책 1~13장 순차 실습 가이드

git --version
uv --version

git version 2.47.1.windows.1
uv 0.12.3 (507230998 2026-08-07 x86_64-pc-windows-msvc)

Set-Location $HOME
uv python install 3.12
uv python find 3.12

C:\Users\<사용자>\AppData\Roaming\uv\python\cpython-3.12-windows-x86_64-none\python.exe

docker version --format "Client {{.Client.Version}} / Server {{.Server.Version}} {{.Server.Os}}/{{.Server.Arch}}"
docker info --format "{{.OSType}} {{.NCPU}} CPU"

Client 29.3.1 / Server 29.3.1 linux/amd64
linux 16 CPU

New-Item -ItemType Directory -Path "$HOME\bin" -Force | Out-Null
curl.exe -Lo "$HOME\bin\kind.exe" https://kind.sigs.k8s.io/dl/v0.27.0/kind-windows-amd64
$env:PATH = "$HOME\bin;$env:PATH"
kind version
kubectl version --client

kind v0.27.0 go1.23.6 windows/amd64
Client Version: v1.34.1
Kustomize Version: v5.7.1

node --version
npm --version

v24.12.0
11.6.2

Set-Location $HOME\ml-labs-book\ml-platform-engineering-labs
$W = ".ml-labs\book-01"
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-base"
Remove-Item Env:MLFLOW_TRACKING_URI -ErrorAction SilentlyContinue

New-Item -ItemType Directory -Path "$HOME\ml-labs-book" -Force | Out-Null
Set-Location "$HOME\ml-labs-book"
git clone https://github.com/johklo/ml-platform-engineering-labs.git
Set-Location ml-platform-engineering-labs
git checkout --detach ff23b38f88d88268216e2985ff5b6ed0c3defb8c
git rev-parse HEAD

ff23b38f88d88268216e2985ff5b6ed0c3defb8c

$W = ".ml-labs\book-01"
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-base"
Remove-Item Env:MLFLOW_TRACKING_URI -ErrorAction SilentlyContinue
uv sync --locked --python 3.12 --extra dev
uv run --locked --extra dev python --version
uv run --locked --extra dev ml-labs --help

Resolved 367 packages in 2ms
Checked 86 packages in 33ms
Python 3.12.12
usage: ml-labs [-h]
               {cards,movies,rag,register,serve,smoke,release,batch,exercise,azure,tools}
               ...

Run reproducible local ML platform labs; cloud resources are never created.

$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-tools"
uv sync --locked --python 3.12 --extra dev --extra feast --extra pipeline --extra monitor
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-bento"
uv sync --locked --python 3.12 --extra dev --extra bento
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-vision"
uv sync --locked --python 3.12 --extra dev --extra vision
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-base"
Get-ChildItem .ml-labs -Directory -Filter "book-env-*" | Select-Object Name

Name
----
book-env-base
book-env-bento
book-env-tools
book-env-vision

uv run --locked --extra dev ml-labs smoke --work "$W\smoke"

2026/11/07 20:26:56 INFO mlflow.store.db.utils: Creating initial MLflow database tables...
2026/11/07 20:26:57 INFO mlflow.tracking.fluent: Experiment with name 'ml-platform-labs' does not exist. Creating a new experiment.
Successfully registered model 'ml-labs-cards'.
Created version '1' of model 'ml-labs-cards'.
Successfully registered model 'ml-labs-movies'.
Created version '1' of model 'ml-labs-movies'.
Successfully registered model 'ml-labs-rag'.
Created version '1' of model 'ml-labs-rag'.
{
  "cards": { "task": "cards", "passed": true, ... },
  "movies": { "task": "movies", "passed": true, ... },
  "rag": { "task": "rag", "passed": true, "answer_mode": "extractive_offline", ... },
  "registered": [
    "models:/ml-labs-cards/1",
    "models:/ml-labs-movies/1",
    "models:/ml-labs-rag/1"
  ]
}

Get-Content "$W\smoke\runs\movies\candidate.json"
Get-ChildItem "$W\smoke" | Select-Object Name

{
  "task": "movies",
  "run_id": "16fefc79d7094604876f9e5a36d8c924",
  "model_uri": "runs:/16fefc79d7094604876f9e5a36d8c924/model",
  "tracking_uri": "sqlite:///C:/Users/<사용자>/.../.ml-labs/book-01/smoke/tracking/tracking.db",
  "dataset": {
    "dataset": "synthetic-movies",
    "seed": 42,
    "rows": 432,
    "sha256": "6bb44545d72777952854f09ac4c22213a63fc8098cf9f664e690efce5e374910",
    ...
  },
  "metrics": {
    "validation_rmse": 0.4885255171433486,
    "baseline_rmse": 0.730372280846849,
    ...
  },
  "registered_model": "ml-labs-movies",
  "registered_version": "1",
  "registered_alias": "champion",
  "registered_uri": "models:/ml-labs-movies/1"
}

Name
----
data
reports
runs
tracking

docker version --format "Client {{.Client.Version}} / Server {{.Server.Version}} {{.Server.Os}}/{{.Server.Arch}}"
kind version
kubectl version --client

Client 29.3.1 / Server 29.3.1 linux/amd64
kind v0.27.0 go1.23.6 windows/amd64
Client Version: v1.34.1
Kustomize Version: v5.7.1

Set-Location "$HOME\ml-labs-book"
git clone https://github.com/johklo/ml-platform-book-env.git
Set-Location ml-platform-book-env
uv run --no-project --python 3.12 python bookenv.py check

docker   C:\Program Files\Docker\Docker\resources\bin\docker.EXE
kind     C:\Users\<사용자>\...\bin\kind.EXE
kubectl  C:\Program Files\Docker\Docker\resources\bin\kubectl.EXE
git      C:\Program Files\Git\cmd\git.EXE
$ C:\Program Files\Docker\Docker\resources\bin\docker.EXE info --format Docker {{.ServerVersion}}
Docker 29.3.1
All required tools are available.

uv run --no-project --python 3.12 python bookenv.py up

Set-Location "$HOME\ml-labs-book\ml-platform-book-env"
uv run --no-project --python 3.12 python bookenv.py forward

uv run --no-project --python 3.12 python bookenv.py status

== kubeflow
NAME                                                READY   STATUS      RESTARTS      AGE
cache-deployer-deployment-67b7ff8785-jvwl9          1/1     Running     0             89m
cache-server-fb5bc88b7-cjz89                        1/1     Running     0             89m
metadata-envoy-deployment-7964b7b586-9nmcn          1/1     Running     0             89m
metadata-grpc-deployment-66b7d897b4-xt9b9           1/1     Running     7 (82m ago)   89m
metadata-writer-76899549df-bqvxx                    1/1     Running     3 (78m ago)   89m
minio-76d4b8f979-bzk5t                              1/1     Running     0             73m
ml-pipeline-794c8cd8b7-z895t                        1/1     Running     7 (77m ago)   89m
ml-pipeline-persistenceagent-68b6c7fddf-zjqt4       1/1     Running     4 (76m ago)   89m
ml-pipeline-scheduledworkflow-7f8667b76b-sgv96      1/1     Running     0             89m
ml-pipeline-ui-649594d7f4-nb58r                     1/1     Running     0             89m
ml-pipeline-viewer-crd-79d4555d8f-tbgzh             1/1     Running     0             89m
ml-pipeline-visualizationserver-6b9c885665-sqptc    1/1     Running     0             89m
mysql-75d57bbfbf-pdfvh                              1/1     Running     0             89m
workflow-controller-5cb6757498-l2vzd                1/1     Running     0             89m

