxes, and size them so sandbox workloads can be scheduled.
- [Deep Coding Agents](https://docs.chalk.ai/docs/compute/deep-coding-agents): Run coding agents like OpenCode in persistent Chalk scaling groups.
- [LangChain Agents](https://docs.chalk.ai/docs/compute/langchain-agents): Deploy LangChain agents as Functions with persistent memory.
- [Model Inference](https://docs.chalk.ai/docs/compute/model-inference): Deploy open-weight models like Gemma 4 with vLLM on Chalk Compute.
- [Fan-Out Inference](https://docs.chalk.ai/docs/compute/fan-out-inference): Orchestrate parallel embedding jobs across worker functions with shared volumes.
- [Batched Embeddings](https://docs.chalk.ai/docs/compute/batched-embeddings): Batch and parallelize embedding calls across a DataFrame with F.catalog_call().
- [RAG Pipeline](https://docs.chalk.ai/docs/compute/rag-pipeline): Build a retrieval-augmented generation function that embeds a query and searches OpenSearch.
- [Kafka Consumer](https://docs.chalk.ai/docs/compute/kafka-consumer): Subscribe to a Kafka topic from a sandbox and fan out processing to remote functions.
- [OCR Pipeline](https://docs.chalk.ai/docs/compute/ocr-pipeline): Process images received via SMS by running OCR in a remote function.
- [SQLite Persistence](https://docs.chalk.ai/docs/compute/sqlite-persistence): Write a SQLite database in one sandbox and read it from others using a shared volume.
- [Webhook Orchestrator](https://docs.chalk.ai/docs/compute/webhook-agent-orchestrator): Build a scaling group that listens for webhooks, spawns coding agents, and tracks state in SQLite.
- [Open-Weight Models](https://docs.chalk.ai/docs/compute/running-your-own-models): Run Gemma with vLLM on your own GPU and get an authenticated OpenAI-compatible endpoint.
- [Dynamic Chalk Queries](https://docs.chalk.ai/docs/compute/dynamic-chalk-queries): Let an agent write and run Chalk queries on demand, fetching exactly the features it reasons it needs.
- [Tunnels](https://docs.chalk.ai/docs/tunnels): Reach a TCP service inside a Chalk container from your machine — ssh, scp, port forwarding, and remote IDEs.

## Tutorials

- [Fraud Detection Pipeline](https://docs.chalk.ai/docs/fraud-tutorial): Build a feature pipeline for fraud detection.
- [Feature Versions](https://docs.chalk.ai/docs/feature-version-tutorial): Learn how to create and interact with versioned features
- [Jupyter Notebooks](https://docs.chalk.ai/docs/notebook-tutorial): Work through an example using Chalk in a Jupyter notebook
- [AWS SageMaker](https://docs.chalk.ai/docs/sagemaker-tutorial): Build a Chalk feature pipeline for training and serving models with AWS SageMaker.
- [Model Development](https://docs.chalk.ai/docs/model-tutorial): Build a complete ML pipeline from features to inference
- [ChalkDF Static Resolvers](https://docs.chalk.ai/docs/chalkdf/static-resolvers): Build vectorized batch feature computations using chalkdf and static resolvers

