diff --git a/.github/PULL_REQUEST_TEMPLATE/MEP_template.md b/.github/PULL_REQUEST_TEMPLATE/MEP_template.md
index 06c74c6c..45b2a04a 100644
--- a/.github/PULL_REQUEST_TEMPLATE/MEP_template.md
+++ b/.github/PULL_REQUEST_TEMPLATE/MEP_template.md
@@ -6,4 +6,5 @@ version:
 
 - [ ] version number
 - [ ] CI/CD tests pass
-- [ ] Add message on data review slack channel 
\ No newline at end of file
+- [ ] Add message on data review slack channel
+- [ ] ⚠️ I will click on **Merge Pull Request** and **NOT** *on Squash and merge*
diff --git a/.github/PULL_REQUEST_TEMPLATE/MES_template.md b/.github/PULL_REQUEST_TEMPLATE/MES_template.md
index fe4672c8..c6722748 100644
--- a/.github/PULL_REQUEST_TEMPLATE/MES_template.md
+++ b/.github/PULL_REQUEST_TEMPLATE/MES_template.md
@@ -6,4 +6,5 @@ version:
 
 - [ ] version number
 - [ ] CI/CD tests pass
-- [ ] Add message on data review slack channel 
\ No newline at end of file
+- [ ] Add message on data review slack channel
+- [ ] ⚠️ I will click on **Merge Pull Request** and **NOT** *on Squash and merge*
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index af11a991..b7a2f8f9 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -1,8 +1,8 @@
 ### Select PR template in preview mode:
+* [Ticket](?expand=1&template=Generic_template.md)
 * [Hotfix](?expand=1&template=Hotfix_template.md)
 * [MES](?expand=1&template=MES_template.md)
 * [MEP](?expand=1&template=MEP_template.md)
-* [Other](?expand=1&template=Generic_template.md)
 
 
 
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 7efd3a1c..1f22de6a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -23,7 +23,7 @@ repos:
     stages: [commit]
   - id: lint-check
     name: Linting (ruff)
-    entry: ruff check
+    entry: ruff check --fix
     language: system
     types: [python]
     stages: [commit]
diff --git a/apps/fraud/compliance/api/Dockerfile b/apps/fraud/compliance/api/Dockerfile
index f438aa2d..2681304f 100644
--- a/apps/fraud/compliance/api/Dockerfile
+++ b/apps/fraud/compliance/api/Dockerfile
@@ -1,15 +1,23 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
+COPY --from=ghcr.io/astral-sh/uv:0.3.4 /uv /bin/uv
 
-ENV PYTHONUNBUFFERED True
-
-ENV APP_HOME /app
+ENV PYTHONUNBUFFERED=True
+ENV APP_HOME=/app
 WORKDIR $APP_HOME
+
 # Install libraries
 COPY ./requirements.txt ./
-RUN pip install --no-cache-dir -r requirements.txt
-COPY ./src/main.py .
+RUN uv pip install -r requirements.txt --system
 COPY ./src .
-# Copy local code to the container
-COPY ./src/pcpapillon ./pcpapillon
 
-CMD exec gunicorn -k uvicorn.workers.UvicornWorker --bind :$PORT --workers 1 --threads 10 --preload --timeout 0 main:app
\ No newline at end of file
+# Conditionally copy the Google Cloud credentials file based on the argument
+ARG LOCAL=false
+ARG GOOGLE_CLOUD_PROJECT
+ENV GOOGLE_CLOUD_PROJECT=${GOOGLE_CLOUD_PROJECT}
+RUN if [ "$LOCAL" = "true" ]; then \
+    mkdir -p /root/.config/gcloud && \
+    cp ./application_default_credentials.json /root/.config/gcloud/application_default_credentials.json; \
+    fi
+
+# Run the container
+CMD exec gunicorn -k uvicorn.workers.UvicornWorker --bind :$PORT --workers 1 --threads 10 --preload --timeout 0 main:app
diff --git a/apps/fraud/compliance/api/Makefile b/apps/fraud/compliance/api/Makefile
index 4a66fd99..1af179fb 100644
--- a/apps/fraud/compliance/api/Makefile
+++ b/apps/fraud/compliance/api/Makefile
@@ -1,5 +1,5 @@
 init:
-	pyenv install 3.9 -s
+	pyenv install 3.10 -s
 	@eval "$$(pyenv init -)" && pyenv virtualenv 3.9 api-data-fraud-compliance && pyenv local api-data-fraud-compliance
 