== mlflow
NAME                                READY   STATUS    RESTARTS   AGE
mlflow-deployment-d9f8c7688-7njm8   1/1     Running   0          74m

== postgres
NAME                                           READY   STATUS    RESTARTS   AGE
postgres-release-postgresql-546c65b4db-nbmzd   1/1     Running   0          89m

== redis
NAME                                       READY   STATUS    RESTARTS   AGE
redis-deployment-master-68886f9c8f-mtjxh   1/1     Running   0          89m

$W = ".ml-labs\book-01"
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-base"
uv run --locked --extra dev ml-labs movies prepare --output "$W\data\movies"

{
  "result": ".ml-labs\\book-01\\data\\movies"
}

uv run --locked --extra dev ml-labs movies train --data "$W\data\movies" --output "$W\runs\movies" --work "$W\tracking" --rank 4

2026/11/07 20:29:01 INFO mlflow.store.db.utils: Creating initial MLflow database tables...
2026/11/07 20:29:03 INFO mlflow.tracking.fluent: Experiment with name 'ml-platform-labs' does not exist. Creating a new experiment.
{
  "result": ".ml-labs\\book-01\\runs\\movies\\candidate.json"
}

uv run --locked --extra dev ml-labs movies evaluate --candidate "$W\runs\movies\candidate.json" --data "$W\data\movies" --report "$W\reports\movies.json"
$LASTEXITCODE
uv run --locked --extra dev ml-labs register --candidate "$W\runs\movies\candidate.json" --report "$W\reports\movies.json" --name book-movie
(Get-Content "$W\runs\movies\candidate.json" -Raw | ConvertFrom-Json).registered_uri

{
  "result": {
    "task": "movies",
    "passed": true,
    "max_validation_rmse": 1.5,
    ...
    "observed_validation_rmse": 0.6722079292983877,
    "ranking_gate": {
      "min_ndcg_lift": null,
      "observed_lift": 0.32112089644232705,
      "passed": true
    },
    "test_policy": "Test split was scored only after the validation gate."
  }
}
0
Successfully registered model 'book-movie'.
Created version '1' of model 'book-movie'.
models:/book-movie/1

Set-Location $HOME\ml-labs-book\ml-platform-engineering-labs
$W = ".ml-labs\book-01"
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-base"
$db = (Resolve-Path "$W\tracking\tracking.db").Path.Replace('\', '/')
uv run --locked --extra dev mlflow ui --backend-store-uri "sqlite:///$db" --host 127.0.0.1 --port 5001

uv run --locked --extra dev ml-labs movies evaluate --candidate "$W\runs\movies\candidate.json" --data "$W\data\movies" --report "$W\reports\movies-rejected.json" --max-rmse 0.01
$LASTEXITCODE
uv run --locked --extra dev ml-labs register --candidate "$W\runs\movies\candidate.json" --report "$W\reports\movies-rejected.json" --name must-not-register
$LASTEXITCODE

ml-labs: error: Model validation gate rejected candidate; report: .ml-labs\book-01\reports\movies-rejected.json
2
ml-labs: error: Registry promotion requires a passing movies evaluation report.
2

$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-tools"
uv run --locked --extra feast ml-labs tools feast init --data "$W\data\movies" --output "$W\feast"
uv run --locked --extra feast ml-labs tools feast run --repo "$W\feast" --action apply
uv run --locked --extra feast ml-labs tools feast run --repo "$W\feast" --action historical --user-id u007

Uninstalled 1 package in 17ms
Installed 1 package in 48ms
{
  "repo": ".ml-labs\\book-01\\feast"
}
WARNING:root:Cannot use sqlite_vec for vector search
{
  "action": "apply",
  "dataset_sha256": "9acf3ae528740f8e99f986e3eba5e69377ab04fcdeef77f351338415e8aa5f1b",
  "status": "applied"
}
{
  "action": "historical",
  "user_id": "u007",
  "rows": [
    { "event_timestamp": "2024-12-31T00:00:00+00:00", "average_rating": null, "ratings_count": null,
      "returned_by_feast": false, "point_in_time_correct": true },
    { "event_timestamp": "2025-01-01T00:00:00+00:00", "average_rating": 2.240000009536743, "ratings_count": 1,
      "returned_by_feast": true, "point_in_time_correct": true },
    { "event_timestamp": "2025-01-08T12:00:00+00:00", "average_rating": 2.40625, "ratings_count": 8,
      "returned_by_feast": true, "point_in_time_correct": true },
    { "event_timestamp": "2025-02-13T00:00:00+00:00", "average_rating": 2.780428647994995, "ratings_count": 14,
      "returned_by_feast": true, "point_in_time_correct": true }
  ]
}

uv run --locked --extra feast ml-labs tools feast run --repo "$W\feast" --action online --user-id u007
$LASTEXITCODE
uv run --locked --extra feast ml-labs tools feast run --repo "$W\feast" --action materialize
uv run --locked --extra feast ml-labs tools feast run --repo "$W\feast" --action online --user-id u007

ml-labs: error: No online features for 'u007'. Run `ml-labs tools feast run --action apply` and `--action materialize` first (the online store is filled only by materialization).
2
Materializing 1 feature views from 2025-01-01 08:59:59+09:00 to 2025-01-14 09:00:01+09:00 into the sqlite online store.

user_movie_stats:
100%|█████████████████████████████████████████████████████████████| 48/48 [00:00<00:00, 1981.87it/s]
{
  "action": "materialize",
  "status": "materialized",
  "start": "2024-12-31T23:59:59+00:00",
  "end": "2025-01-14T00:00:01+00:00"
}
{
  "action": "online",
  "user_id": "u007",
  "features": {
    "user_id": ["u007"],
    "average_rating": [2.780428647994995],
    "observed_at_unix": [1736812800],
    "ratings_count": [14]
  }
}

$W = ".ml-labs\book-01"
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-tools"
$revision = git rev-parse HEAD
uv run --locked --extra pipeline ml-labs tools kfp compile --source-revision $revision --output "$W\kfp\movie-pipeline.yaml"
Get-Content "$W\kfp\movie-pipeline.yaml" -TotalCount 12