## References

- [References Home](https://docs.chalk.ai/references)
- [Python SDK](https://docs.chalk.ai/api-docs)
- [Compute SDK](https://docs.chalk.ai/compute-docs)
- [CLI](https://docs.chalk.ai/cli)
- [DataFrame](https://docs.chalk.ai/dataframe-docs)
- [Overview](https://docs.chalk.ai/docs/chalksql/statements/overview): The SQL statements supported by Chalk SQL.
- [SELECT](https://docs.chalk.ai/docs/chalksql/statements/select): Retrieve rows from tables across your Chalk environment.
- [DESCRIBE & SHOW](https://docs.chalk.ai/docs/chalksql/statements/describe): Inspect the schema of a table or query, or list available tables.
- [EXPLAIN](https://docs.chalk.ai/docs/chalksql/statements/explain): Show the physical plan for a statement, optionally with execution statistics.
- [COPY TO](https://docs.chalk.ai/docs/chalksql/statements/copy): Write the result of a query to Parquet, CSV, or Iceberg files.
- [Function Reference](https://docs.chalk.ai/chalksql-docs)
- [TypeScript](https://docs.chalk.ai/docs/client-typescript): Query features with Chalk's TypeScript client library
- [Python](https://docs.chalk.ai/docs/client-python): Query features with Chalk's Python client library
- [Go](https://docs.chalk.ai/docs/client-go): Query features with Chalk's Go client library
- [Rust](https://docs.chalk.ai/docs/client-rust): Query features with Chalk's Rust client library
- [Java](https://docs.chalk.ai/docs/client-java): Query features with Chalk's Java client library
- [C#](https://docs.chalk.ai/docs/client-csharp): Query features with Chalk's C# client library
- [Elixir](https://docs.chalk.ai/docs/client-elixir): Query features with Chalk's Elixir client library
- [Ruby](https://docs.chalk.ai/docs/client-ruby): Query features with Chalk's Ruby client library

## Updates

- [Release Notes](https://docs.chalk.ai/release-notes)

## Other pages

- [Streaming Resource Groups](https://docs.chalk.ai/docs/streaming-resource-groups): Assign streaming resolvers to dedicated resource groups for workload isolation.
- [GPUs and Resource Hints](https://docs.chalk.ai/docs/resource-hints): Schedule workloads on differentiated hardware & execution pools
- [Error Handling](https://docs.chalk.ai/docs/query-errors): Run resolvers in the presence of upstream failures.
- [Query Scheduling](https://docs.chalk.ai/docs/query-cron): Create and ingest datasets on a schedule
- [Sharing sensitive data with Chalk](https://docs.chalk.ai/docs/public-key): Encrypt data with Chalk's public key before sending it, using GnuPG.
- [OpenAI](https://docs.chalk.ai/docs/openai): Integrate with OpenAI for embedding models.
- [Offline Query Heartbeat Timeout](https://docs.chalk.ai/docs/offline-query-timeout): Learn more about debugging offline query timeouts.
- [Magics](https://docs.chalk.ai/docs/notebook-magics): Chalk cell magics for notebooks
- [Inline Syntax](https://docs.chalk.ai/docs/notebook-inline): Defining features inline in notebooks
- [Job Queue](https://docs.chalk.ai/docs/job-queue): Understanding Chalk's job queue and resource groups
- [Getting Started](https://docs.chalk.ai/docs/getting-started): Your first time with Chalk
- [Tutorial: Backtesting](https://docs.chalk.ai/docs/fraud-6): Try out new feature values on historical data.
- [Tutorial: Inference](https://docs.chalk.ai/docs/fraud-5): Integrate Chalk into your production decisioning systems.
- [Tutorial: Python Resolvers](https://docs.chalk.ai/docs/fraud-4): Define resolvers in Python that call APIs and compute derived features.
- [Tutorial: SQL Resolvers](https://docs.chalk.ai/docs/fraud-3): Mapping data from SQL sources to feature classes.
- [Tutorial: Data Modeling](https://docs.chalk.ai/docs/fraud-2): Defining the features that we want to compute.
- [Feature Studio](https://docs.chalk.ai/docs/feature-studio): Define, deploy, and manage features and resolvers with Chalk's Feature Studio.
- [Support](https://docs.chalk.ai/docs/enterprise-support): Get in touch with our support team!
- [Enterprise Deployment](https://docs.chalk.ai/docs/deployment): Enterprise Deployment Model
- [Chalk Billing](https://docs.chalk.ai/docs/billing): Learn about Chalk's billing model and how to optimize costs.
- [Benchmarking](https://docs.chalk.ai/docs/benchmark): Preflighting scale to production
- [AWS Cloud Deployment IAM Requirements](https://docs.chalk.ai/docs/aws-cloud-deployment): Deploying Chalk to your AWS account.
- [Ambiguous Resolvers](https://docs.chalk.ai/docs/ambiguous-resolvers): Ambiguous Resolvers
- [AIMD Queue Server](https://docs.chalk.ai/docs/aimd-queue-server): Adaptive concurrency control for maximizing query throughput within a latency budget