 install_with_uv:
diff --git a/apps/fraud/compliance/api/deploy_local.sh b/apps/fraud/compliance/api/deploy_local.sh
old mode 100644
new mode 100755
index 481dcd53..b8a7363a
--- a/apps/fraud/compliance/api/deploy_local.sh
+++ b/apps/fraud/compliance/api/deploy_local.sh
@@ -1,2 +1,7 @@
-docker build -t pcpapillon --progress=plain .
-docker run -it -p 8080:8080 pcpapillon
\ No newline at end of file
+if [ ! -f ~/.config/gcloud/application_default_credentials.json ]; then
+    gcloud auth application-default login
+fi
+cp ~/.config/gcloud/application_default_credentials.json src/application_default_credentials.json
+docker build -t pcpapillon --build-arg LOCAL=true --build-arg GOOGLE_CLOUD_PROJECT=passculture-data-ehp .
+rm src/application_default_credentials.json
+docker run -p 8080:8080 -e "PORT=8080" pcpapillon
diff --git a/apps/fraud/compliance/api/requirements.in b/apps/fraud/compliance/api/requirements.in
index 1d5935f4..fd5ba553 100644
--- a/apps/fraud/compliance/api/requirements.in
+++ b/apps/fraud/compliance/api/requirements.in
@@ -1,4 +1,6 @@
-catboost==1.2
+apscheduler==3.10.4
+catboost==1.2.5
+cloudpickle==3.0.0
 dataclass-wizard==0.22.2
 fastapi-cloud-logging==1.1.0
 fastapi-versioning==0.10.0
@@ -8,14 +10,19 @@ google-cloud-logging==3.5.0
 google-cloud-secret-manager==2.2.0
 google-cloud-storage==2.9.0
 gunicorn==20.0.4
+ipython==8.24.0
 loguru==0.7.0
-mlflow==2.3.1
-numpy==1.22
+matplotlib==3.9.2
+mlflow==2.10.2
+numpy==1.26.4
 oauth2client==4.1.3
-pandas==2.0.1
+pandas==2.2.0
+psutil==5.9.8
 python-jose==3.3.0
 python-multipart==0.0.6
 ruff==0.4.7
+scikit-learn==1.5.0
+scipy==1.13.1
 sentence-transformers==2.2.2
-shap==0.41.0
+shap==0.46.0
 uvicorn==0.22.0
diff --git a/apps/fraud/compliance/api/requirements.txt b/apps/fraud/compliance/api/requirements.txt
index 2c126840..27eaddd2 100644
--- a/apps/fraud/compliance/api/requirements.txt
+++ b/apps/fraud/compliance/api/requirements.txt
@@ -4,12 +4,15 @@ alembic==1.13.2
     # via mlflow
 anyio==4.4.0
     # via starlette
+apscheduler==3.10.4
+    # via -r requirements.in
+asttokens==2.4.1
+    # via stack-data
 blinker==1.8.2
     # via flask
 cachetools==5.4.0
     # via google-auth
-apscheduler==3.10.4
-catboost==1.2
+catboost==1.2.5
     # via -r requirements.in
 certifi==2024.7.4
     # via requests
@@ -17,23 +20,23 @@ charset-normalizer==3.3.2
     # via requests
 click==8.1.7
     # via
-    #   databricks-cli
     #   flask
     #   mlflow
     #   nltk
     #   uvicorn
-cloudpickle==2.2.1
+cloudpickle==3.0.0
     # via
+    #   -r requirements.in
     #   mlflow
     #   shap
 contourpy==1.2.1
     # via matplotlib
 cycler==0.12.1
     # via matplotlib
-databricks-cli==0.18.0
-    # via mlflow
 dataclass-wizard==0.22.2
     # via -r requirements.in
+decorator==5.1.1
+    # via ipython
 docker==6.1.3
     # via mlflow
 ecdsa==0.19.0
@@ -41,7 +44,11 @@ ecdsa==0.19.0
 entrypoints==0.4
     # via mlflow
 exceptiongroup==1.2.2
-    # via anyio
+    # via
+    #   anyio
+    #   ipython
+executing==2.0.1
+    # via stack-data
 fastapi==0.95.1
     # via
     #   -r requirements.in
@@ -152,14 +159,13 @@ idna==3.7
     #   anyio
     #   requests
 importlib-metadata==6.11.0
-    # via
-    #   flask
-    #   markdown
-    #   mlflow
-importlib-resources==6.4.0
-    # via matplotlib
+    # via mlflow
+ipython==8.24.0
+    # via -r requirements.in
 itsdangerous==2.2.0
     # via flask
+jedi==0.19.1
+    # via ipython
 jinja2==3.1.4
     # via
     #   flask
@@ -186,11 +192,14 @@ markupsafe==2.1.5
     #   jinja2
     #   mako
     #   werkzeug
-matplotlib==3.8.4
+matplotlib==3.9.2
     # via
+    #   -r requirements.in
     #   catboost
     #   mlflow
-mlflow==2.3.1
+matplotlib-inline==0.1.7
+    # via ipython
+mlflow==2.10.2
     # via -r requirements.in
 mpmath==1.3.0
     # via sympy
@@ -200,7 +209,7 @@ nltk==3.8.1
     # via sentence-transformers
 numba==0.60.0
     # via shap
-numpy==1.22.0
+numpy==1.26.4
     # via
     #   -r requirements.in
     #   catboost
@@ -249,8 +258,6 @@ nvidia-nvtx-cu12==12.1.105
     # via torch
 oauth2client==4.1.3
     # via -r requirements.in
-oauthlib==3.2.2
-    # via databricks-cli
 packaging==23.2
     # via
     #   docker
@@ -260,18 +267,24 @@ packaging==23.2
     #   plotly
     #   shap
     #   transformers
-pandas==2.0.1
+pandas==2.2.0
     # via
     #   -r requirements.in
     #   catboost
     #   mlflow
     #   shap
+parso==0.8.4
+    # via jedi
+pexpect==4.9.0
+    # via ipython
 pillow==10.4.0
     # via
     #   matplotlib
     #   torchvision
 plotly==5.22.0
     # via catboost
+prompt-toolkit==3.0.47
+    # via ipython
 proto-plus==1.24.0
     # via
     #   google-cloud-appengine-logging
@@ -288,6 +301,12 @@ protobuf==3.20.3
     #   grpcio-status
     #   mlflow
     #   proto-plus
+psutil==5.9.8
+    # via -r requirements.in