{
  "output": ".ml-labs\\book-01\\kfp\\movie-pipeline.yaml",
  "image": "SET-YOUR-REGISTRY/ml-platform-engineering-labs:SET-IMAGE-TAG",
  "source_revision": "ff23b38f88d88268216e2985ff5b6ed0c3defb8c",
  "image_is_placeholder": true,
  "submitted": false,
  "warning": "Image is a placeholder; build/push the repo Dockerfile and recompile with --image <registry>/<name>@sha256:<digest> before submitting."
}
# PIPELINE DEFINITION
# Name: ml-labs-movie-recommender
# Description: Synthetic movie data -> SVD recommender (MLflow) -> gated holdout evaluation
# Inputs:
#    items: int [Default: 32.0]
#    max_rmse: float [Default: 1.5]
#    mlflow_root: str [Default: '/workspace/mlflow']
#    rank: int [Default: 4.0]
#    seed: int [Default: 42.0]
#    source_revision: str [Default: 'ff23b38f88d88268216e2985ff5b6ed0c3defb8c']
#    users: int [Default: 48.0]
components:

      evaluate-movie-recommender:
        dependentTasks:
        - prepare-movie-data
        - train-movie-recommender
        inputs:
          artifacts:
            dataset:
              taskOutputArtifact:
                outputArtifactKey: dataset
                producerTask: prepare-movie-data
            model:
              taskOutputArtifact:
                outputArtifactKey: model
                producerTask: train-movie-recommender
        taskInfo:
          name: evaluate-holdout

uv run --locked --extra dev --extra pipeline pytest -q -p no:warnings tests\test_optional_integrations.py -k "native_kfp or kfp_component_bodies"

Uninstalled 1 package in 15ms
Installed 1 package in 34ms
..                                                                       [100%]
2 passed, 8 deselected in 6.31s

$revision = git rev-parse HEAD
docker build --platform linux/amd64 --build-arg SOURCE_REVISION=$revision -t book-labs:ff23b38 .
docker images book-labs

#14 [stage-0 8/9] RUN uv pip install --system --no-cache --no-deps .     && python -c "import kfp, ml_labs.movies, ml_labs.data; print('ml_labs image ready')"
#14 1.693 ml_labs image ready
#14 DONE 10.1s
#16 naming to docker.io/library/book-labs:ff23b38 0.0s done
#16 unpacking to docker.io/library/book-labs:ff23b38 5.7s done
#16 DONE 26.8s
IMAGE               ID             DISK USAGE   CONTENT SIZE   EXTRA
book-labs:ff23b38   03760880abcd       1.14GB          254MB

kind load docker-image book-labs:ff23b38 --name book-env
docker exec book-env-control-plane crictl images | Select-String "book-labs|IMAGE"

Image: "book-labs:ff23b38" with ID "sha256:03760880abcd5266131afd12415e007bf1274fa0d0914f925060d539fdedc062" not yet present on node "book-env-control-plane", loading...
IMAGE                                                TAG                          IMAGE ID            SIZE
docker.io/library/book-labs                          ff23b38                      20096132a8c97       254MB

docker save book-labs:ff23b38 -o "$W\book-labs.tar"
kind load image-archive "$W\book-labs.tar" --name book-env

uv run --locked --extra pipeline ml-labs tools kfp compile --source-revision $revision --image book-labs:ff23b38 --output "$W\kfp\movie-pipeline-cluster.yaml"
Select-String -Path "$W\kfp\movie-pipeline-cluster.yaml" -Pattern "image:"

{
  "output": ".ml-labs\\book-01\\kfp\\movie-pipeline-cluster.yaml",
  "image": "book-labs:ff23b38",
  "source_revision": "ff23b38f88d88268216e2985ff5b6ed0c3defb8c",
  "image_is_placeholder": false,
  "submitted": false
}
.ml-labs\book-01\kfp\movie-pipeline-cluster.yaml:133:        image: book-labs:ff23b38
.ml-labs\book-01\kfp\movie-pipeline-cluster.yaml:163:        image: book-labs:ff23b38
.ml-labs\book-01\kfp\movie-pipeline-cluster.yaml:204:        image: book-labs:ff23b38

panic: runtime error: invalid memory address or nil pointer dereference
github.com/kubeflow/pipelines/backend/src/v2/driver.resolveUpstreamArtifacts(...)
	/go/src/github.com/kubeflow/pipelines/backend/src/v2/driver/driver.go:1519 +0x4b9

uv run --locked --extra pipeline python -c "import sys,yaml; d=yaml.safe_load(open(sys.argv[1],encoding='utf-8')); [t['taskInfo'].update(name=k) for k,t in d['root']['dag']['tasks'].items()]; yaml.safe_dump(d,open(sys.argv[2],'w',encoding='utf-8'),sort_keys=False); print({k:t['taskInfo']['name'] for k,t in d['root']['dag']['tasks'].items()})" "$W\kfp\movie-pipeline-cluster.yaml" "$W\kfp\movie-pipeline-kfp24.yaml"

{'evaluate-movie-recommender': 'evaluate-movie-recommender', 'prepare-movie-data': 'prepare-movie-data', 'train-movie-recommender': 'train-movie-recommender'}

uv run --locked --extra pipeline python -c "import sys,kfp; c=kfp.Client(host='http://127.0.0.1:8080'); run=c.create_run_from_pipeline_package(sys.argv[1],arguments={'rank':4},experiment_name='guide-book-labs',run_name='guide-movie-rank4',enable_caching=False); r=c.wait_for_run_completion(run.run_id,timeout=900); print(run.run_id, r.state); [print(t.display_name, t.state) for t in r.run_details.task_details if t.display_name in ('prepare-movie-data','train-movie-recommender','evaluate-movie-recommender')]" "$W\kfp\movie-pipeline-kfp24.yaml"

Experiment details: http://127.0.0.1:8080/#/experiments/details/652161b2-4c8f-47b7-a416-cb78409f5f44
Run details: http://127.0.0.1:8080/#/runs/details/917f69c6-a703-4720-a3f6-b8dd6a04c2a9
917f69c6-a703-4720-a3f6-b8dd6a04c2a9 SUCCEEDED
prepare-movie-data SUCCEEDED
train-movie-recommender SUCCEEDED
evaluate-movie-recommender SUCCEEDED

kubectl --context kind-book-env -n kubeflow get pods | Select-String "ml-labs-movie-recommender"

Set-Location $HOME\ml-labs-book\ml-platform-engineering-labs
$W = ".ml-labs\book-01"
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-base"
Remove-Item Env:MLFLOW_TRACKING_URI -ErrorAction SilentlyContinue
uv run --locked --extra dev ml-labs serve --movies "$W\runs\movies\candidate.json" --host 127.0.0.1 --port 8000

