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docs: point model developers to the Chap guides for the MLproject flow (CLIM-1477) #158

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2 changes: 2 additions & 0 deletions README.md
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Chapkit provides domain-specific modules for building machine learning services on top of servicekit's core framework. Includes artifact storage, configuration management, and ML train/predict workflows.

**Want to run a forecasting model in Chap?** You do not need to write a chapkit service. Write train and predict scripts and an `MLproject` file, and evaluate them with `chap eval --run-config.as-chapkit`. See [Bring your existing model to Chap](https://chap.dhis2.org/chap-modeling-platform/external_models/existing_model/) and [Build a new model from a template](https://chap.dhis2.org/chap-modeling-platform/external_models/mlproject_tutorial/) in the Chap docs.

## Features

- **Artifact Module**: Hierarchical storage for models, data, and experiment tracking with parent-child relationships
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2 changes: 2 additions & 0 deletions docs/guides/mlproject-runner.md
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`chapkit mlproject run` turns any directory containing an MLflow-style `MLproject` file into a running chapkit service — no code generation, no changes to the MLproject repo. It is aimed at users coming to chapkit with an existing `train.r` / `predict.py` (or any shell-command-driven model) who want a chapkit HTTP API around their scripts in seconds.

To evaluate an MLproject model with Chap, you do not need to start the service yourself: `chap eval --model-name <directory> --run-config.as-chapkit` runs `chapkit mlproject run` for you. The Chap docs cover the whole flow, from your scripts to a published Docker image: [Bring your existing model to Chap](https://chap.dhis2.org/chap-modeling-platform/external_models/existing_model/) and [Build a new model from a template](https://chap.dhis2.org/chap-modeling-platform/external_models/mlproject_tutorial/).

## Quick Start

Point it at an MLproject file or the directory that holds one:
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Build production-ready ML services with train/predict workflows, artifact storage, config management, and job scheduling - all in a few lines of code.

!!! tip "Want to run a forecasting model in Chap?"
You do not need to write a chapkit service or install chapkit yourself. Write a train and a predict script in R or Python and a short `MLproject` file, and evaluate the model with `chap eval --run-config.as-chapkit`, which starts the chapkit service for you. The Chap docs walk through it: [Bring your existing model to Chap](https://chap.dhis2.org/chap-modeling-platform/external_models/existing_model/) and [Build a new model from a template](https://chap.dhis2.org/chap-modeling-platform/external_models/mlproject_tutorial/).

## Quick Start: ML Service

```python
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