+ptyprocess==0.7.0
+    # via pexpect
+pure-eval==0.2.3
+    # via stack-data
 pyarrow==11.0.0
     # via mlflow
 pyasn1==0.6.0
@@ -302,8 +321,8 @@ pyasn1-modules==0.4.0
     #   oauth2client
 pydantic==1.10.17
     # via fastapi
-pyjwt==2.8.0
-    # via databricks-cli
+pygments==2.18.0
+    # via ipython
 pyparsing==3.1.2
     # via
     #   httplib2
@@ -318,6 +337,7 @@ python-multipart==0.0.6
     # via -r requirements.in
 pytz==2023.4
     # via
+    #   apscheduler
     #   mlflow
     #   pandas
 pyyaml==6.0.1
@@ -334,7 +354,6 @@ regex==2024.5.15
     #   transformers
 requests==2.32.3
     # via
-    #   databricks-cli
     #   docker
     #   google-api-core
     #   google-cloud-storage
@@ -350,13 +369,15 @@ ruff==0.4.7
     # via -r requirements.in
 safetensors==0.4.3
     # via transformers
-scikit-learn==1.5.1
+scikit-learn==1.5.0
     # via
+    #   -r requirements.in
     #   mlflow
     #   sentence-transformers
     #   shap
-scipy==1.11.4
+scipy==1.13.1
     # via
+    #   -r requirements.in
     #   catboost
     #   mlflow
     #   scikit-learn
@@ -368,18 +389,19 @@ sentencepiece==0.2.0
     # via sentence-transformers
 setuptools==70.3.0
     # via gunicorn
-shap==0.41.0
+shap==0.46.0
     # via -r requirements.in
 six==1.16.0
     # via
+    #   apscheduler
+    #   asttokens
     #   catboost
-    #   databricks-cli
     #   ecdsa
     #   google-api-python-client
     #   oauth2client
     #   python-dateutil
     #   querystring-parser
-slicer==0.0.7
+slicer==0.0.8
     # via shap
 smmap==5.0.1
     # via gitdb
@@ -391,14 +413,14 @@ sqlalchemy==2.0.31
     #   mlflow
 sqlparse==0.5.1
     # via mlflow
+stack-data==0.6.3
+    # via ipython
 starlette==0.26.1
     # via
     #   fastapi
     #   fastapi-versioning
 sympy==1.13.0
     # via torch
-tabulate==0.9.0
-    # via databricks-cli
 tenacity==8.5.0
     # via plotly
 threadpoolctl==3.5.0
@@ -418,6 +440,10 @@ tqdm==4.66.4
     #   sentence-transformers
     #   shap
     #   transformers
+traitlets==5.14.3
+    # via
+    #   ipython
+    #   matplotlib-inline
 transformers==4.42.4
     # via sentence-transformers
 triton==2.3.1
@@ -426,28 +452,28 @@ typing-extensions==4.12.2
     # via
     #   alembic
     #   anyio
-    #   dataclass-wizard
     #   huggingface-hub
+    #   ipython
     #   pydantic
     #   sqlalchemy
-    #   starlette
     #   torch
 tzdata==2024.1
     # via pandas
+tzlocal==5.2
+    # via apscheduler
 uritemplate==3.0.1
     # via google-api-python-client
 urllib3==2.2.2
     # via
-    #   databricks-cli
     #   docker
     #   requests
 uvicorn==0.22.0
     # via -r requirements.in
+wcwidth==0.2.13
+    # via prompt-toolkit
 websocket-client==1.8.0
     # via docker
 werkzeug==3.0.3
     # via flask
 zipp==3.19.2
-    # via
-    #   importlib-metadata
-    #   importlib-resources
+    # via importlib-metadata
diff --git a/apps/fraud/compliance/api/src/pcpapillon/core/compliance/__init__.py b/apps/fraud/compliance/api/src/pcpapillon/core/compliance/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/apps/fraud/compliance/api/src/pcpapillon/core/compliance/compliance_model.py b/apps/fraud/compliance/api/src/pcpapillon/core/compliance/compliance_model.py
deleted file mode 100644
index 793f8965..00000000
--- a/apps/fraud/compliance/api/src/pcpapillon/core/compliance/compliance_model.py
+++ /dev/null
@@ -1,120 +0,0 @@
-from dataclasses import dataclass
-from typing import Union
-
-import mlflow
-from main import custom_logger
-from pcpapillon.core.compliance.predict import get_prediction_and_main_contribution
-from pcpapillon.core.compliance.preprocess import preprocess
-from pcpapillon.utils.config_handler import ConfigHandler
-from pcpapillon.utils.constants import APIType, ModelName, ModelType
-from pcpapillon.utils.env_vars import (
-    IS_API_LOCAL,
-)
-from pcpapillon.utils.model_handler import ModelHandler
-from sentence_transformers import SentenceTransformer
-
-
-@dataclass
-class ModelData:
-    classification_model: Union[mlflow.pyfunc.PythonModel, SentenceTransformer]
-    model_identifier: str
-    preprocessing_models: dict[str, SentenceTransformer]
-
-
-class ComplianceModel:
-    MODEL_NAME = ModelName.COMPLIANCE
-    MODEL_TYPE = ModelType.LOCAL if IS_API_LOCAL else ModelType.DEFAULT
-    PREPROC_MODEL_TYPE = MODEL_TYPE.PREPROCESSING
-
-    def __init__(self):
-        self.api_config = ConfigHandler.get_api_config(APIType.DEFAULT)
-        self.model_config = ConfigHandler.get_model_config(ModelType.DEFAULT)
-        self.model_handler = ModelHandler()
-        model_data = self._load_models()
-        self.classfier_model = model_data.classification_model
-        self.classifier_model_identifier = model_data.model_identifier
-        self.prepoc_models = model_data.preprocessing_models
-
-    def _load_models(
-        self,
-    ) -> ModelData:
-        custom_logger.info("load classification model..")
-        catboost_model_with_metadata = (
-            self.model_handler.get_model_with_metadata_by_name(
-                model_name=self.MODEL_NAME, model_type=self.MODEL_TYPE
-            )
-        )
-
-        custom_logger.info("load preprocessings model..")
-        prepoc_models = {}
-        for (
-            feature_type,
-            sentence_transformer_name,
-        ) in self.model_config.pre_trained_model_for_embedding_extraction.items():
-            prepoc_models[feature_type] = (
-                self.model_handler.get_model_with_metadata_by_name(
-                    model_name=sentence_transformer_name,
-                    model_type=self.PREPROC_MODEL_TYPE,
-                ).model
-            )
-
-        custom_logger.info(
-            f"Preprocessing models for {self.MODEL_NAME} : {prepoc_models}"
-        )
-        return ModelData(
-            classification_model=catboost_model_with_metadata.model,
-            model_identifier=catboost_model_with_metadata.model_identifier,
-            preprocessing_models=prepoc_models,
-        )
-
-    def predict(self, data):
-        """
-        Predicts the class labels for the given data using the trained classifier model.
-
-        Args:
-            api_config (dict): Configuration parameters for the API.
-            model_config (dict): Configuration parameters for the model.
-            data (list): Input data to be predicted.
-
-        Returns:
-            tuple: A tuple containing the predicted class labels and the main contribution.
-                offer validition probability
-                offer rejection probability (=1-proba_val)
-                main features contributing to increase validation probability
-                main features contributing to reduce validation probability
-        """
-
-        # Preprocess the data and the embedder
-        pool, data_w_emb = preprocess(
-            self.api_config, self.model_config, data, self.prepoc_models
-        )
-
-        # Run the prediction
-        return get_prediction_and_main_contribution(
-            self.classfier_model, data_w_emb, pool
-        )
-
-    def _is_newer_model_available(self) -> bool:
-        return (
-            self.classifier_model_identifier
-            != self.model_handler.get_model_hash_from_mlflow(self.MODEL_NAME)
-        )
-
-    def reload_model_if_newer_is_available(self):
-        custom_logger.debug("Checking if newer model is available...")
-        if self._is_newer_model_available():
-            custom_logger.info("New model available: Loading it...")
-            classfier_model_with_metadata = (
-                self.model_handler.get_model_with_metadata_by_name(
-                    model_name=self.MODEL_NAME, model_type=self.MODEL_TYPE
-                )
-            )
-            self.classfier_model, self.classifier_model_identifier = (
-                classfier_model_with_metadata.model,
-                classfier_model_with_metadata.model_identifier,
-            )
-            custom_logger.info(
-                f"...New model loaded with hash {self.classifier_model_identifier}"
-            )
-        else:
-            custom_logger.debug("...No newer model available")
diff --git a/apps/fraud/compliance/api/src/pcpapillon/core/compliance/extract_embedding.py b/apps/fraud/compliance/api/src/pcpapillon/core/compliance/extract_embedding.py
deleted file mode 100644
index 6b6310fb..00000000
--- a/apps/fraud/compliance/api/src/pcpapillon/core/compliance/extract_embedding.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import contextlib
-import io
-
-import numpy as np
-import requests
-from PIL import Image
-
-
-def extract_embedding(data, params, prepoc_models):
-    """
-    Extract embedding with pretrained models
-    Two types available:
-    - image :
-        - Input: list of urls
-    - text  :
-        - Input: list of string
-    Params template:
-    [
-        {"name": "offer_name", "type": "text"},
-        {"name": "offer_description", "type": "text"},
-        {"name": "image_url", "type": "image"},
-    ]
-    """
-    for feature in params:
-        if feature["type"] == "image":
-            model = prepoc_models[feature["type"]]
-            url = data[feature["name"]]
-            data["image_embedding"] = _encode_img_from_url(model, url)
-            with contextlib.suppress(KeyError):
-                del data[feature["name"]]
-        if feature["type"] == "text":
-            model = prepoc_models[feature["type"]]
-            embedding = model.encode(data[feature["name"]])
-            data[f"""{feature["name"]}_embedding"""] = embedding
-
-    return data
-
-
-def _encode_img_from_url(model, url):
-    """
-    Encode image with pre-trained model from url