Invoke-RestMethod http://127.0.0.1:8000/health | ConvertTo-Json -Depth 6
$body = @{ user_id = "u000" } | ConvertTo-Json
Invoke-RestMethod http://127.0.0.1:8000/recommend -Method Post -ContentType application/json -Body $body | ConvertTo-Json -Depth 6
$body = @{ user_id = "new-user" } | ConvertTo-Json
Invoke-RestMethod http://127.0.0.1:8000/recommend -Method Post -ContentType application/json -Body $body | ConvertTo-Json -Depth 6

{
  "status": "ok",
  "models": { "cards": false, "movies": true, "rag": false },
  "movie_release_id": null,
  "feast_enabled": false,
  "rag_release_configured": false
}
{
  "user_id": "u000",
  "status": "personalized",
  "recommendations": [
    { "item_id": "m025", "score": 3.972719144406785 },
    { "item_id": "m022", "score": 3.758731167815679 },
    ...
  ],
  "feature_routing": null,
  "model_uri": "models:/book-movie/1",
  "release_id": null
}
{
  "user_id": "new-user",
  "status": "cold_start_popularity",
  "recommendations": [
    { "item_id": "m031", "score": 3.3113125 },
    { "item_id": "m025", "score": 3.2506818181818176 },
    ...
  ],
  "feature_routing": null,
  "model_uri": "models:/book-movie/1",
  "release_id": null
}

$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-bento"
$env:BENTOML_HOME = Join-Path (Resolve-Path $W).Path "bentoml-home"
uv run --locked --extra bento ml-labs tools bento build --candidate "$W\runs\movies\candidate.json" --output "$W\bento"
uv run --locked --extra bento bentoml build "$W\bento"
uv run --locked --extra bento bentoml models list
uv run --locked --extra bento bentoml list

{
  "output": ".ml-labs\\book-01\\bento\\bentofile.yaml"
}
INFO: Locking PyPI package versions.
INFO: Locking packages for x86_64-unknown-linux-gnu. Pass `--platform` option to specify the platform.

Successfully built Bento(tag="ml-labs-movies:nql2o762ug3h3laz").
 Tag                           Module          Size        Creation Time
 ml-labs-movies:njdahiw2ugjh…  bentoml.mlflow  219.72 KiB  2026-11-07 20:50:23
 Tag                              Size       Model Size  Creation Time
 ml-labs-movies:nql2o762ug3h3laz  19.24 KiB  219.72 KiB  2026-11-07 20:50:26

$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-bento"
$env:BENTOML_HOME = Join-Path (Resolve-Path $W).Path "bentoml-home"
uv run --locked --extra bento bentoml serve service:PlatformLabService --working-dir "$W\bento" --host 127.0.0.1 --port 3000

$body = @{ request = @{ user_id = "u000" } } | ConvertTo-Json -Depth 4
Invoke-RestMethod http://127.0.0.1:3000/predict -Method Post -ContentType application/json -Body $body | ConvertTo-Json -Depth 6

{
  "user_id": "u000",
  "status": "personalized",
  "recommendations": [
    { "item_id": "m025", "score": 3.972719144406785 },
    { "item_id": "m022", "score": 3.758731167815679 },
    ...
  ]
}

$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-base"
uv run --locked --extra dev ml-labs cards prepare --output "$W\data\cards" --train 80 --validation 20 --test 20
Get-Content "$W\data\cards\manifest.json"

{
  "result": "C:\\Users\\<사용자>\\...\\.ml-labs\\book-01\\data\\cards"
}
{
  "dataset": "synthetic-cards",
  "seed": 42,
  "sha256": "3b33e08ea983fa42431bbfc7dc1b07e203afa21fec1c678437c30ac7ed1c19aa",
  "counts": {
    "train": 80,
    "validation": 20,
    "test": 20
  },
  "limitations": "Axis-aligned generated cards, independent images, no identity data; not OCR."
}

Get-ChildItem "$W\data\cards\images\train" | Select-Object -First 3 Name, Length
Get-ChildItem "$W\data\cards\labels\train" | Select-Object -First 3 Name, Length
Get-Content "$W\data\cards\labels\train\train-0001.txt"

Name                 Length
----                 ------
train-0000.png        43666
train-0001.png        37664
train-0002.png        37428
train-0000.txt            0
train-0001.txt           39
train-0002.txt           39
0 0.355469 0.527344 0.507812 0.273438

New-Item -ItemType Directory -Path "$W\downloads" -Force | Out-Null
Invoke-WebRequest https://files.grouplens.org/datasets/movielens/ml-latest-small.zip -OutFile "$W\downloads\ml-latest-small.zip"
uv run --locked --extra dev ml-labs movies import-movielens --archive "$W\downloads\ml-latest-small.zip" --output "$W\data\movielens"
Get-Content "$W\data\movielens\manifest.json"

{
  "result": ".ml-labs\\book-01\\data\\movielens"
}
{
  "dataset": "movielens-latest-small",
  "rows": 100836,
  "sha256": "040461a97369a50dddec17d460ae4d11be07d5e8a3ebecb665cba20dbdda02a1",
  "archive_sha256": "28cef425f57f870a1b4d8e32d100017b4dd8f3238481430fb9bca8705100d27d",
  "source": "https://grouplens.org/datasets/movielens/latest/",
  "rating_transform": "1 + (original_rating - 0.5) * 4 / 4.5",
  "split": "per-user chronological timestamp groups, 80/10/10 of unique timestamps",
  "license": "MovieLens terms apply; archive and derived data must not be committed."
}

uv run --locked --extra dev ml-labs cards train --data "$W\data\cards" --output "$W\runs\cards" --work "$W\tracking"
uv run --locked --extra dev ml-labs cards evaluate --candidate "$W\runs\cards\candidate.json" --data "$W\data\cards" --report "$W\reports\cards.json"
uv run --locked --extra dev ml-labs register --candidate "$W\runs\cards\candidate.json" --report "$W\reports\cards.json" --name book-card-baseline

{
  "result": ".ml-labs\\book-01\\runs\\cards\\candidate.json"
}
{
  "result": {
    "task": "cards",
    "passed": true,
    "min_mean_iou": 0.75,
    "validation_mean_iou": 0.9999976948801512,
    "metrics": {
      "images": 20,
      "mean_iou": 0.9999983959985037,
      "box_recall_iou_0_5": 1.0,
      "negative_images": 4,
      "false_positives": 0
    },
    "candidate": { "backend": "color_component_baseline", ... },
    "split": "test",
    "test_policy": "Test split opened after passing the validation IoU gate."
  }
}
Successfully registered model 'book-card-baseline'.
Created version '1' of model 'book-card-baseline'.