-
-    inputs:
-        - model : HugginFaces pre-trained model using Sentence-Transformers
-        - url : string of image url
-    """
-    offer_img_embs = []
-    try:
-        img_emb = model.encode(Image.open(io.BytesIO(requests.get(url).content)))
-        offer_img_embs = img_emb
-    except Exception:
-        offer_img_embs = np.array([0] * 512)
-    return offer_img_embs
diff --git a/apps/fraud/compliance/api/src/pcpapillon/core/compliance/predict.py b/apps/fraud/compliance/api/src/pcpapillon/core/compliance/predict.py
deleted file mode 100644
index cb82ff10..00000000
--- a/apps/fraud/compliance/api/src/pcpapillon/core/compliance/predict.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from heapq import nlargest, nsmallest
-
-import shap
-
-
-def get_prediction_and_main_contribution(model, data_w_emb, pool):
-    """
-    Prediction:
-        Predict validation/rejection probability for a given input as catboost pool
-        inputs:
-            - pool: Catboost pool with offer features
-            - model: Catboost custom trained model
-        outputs:
-            proba_val: offer validition probability
-            proba_rej: offer rejection probability (=1-proba_val)
-    Main contribution:
-        Extract prediction main contribution features from shap values
-        inputs:
-            - model: Catboost custom trained model
-            - data: json with offer features
-            - pool: Catboost with offer features
-        outputs:
-            top_val: main features contributing to increase validation probability
-            top_reg: main features contributing to reduce validation probability
-    """
-    proba_predicted = list(
-        model.predict(
-            pool,
-            prediction_type="Probability",
-            ntree_start=0,
-            ntree_end=0,
-            thread_count=1,
-            verbose=None,
-        )[0]
-    )
-    proba_rej = proba_predicted[0] * 100
-    proba_val = proba_predicted[1] * 100
-    top_val, top_rej = _get_prediction_main_contribution(model, data_w_emb, pool)
-    return proba_val, proba_rej, top_val, top_rej
-
-
-def _get_prediction_main_contribution(model, data, pool):
-    explainer = shap.Explainer(model, link=shap.links.logit)
-    shap_values = explainer.shap_values(pool)
-    top_val, top_rej = __get_contribution_from_shap_values(shap_values, data)
-    return top_val, top_rej
-
-
-def __get_contribution_from_shap_values(shap_values, data):
-    topk_validation_factor = []
-    topk_rejection_factor = []
-    data_keys = list(data.keys())
-    # for i in range(len(data)):
-    individual_shap_values = list(shap_values[0, :])
-    klargest = nlargest(3, individual_shap_values)
-    ksmallest = nsmallest(3, individual_shap_values)
-    topk_validation_factor = [
-        data_keys[individual_shap_values.index(max_val)] for max_val in klargest
-    ]
-
-    topk_rejection_factor = [
-        data_keys[individual_shap_values.index(min_val)] for min_val in ksmallest
-    ]
-    return topk_validation_factor, topk_rejection_factor
diff --git a/apps/fraud/compliance/api/src/pcpapillon/core/compliance/preprocess.py b/apps/fraud/compliance/api/src/pcpapillon/core/compliance/preprocess.py
deleted file mode 100644
index 804abbcc..00000000
--- a/apps/fraud/compliance/api/src/pcpapillon/core/compliance/preprocess.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import contextlib
-import itertools
-
-from catboost import Pool
-from pcpapillon.core.compliance.extract_embedding import extract_embedding
-
-
-def preprocess(api_config, model_config, data, prepoc_models):
-    """
-    Preprocessing steps:
-        - prepare features
-        - convert json data to catboost pool
-    """
-    data_clean = prepare_features(data, api_config.preprocess_features_type)
-    data_w_emb = extract_embedding(
-        data_clean, api_config.features_to_extract_embedding, prepoc_models
-    )
-
-    scoring_features = list(
-        itertools.chain.from_iterable(model_config.catboost_features_types.values())
-    )
-    data_w_emb_clean = {}
-    for feature in scoring_features:
-        data_w_emb_clean[feature] = data_w_emb[feature]
-    pool = convert_data_to_catboost_pool(
-        data_w_emb_clean, model_config.catboost_features_types
-    )
-    return pool, data_w_emb
-
-
-def prepare_features(data, params):
-    """
-    Prepare features:
-        - Fill integer null values with 0
-        - Fill string null values with "none"
-        - Convert boolean columns to int
-    """
-    with contextlib.suppress(KeyError):
-        del data["offer_id"]
-
-    for key in data:
-        if key in params["text_features"]:
-            data[key] = "" if data[key] is None else str(data[key])
-        if key in params["numerical_features"]:
-            data[key] = 0 if data[key] is None else int(data[key])
-    if "macro_text" in params:
-        semantic_content = " ".join(
-            [semantic_feature.lower() for semantic_feature in params["macro_text"]]
-        )
-        data["semantic_content"] = semantic_content
-    return data
-
-
-def convert_data_to_catboost_pool(data, features_type_dict):
-    """
-    Convert json data to catboost pool:
-        - inputs:
-            - Features names: list of features name (same order as list of features)
-            - cat_features: list of categorical features names
-            - text_features: list of text features names
-            - embedding_features: list of embedding features names
-        - output:
-            - catboost pool
-    """
-    data_input = [list(data.values())]
-    pool = Pool(
-        data=data_input,
-        feature_names=list(data.keys()),
-        cat_features=features_type_dict["cat_features"],
-        text_features=features_type_dict["text_features"],
-        embedding_features=features_type_dict["embedding_features"],
-    )
-    return pool
diff --git a/apps/fraud/compliance/api/src/pcpapillon/core/compliance_model.py b/apps/fraud/compliance/api/src/pcpapillon/core/compliance_model.py
new file mode 100644
index 00000000..40e0e90f
--- /dev/null
+++ b/apps/fraud/compliance/api/src/pcpapillon/core/compliance_model.py
@@ -0,0 +1,63 @@
+from main import custom_logger
+from pcpapillon.utils.constants import ModelName, ModelType
+from pcpapillon.utils.data_model import ComplianceInput, ComplianceOutput
+from pcpapillon.utils.model_handler import ModelHandler, ModelWithMetadata
+
+
+class ComplianceModel:
+    MODEL_NAME = ModelName.COMPLIANCE
+    MODEL_TYPE = ModelType.DEFAULT
+
+    def __init__(self):
+        self.model_handler = ModelHandler()
+        model_data = self._load_models()
+        self.model = model_data.model
+        self.model_identifier = model_data.model_identifier
+
+    def _load_models(
+        self,
+    ) -> ModelWithMetadata:
+        custom_logger.info(f"load {self.MODEL_NAME} model..")
+        return self.model_handler.get_model_with_metadata_by_name(
+            model_name=self.MODEL_NAME, model_type=self.MODEL_TYPE
+        )
+
+    def predict(self, data: ComplianceInput) -> ComplianceOutput:
+        """
+        Predicts the class labels for the given data using the trained classifier model.
+
+        Args:
+            data (ComplianceInput): Input data to be predicted.
+
+        Returns:
+            ComplianceOutput: An object containing the predicted class labels
+                and the main contributions.
+        """
+        predictions = self.model.predict(data.dict())
+        return ComplianceOutput(
+            offer_id=data.offer_id,
+            probability_validated=predictions.probability_validated,
+            validation_main_features=predictions.validation_main_features,
+            probability_rejected=predictions.probability_rejected,
+            rejection_main_features=predictions.rejection_main_features,
+        )
+
+    def _is_newer_model_available(self) -> bool:
+        return self.model_identifier != self.model_handler.get_model_hash_from_mlflow(
+            self.MODEL_NAME
+        )
+
+    def reload_model_if_newer_is_available(self):
+        custom_logger.debug("Checking if newer model is available...")
+        if self._is_newer_model_available():
+            custom_logger.info("New model available: Loading it...")
+            new_model = self.model_handler.get_model_with_metadata_by_name(
+                model_name=self.MODEL_NAME, model_type=self.MODEL_TYPE
+            )
+            self.classfier_model, self.model_identifier = (
+                new_model.model,
+                new_model.model_identifier,
+            )
+            custom_logger.info(f"...New model loaded with hash {self.model_identifier}")
+        else:
+            custom_logger.debug("...No newer model available")
diff --git a/apps/fraud/compliance/api/src/pcpapillon/core/offer_categorisation/offer_categorisation_model.py b/apps/fraud/compliance/api/src/pcpapillon/core/offer_categorisation_model.py
similarity index 96%
rename from apps/fraud/compliance/api/src/pcpapillon/core/offer_categorisation/offer_categorisation_model.py
rename to apps/fraud/compliance/api/src/pcpapillon/core/offer_categorisation_model.py
index d234da61..1b4ecc1a 100644
--- a/apps/fraud/compliance/api/src/pcpapillon/core/offer_categorisation/offer_categorisation_model.py
+++ b/apps/fraud/compliance/api/src/pcpapillon/core/offer_categorisation_model.py
@@ -11,9 +11,6 @@
 from pcpapillon.utils.data_model import (
     OfferCategorisationInput,
 )
-from pcpapillon.utils.env_vars import (
-    IS_API_LOCAL,
-)
 from pcpapillon.utils.model_handler import ModelHandler
 from sentence_transformers import SentenceTransformer
 