$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-vision"
New-Item -ItemType Directory -Path "$W\yolo-config" -Force | Out-Null
$env:YOLO_CONFIG_DIR = (Resolve-Path "$W\yolo-config").Path
uv run --locked --extra vision yolo settings mlflow=False sync=False

Creating new Ultralytics Settings v0.0.6 file ✅
View Ultralytics Settings with 'yolo settings' or at 'C:\Users\<사용자>\...\.ml-labs\book-01\yolo-config\settings.json'
...
  "sync": false,
  ...
  "mlflow": false,

uv run --locked --extra vision ml-labs cards train --data "$W\data\cards" --output "$W\runs\cards-yolo" --work "$W\tracking" --backend yolov8 --epochs 10

Ultralytics 8.3.40 🚀 Python-3.12.12 torch-2.5.1+cpu CPU (13th Gen Intel Core(TM) i7-1360P)
engine\trainer: task=detect, mode=train, model=yolov8n.pt, ..., epochs=10, ..., imgsz=128, ..., device=cpu, ...
Overriding model.yaml nc=80 with nc=1
Transferred 319/355 items from pretrained weights
...
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)
                   all         20         16      0.987          1      0.995      0.976
Results saved to .ml-labs\book-01\runs\cards-yolo\yolo\synthetic-cards
{
  "result": ".ml-labs\\book-01\\runs\\cards-yolo\\candidate.json"
}

Get-ChildItem "$W\runs\cards-yolo\yolo\synthetic-cards" -Recurse -Include *.pt, results.png, val_batch0_pred.jpg, results.csv | Select-Object Name, Length
Start-Process "$W\runs\cards-yolo\yolo\synthetic-cards\results.png"

Name                 Length
----                 ------
best.pt             6190883
last.pt             6190883
results.csv            1332
results.png          282859
val_batch0_pred.jpg  107486

uv run --locked --extra vision ml-labs cards evaluate --candidate "$W\runs\cards-yolo\candidate.json" --data "$W\data\cards" --report "$W\reports\cards-yolo.json" --min-iou 0.5
uv run --locked --extra vision ml-labs register --candidate "$W\runs\cards-yolo\candidate.json" --report "$W\reports\cards-yolo.json" --name book-card-yolo

{
  "result": {
    "task": "cards",
    "passed": true,
    "min_mean_iou": 0.5,
    "validation_mean_iou": 0.8492493319417679,
    "metrics": {
      "images": 20,
      "mean_iou": 0.9675049159141798,
      "box_recall_iou_0_5": 1.0,
      "negative_images": 4,
      "false_positives": 0
    },
    "candidate": {
      "backend": "yolov8",
      "validation": { "detection_rate": 0.7, "mean_iou": 0.8492493319417679, "box_recall_iou_0_5": 0.875, ... },
      ...
    }
  }
}
Successfully registered model 'book-card-yolo'.
Created version '1' of model 'book-card-yolo'.

$env:ML_LABS_RUN_VISION = "1"
uv run --locked --extra dev --extra vision pytest -q -p no:warnings tests\test_vision.py
Remove-Item Env:ML_LABS_RUN_VISION

.                                                                        [100%]
1 passed in 12.69s

$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-base"
uv run --locked --extra dev ml-labs movies prepare --output "$W\data\movies-ch09" --seed 43
uv run --locked --extra dev ml-labs movies train --data "$W\data\movies-ch09" --output "$W\runs\movies-rank2" --work "$W\tracking" --rank 2
uv run --locked --extra dev ml-labs movies train --data "$W\data\movies-ch09" --output "$W\runs\movies-rank4" --work "$W\tracking" --rank 4

{
  "result": ".ml-labs\\book-01\\data\\movies-ch09"
}
{
  "result": ".ml-labs\\book-01\\runs\\movies-rank2\\candidate.json"
}
{
  "result": ".ml-labs\\book-01\\runs\\movies-rank4\\candidate.json"
}

$paths = @("$W\runs\movies-rank2\candidate.json", "$W\runs\movies-rank4\candidate.json")
$ranked = $paths | ForEach-Object {
    $candidate = Get-Content $_ -Raw | ConvertFrom-Json
    [pscustomobject]@{
        Path = $_
        NDCG = $candidate.metrics.validation_model_ndcg_at_5
        RMSE = $candidate.metrics.validation_rmse
    }
} | Sort-Object @{Expression = "NDCG"; Descending = $true}, @{Expression = "RMSE"; Ascending = $true}
$ranked | Format-Table -AutoSize
$MovieCandidate = $ranked[0].Path
$MovieCandidate | Set-Content "$W\selected-movie-path.txt" -Encoding utf8

Path                                              NDCG RMSE
----                                              ---- ----
.ml-labs\book-01\runs\movies-rank2\candidate.json 0.50 0.68
.ml-labs\book-01\runs\movies-rank4\candidate.json 0.40 0.67

uv run --locked --extra dev ml-labs movies evaluate --candidate $MovieCandidate --data "$W\data\movies-ch09" --report "$W\reports\movie-selected.json"
$LASTEXITCODE
uv run --locked --extra dev ml-labs register --candidate $MovieCandidate --report "$W\reports\movie-selected.json" --name book-movie-selected

    "passed": true,
      "test_recall_at_5": 0.6666666666666666,
    "observed_validation_rmse": 0.6786069439813943,
      "observed_lift": 0.21742120054652125,
      "passed": true
0
Successfully registered model 'book-movie-selected'.
Created version '1' of model 'book-movie-selected'.
  "registered_uri": "models:/book-movie-selected/1"

$MovieCandidate = (Get-Content "$W\selected-movie-path.txt" -Raw).Trim()
uv run --locked --extra dev ml-labs release activate --candidate $MovieCandidate --report "$W\reports\movie-selected.json" --store "$W\movie-releases"
uv run --locked --extra dev ml-labs release show --store "$W\movie-releases"

{
  "candidate": {
    "task": "movies",
    "run_id": "a260879e6a8a449b955a6cf19ac316d0",
    ...
    "registered_uri": "models:/book-movie-selected/1"
  },
  "report_sha256": "f54cd82f8224ea5ac4932708798020e748ec947c0463d99f18c549e3c284d6b1",
  "dataset_sha256": "8e9118d658ca4acb4d1898662df0a296b958692bc69b2eff1b6815d2fbb6cc7e",
  "release_id": "5a919a1c3045fc007f7148f5a624702cb0c20998a4e23b7856f7b6badf5e08c3"
}

Set-Location $HOME\ml-labs-book\ml-platform-engineering-labs
$W = ".ml-labs\book-01"
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-base"
Remove-Item Env:MLFLOW_TRACKING_URI -ErrorAction SilentlyContinue
uv run --locked --extra dev ml-labs serve --movie-release "$W\movie-releases" --host 127.0.0.1 --port 8000

Invoke-RestMethod http://127.0.0.1:8000/health | ConvertTo-Json -Depth 6
$body = @{ user_id = "u000" } | ConvertTo-Json
Invoke-RestMethod http://127.0.0.1:8000/recommend -Method Post -ContentType application/json -Body $body | ConvertTo-Json -Depth 6

{
  "status": "ok",
  "models": { "cards": false, "movies": true, "rag": false },
  "movie_release_id": "5a919a1c3045fc007f7148f5a624702cb0c20998a4e23b7856f7b6badf5e08c3",
  ...
}
{
  "user_id": "u000",
  "status": "personalized",
  "recommendations": [
    { "item_id": "m022", "score": 3.405480632014961 },
    { "item_id": "m005", "score": 3.279502309118273 },
    ...
  ],
  "feature_routing": null,
  "model_uri": "models:/book-movie-selected/1",
  "release_id": "5a919a1c3045fc007f7148f5a624702cb0c20998a4e23b7856f7b6badf5e08c3"
}

@("user_id", "u000", "new-user") | Set-Content "$W\batch-users.csv" -Encoding utf8
uv run --locked --extra dev ml-labs batch --candidate $MovieCandidate --report "$W\reports\movie-selected.json" --users "$W\batch-users.csv" --output "$W\batch-results" --job-key book-day-01 --fail-before-commit
$LASTEXITCODE
Get-ChildItem "$W\batch-results" -Recurse -File

ml-labs: error: Injected failure after inference, before batch publication.
2

uv run --locked --extra dev ml-labs batch --candidate $MovieCandidate --report "$W\reports\movie-selected.json" --users "$W\batch-users.csv" --output "$W\batch-results" --job-key book-day-01
uv run --locked --extra dev ml-labs batch --candidate $MovieCandidate --report "$W\reports\movie-selected.json" --users "$W\batch-users.csv" --output "$W\batch-results" --job-key book-day-01

{
  "output": ".ml-labs\\book-01\\batch-results\\130bd3280be6ec957d458e7d8bc0dd495362c82053fbc8f4f8a9e5fd8900b892",
  "reused": false,
  "identity": {
    "job_key": "book-day-01",
    "model_uri": "models:/book-movie-selected/1",
    "users_sha256": "753bea19c4ac45477e724eefe22c10621c17d9258ad7774a11d6de6437ac501d",
    "dataset_sha256": "8e9118d658ca4acb4d1898662df0a296b958692bc69b2eff1b6815d2fbb6cc7e",
    ...
  },
  "rows": 2,
  "output_sha256": "fbbe21abf540f1739a4a7c8f766a91f2fb28cd8d76cf7f7191d86a8a89bffd8c"
}
{
  "output": ".ml-labs\\book-01\\batch-results\\130bd3280be6ec957d458e7d8bc0dd495362c82053fbc8f4f8a9e5fd8900b892",
  "reused": true,
  ...
  "output_sha256": "fbbe21abf540f1739a4a7c8f766a91f2fb28cd8d76cf7f7191d86a8a89bffd8c"
}

@("user_id", "u001") | Set-Content "$W\batch-users-other.csv" -Encoding utf8
uv run --locked --extra dev ml-labs batch --candidate $MovieCandidate --report "$W\reports\movie-selected.json" --users "$W\batch-users-other.csv" --output "$W\batch-results" --job-key book-day-01

ml-labs: error: Business job key already belongs to different inputs or model.

Set-Location $HOME\ml-labs-book\ml-platform-engineering-labs
$W = ".ml-labs\book-01"
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-vision"
$env:YOLO_CONFIG_DIR = (Resolve-Path "$W\yolo-config").Path
Remove-Item Env:MLFLOW_TRACKING_URI -ErrorAction SilentlyContinue
uv run --locked --extra vision ml-labs serve --cards "$W\runs\cards-yolo\candidate.json" --host 127.0.0.1 --port 8002

foreach ($name in "test-0000", "test-0001") {
    $body = @{ image_path = "$W\data\cards\images\test\$name.png" } | ConvertTo-Json
    Invoke-RestMethod http://127.0.0.1:8002/predict/cards -Method Post -ContentType application/json -Body $body | ConvertTo-Json -Depth 6
}
Get-Content "$W\data\cards\labels\test\test-0001.txt"

{
  "image_path": ".ml-labs\\book-01\\data\\cards\\images\\test\\test-0000.png",
  "detected": false,
  "box_xyxy": null,
  "backend": "yolov8"
}
{
  "image_path": ".ml-labs\\book-01\\data\\cards\\images\\test\\test-0001.png",
  "detected": true,
  "box_xyxy": [
    28.966873168945312,
    14.083757400512695,
    82.9388427734375,
    62.413665771484375
  ],
  "backend": "yolov8"
}
0 0.433594 0.296875 0.429688 0.375000

(Invoke-WebRequest http://127.0.0.1:8000/metrics).Content -split "`n" | Select-String "ml_labs_inference_requests_total\{|ml_labs_inference_seconds_count"
$body = @{ user_id = "new-user" } | ConvertTo-Json
Invoke-RestMethod http://127.0.0.1:8000/recommend -Method Post -ContentType application/json -Body $body | Out-Null
(Invoke-WebRequest http://127.0.0.1:8000/metrics).Content -split "`n" | Select-String "ml_labs_inference_requests_total\{|ml_labs_inference_seconds_count"

ml_labs_inference_requests_total{status="success",task="movies"} 2.0
ml_labs_inference_seconds_count{task="movies"} 2.0
ml_labs_inference_requests_total{status="success",task="movies"} 3.0
ml_labs_inference_seconds_count{task="movies"} 3.0

$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-tools"
uv run --locked --extra monitor python -c "import sys,pandas as pd; d=pd.read_csv(sys.argv[1]); d[['rating']].to_csv(sys.argv[2],index=False); d['rating']=1.0; d[['rating']].to_csv(sys.argv[3],index=False)" "$W\data\movies\ratings.csv" "$W\reference.csv" "$W\current.csv"
uv run --locked --extra monitor ml-labs tools drift --reference "$W\reference.csv" --current "$W\current.csv" --output "$W\reports\drift.json" --backend evidently
Get-Content "$W\reports\drift.json" -TotalCount 12

{
  "backend": "evidently",
  "html_report": ".ml-labs\\book-01\\reports\\drift.html",
  "json_report": ".ml-labs\\book-01\\reports\\drift.json",
  "reference_rows": 864,
  "current_rows": 864,
  "monitoring_note": "Drift is a signal, not proof of model failure."
}
{
  "metrics": [
    {
      "metric": "DatasetDriftMetric",
      "result": {
        "drift_share": 0.5,
        "number_of_columns": 1,
        "number_of_drifted_columns": 1,
        "share_of_drifted_columns": 1.0,
        "dataset_drift": true
      }

Start-Process "$W\reports\drift.html"

uv run --locked --extra dev --extra feast --extra monitor ml-labs exercise --work "$W\integrated"
$evidence = Get-Content "$W\integrated\evidence.json" -Raw | ConvertFrom-Json
$evidence.feature_failure_http
$evidence.feature_recovery.status
$evidence.batch.retried.reused
$evidence.releases | ConvertTo-Json