@@ -22,7 +19,7 @@ class OfferCategorisationModel:
     LABEL_MAPPING_PATH = "pcpapillon/data/offer_categorisation_label_mapping.parquet"  # Will be removed when model predict is updated
     PREPROCESSOR_NAME = "sentence-transformers/all-MiniLM-L6-v2"  # Will be removed when model predict is updated
     MODEL_NAME = ModelName.OFFER_CATEGORISATION
-    MODEL_TYPE = ModelType.LOCAL if IS_API_LOCAL else ModelType.DEFAULT
+    MODEL_TYPE = ModelType.DEFAULT
     PREPROC_MODEL_TYPE = ModelType.PREPROCESSING
 
     def __init__(self):
diff --git a/apps/fraud/compliance/api/src/pcpapillon/utils/constants.py b/apps/fraud/compliance/api/src/pcpapillon/utils/constants.py
index 3821f879..de2e82fd 100644
--- a/apps/fraud/compliance/api/src/pcpapillon/utils/constants.py
+++ b/apps/fraud/compliance/api/src/pcpapillon/utils/constants.py
@@ -1,10 +1,5 @@
 from enum import Enum
 
-from pcpapillon.utils.env_vars import (
-    COMPLIANCE_MODEL_PATH,
-    OFFER_CATEGORISATION_MODEL_PATH,
-)
-
 