503
personalized
True
{
  "first": "1fb36b3ba8a00771dc1863f3f60dab5f9b77153a5aab309fd44034fe2d1a4f59",
  "next": "198edac9ab7e89958a7bb0a2d4dad6e332280e99aeecd8318d720372eab95b0e",
  "restored": "1fb36b3ba8a00771dc1863f3f60dab5f9b77153a5aab309fd44034fe2d1a4f59"
}

$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-base"
uv run --locked --extra dev ml-labs rag release init-demo --output "$W\rag\inputs"
Get-Content "$W\rag\inputs\config.json"
Get-Content "$W\rag\inputs\evaluation.json"

{
  "corpus": ".ml-labs\\book-01\\rag\\inputs\\documents.jsonl",
  "config": ".ml-labs\\book-01\\rag\\inputs\\config.json",
  "prompt": ".ml-labs\\book-01\\rag\\inputs\\prompt.json",
  "evaluation": ".ml-labs\\book-01\\rag\\inputs\\evaluation.json"
}
{"min_score":0.15,"ngram_range":[1,2],"provider":"extractive_offline","retriever":"tfidf","stop_words":"english","top_k":2}
[{"answer_contains":["validation"],"expected_ids":["doc-mlflow-registry"],"id":"registry","query":"model registry champion promotion"},{"answer_contains":["historical"],"expected_ids":["doc-feast"],"id":"features","query":"Feast offline online feature store"},{"id":"unknown","query":"quasar teleportation warranty","unanswerable":true}]

uv run --locked --extra dev ml-labs rag release create --corpus "$W\rag\inputs\documents.jsonl" --config "$W\rag\inputs\config.json" --prompt "$W\rag\inputs\prompt.json" --evaluation "$W\rag\inputs\evaluation.json" --output "$W\rag\v1"
uv run --locked --extra dev ml-labs rag release evaluate --candidate "$W\rag\v1\candidate.json" --report "$W\rag\v1-report.json" | Out-Null
$LASTEXITCODE
(Get-Content "$W\rag\v1-report.json" -Raw | ConvertFrom-Json).summary | ConvertTo-Json
uv run --locked --extra dev ml-labs rag release activate --store "$W\rag\store" --candidate "$W\rag\v1\candidate.json" --report "$W\rag\v1-report.json"

{
  "candidate": ".ml-labs\\book-01\\rag\\v1\\candidate.json"
}
0
{
  "baseline_pass_rate": null,
  "cost_usd": 0,
  "input_tokens": null,
  "p95_latency_ms": 0.5670300015481189,
  "pass_rate": 1.0,
  "regressions": []
}
{
  "schema": "ml-labs-rag-release-v1",
  "candidate_id": "2ddd1503bb9756432d1b16e5fe3c8d7dc02ad5bdb1c8405f42572012ed354cca",
  "report_id": "0a93924f947130bf929b1471161db04a433f5edfab1d3e3e3d1d030086addf2b",
  "release_id": "bbe0e50fce4a0e8176aae32e641466553121f3398be34174581529af55879017"
}

uv run --locked --extra dev ml-labs rag release query --store "$W\rag\store" --query "model registry champion promotion"
uv run --locked --extra dev ml-labs rag release query --store "$W\rag\store" --query "quasar teleportation warranty"

{
  "query": "model registry champion promotion",
  "answer": "[doc-mlflow-registry] A model registry versions named models and supports aliases such as champion. A candidate should pass validation before promotion and deployment.",
  "citations": [ "doc-mlflow-registry" ],
  "retrieved": [ { "id": "doc-mlflow-registry", "title": "Model registry and promotion", "score": 0.42757112043785056, ... } ],
  "abstained": false,
  "citations_valid": true,
  "status": "answered",
  "answer_mode": "extractive_offline",
  "metrics": { "llm_calls": 0, "cost_usd": 0.0, "usage_source": "no_model_called", ... },
  "candidate_id": "2ddd1503bb9756432d1b16e5fe3c8d7dc02ad5bdb1c8405f42572012ed354cca",
  "release_id": "bbe0e50fce4a0e8176aae32e641466553121f3398be34174581529af55879017"
}
{
  "query": "quasar teleportation warranty",
  "answer": "No grounded answer is available in the selected corpus.",
  "citations": [],
  "retrieved": [],
  "abstained": true,
  "citations_valid": true,
  "status": "no_grounding",
  ...
  "release_id": "bbe0e50fce4a0e8176aae32e641466553121f3398be34174581529af55879017"
}

Set-Location $HOME\ml-labs-book\ml-platform-engineering-labs
$W = ".ml-labs\book-01"
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-base"
Remove-Item Env:MLFLOW_TRACKING_URI -ErrorAction SilentlyContinue
uv run --locked --extra dev ml-labs serve --rag-release "$W\rag\store" --host 127.0.0.1 --port 8003

function Show-RagRelease {
    $body = @{ query = "model registry champion promotion" } | ConvertTo-Json
    Invoke-RestMethod http://127.0.0.1:8003/rag/query -Method Post -ContentType application/json -Body $body |
        Select-Object status, release_id, candidate_id, @{ n = "citations"; e = { $_.citations -join "," } } |
        Format-List
}
Show-RagRelease

status       : answered
release_id   : bbe0e50fce4a0e8176aae32e641466553121f3398be34174581529af55879017
candidate_id : 2ddd1503bb9756432d1b16e5fe3c8d7dc02ad5bdb1c8405f42572012ed354cca
citations    : doc-mlflow-registry

$badConfig = Get-Content "$W\rag\inputs\config.json" -Raw | ConvertFrom-Json
$badConfig.min_score = 1.0
$badConfig | ConvertTo-Json -Depth 10 | Set-Content "$W\rag\bad-config.json" -Encoding utf8
uv run --locked --extra dev ml-labs rag release create --corpus "$W\rag\inputs\documents.jsonl" --config "$W\rag\bad-config.json" --prompt "$W\rag\inputs\prompt.json" --evaluation "$W\rag\inputs\evaluation.json" --output "$W\rag\bad"
uv run --locked --extra dev ml-labs rag release evaluate --candidate "$W\rag\bad\candidate.json" --baseline "$W\rag\v1\candidate.json" --min-pass-rate 0.3 --max-regression 0 --report "$W\rag\bad-report.json" | Out-Null
$LASTEXITCODE
(Get-Content "$W\rag\bad-report.json" -Raw | ConvertFrom-Json).summary | ConvertTo-Json
uv run --locked --extra dev ml-labs rag release activate --store "$W\rag\store" --candidate "$W\rag\bad\candidate.json" --report "$W\rag\bad-report.json"
$LASTEXITCODE
Show-RagRelease

{
  "candidate": ".ml-labs\\book-01\\rag\\bad\\candidate.json"
}
ml-labs: error: RAG release gate failed; report: .ml-labs\book-01\rag\bad-report.json
2
{
  "baseline_pass_rate": 1.0,
  "cost_usd": 0,
  "input_tokens": null,
  "p95_latency_ms": 0.6132099995738827,
  "pass_rate": 0.3333333333333333,
  "regressions": [
    "registry",
    "features"
  ]
}
ml-labs: error: RAG release gate failed; activation refused.
2

status       : answered
release_id   : bbe0e50fce4a0e8176aae32e641466553121f3398be34174581529af55879017
candidate_id : 2ddd1503bb9756432d1b16e5fe3c8d7dc02ad5bdb1c8405f42572012ed354cca
citations    : doc-mlflow-registry

$v2Config = Get-Content "$W\rag\inputs\config.json" -Raw | ConvertFrom-Json
$v2Config.top_k = 3
$v2Config | ConvertTo-Json -Depth 10 | Set-Content "$W\rag\v2-config.json" -Encoding utf8
uv run --locked --extra dev ml-labs rag release create --corpus "$W\rag\inputs\documents.jsonl" --config "$W\rag\v2-config.json" --prompt "$W\rag\inputs\prompt.json" --evaluation "$W\rag\inputs\evaluation.json" --output "$W\rag\v2"
uv run --locked --extra dev ml-labs rag release evaluate --candidate "$W\rag\v2\candidate.json" --baseline "$W\rag\v1\candidate.json" --report "$W\rag\v2-report.json" | Out-Null
$LASTEXITCODE
uv run --locked --extra dev ml-labs rag release activate --store "$W\rag\store" --candidate "$W\rag\v2\candidate.json" --report "$W\rag\v2-report.json"
Show-RagRelease

{
  "candidate": ".ml-labs\\book-01\\rag\\v2\\candidate.json"
}
0
{
  "schema": "ml-labs-rag-release-v1",
  "candidate_id": "6c6f8ff9ba9ab661af59a6a11fbf8619b0ed244c885245675fe034820b35f82d",
  "report_id": "761e2bff9c666bc49fda865c6884afa8dc0346c76a59be270334f6efdf5929d3",
  "release_id": "4dd49e008aa1847487f340a458a89bace725d6a8290b223e8fc6374f06c56c01"
}

status       : answered
release_id   : 4dd49e008aa1847487f340a458a89bace725d6a8290b223e8fc6374f06c56c01
candidate_id : 6c6f8ff9ba9ab661af59a6a11fbf8619b0ed244c885245675fe034820b35f82d
citations    : doc-mlflow-registry

uv run --locked --extra dev ml-labs rag release rollback --store "$W\rag\store"
Show-RagRelease

{
  "candidate_id": "2ddd1503bb9756432d1b16e5fe3c8d7dc02ad5bdb1c8405f42572012ed354cca",
  "release_id": "bbe0e50fce4a0e8176aae32e641466553121f3398be34174581529af55879017",
  "report_id": "0a93924f947130bf929b1471161db04a433f5edfab1d3e3e3d1d030086addf2b",
  "schema": "ml-labs-rag-release-v1"
}

status       : answered
release_id   : bbe0e50fce4a0e8176aae32e641466553121f3398be34174581529af55879017
candidate_id : 2ddd1503bb9756432d1b16e5fe3c8d7dc02ad5bdb1c8405f42572012ed354cca
citations    : doc-mlflow-registry

uv run --locked --extra dev ml-labs tools promptfoo release-config --candidate "$W\rag\v1\candidate.json" --output "$W\rag\promptfoo.yaml"
$env:PROMPTFOO_PYTHON = (Resolve-Path "$env:UV_PROJECT_ENVIRONMENT\Scripts\python.exe").Path
$env:PROMPTFOO_DISABLE_TELEMETRY = "1"
$env:PROMPTFOO_CONFIG_DIR = Join-Path (Resolve-Path $W).Path "promptfoo-home"
npm exec --yes --package=promptfoo@0.125.0 -- promptfoo eval --config "$W\rag\promptfoo.yaml" --no-cache --output "$W\rag\promptfoo-results.json"

{
  "config": ".ml-labs\\book-01\\rag\\promptfoo.yaml"
}
Cache is disabled.
Starting evaluation eval-JnZ-2026-11-07T12:33:01
Running 3 test cases (up to 4 at a time)...

┌────────────────────────────────────────────────────────────┬────────────────────────────────────────────────────────────┐
│ query                                                      │ [file://C:\Users\<사용자>\...\ml-platform-engineering-labs… │
│                                                            │ {{query}}                                                  │
├────────────────────────────────────────────────────────────┼────────────────────────────────────────────────────────────┤
│ model registry champion promotion                          │ [PASS] {"query": "model registry champion promotion",      │
│                                                            │ "answer": "[doc-mlflow-registry] A model registry versions │
│                                                            │ named models and supports aliases such as champion. A      │
│                                                            │ candidate should pass validation before promotion and      │
│                                                            │ deployment.", "citations": ["doc-...                       │
├────────────────────────────────────────────────────────────┼────────────────────────────────────────────────────────────┤
│ Feast offline online feature store                         │ [PASS] {"query": "Feast offline online feature store",     │
│                                                            │ "answer": "[doc-feast] Feast separates feature definitions │
│                                                            │ from materialized feature values. An offline store         │
│                                                            │ supports historical training retrieval and an online store │
│                                                            │ serves low-latency latest valu...                          │
├────────────────────────────────────────────────────────────┼────────────────────────────────────────────────────────────┤
│ quasar teleportation warranty                              │ [PASS] {"query": "quasar teleportation warranty",          │
│                                                            │ "answer": "No grounded answer is available in the selected │
│                                                            │ corpus.", "citations": [], "retrieved": [], "abstained":   │
│                                                            │ true, "citations_valid": true, "status": "no_grounding",   │
│                                                            │ "answer_mode": "extractive_offl...                         │
└────────────────────────────────────────────────────────────┴────────────────────────────────────────────────────────────┘
✓ Eval complete (ID: eval-JnZ-2026-11-07T12:33:01)

Results:
  ✓ 3 passed (100%)
  0 failed (0%)
  0 errors (0%)
Duration: 2s (concurrency: 4)

Writing output to .ml-labs\book-01\rag\promptfoo-results.json

$env:PROMPTFOO_DISABLE_TELEMETRY = "1"
$env:PROMPTFOO_CONFIG_DIR = Join-Path (Resolve-Path $W).Path "promptfoo-home"
npm exec --yes --package=promptfoo@0.125.0 -- promptfoo view --port 15500 --no

Set-Location $HOME\ml-labs-book\ml-platform-engineering-labs
$W = ".ml-labs\book-01"
Get-ChildItem $W | Select-Object Name
git rev-parse HEAD
$env:UV_PROJECT_ENVIRONMENT = ".ml-labs\book-env-base"
uv run --locked --extra dev pytest -q -p no:warnings

54 passed, 8 skipped in 19.99s