 class ModelName(Enum):
     """
@@ -20,7 +15,6 @@ class ModelType(Enum):
     Enum class for model types
     """
 
-    LOCAL = "local"
     DEFAULT = "default"
     PREPROCESSING = "custom_sentence_transformer"
 
@@ -40,9 +34,3 @@ class APIType(Enum):
     """
 
     DEFAULT = "default"
-
-
-MODEL_PATHS = {
-    ModelName.COMPLIANCE: COMPLIANCE_MODEL_PATH,
-    ModelName.OFFER_CATEGORISATION: OFFER_CATEGORISATION_MODEL_PATH,
-}
diff --git a/apps/fraud/compliance/api/src/pcpapillon/utils/env_vars.py b/apps/fraud/compliance/api/src/pcpapillon/utils/env_vars.py
index 1d9a8d28..ab39c63f 100644
--- a/apps/fraud/compliance/api/src/pcpapillon/utils/env_vars.py
+++ b/apps/fraud/compliance/api/src/pcpapillon/utils/env_vars.py
@@ -1,27 +1,26 @@
 import contextvars
 import os
 
-from google.auth.exceptions import DefaultCredentialsError
 from google.cloud import secretmanager
 
 
-def access_secret(project_id, secret_id, version_id="latest", default=None):
-    try:
-        client = secretmanager.SecretManagerServiceClient()
-        name = f"projects/{project_id}/secrets/{secret_id}/versions/{version_id}"
-        response = client.access_secret_version(request={"name": name})
-        return response.payload.data.decode("UTF-8")
-    except DefaultCredentialsError:
-        return default
+def access_secret(project_id, secret_id):
+    client = secretmanager.SecretManagerServiceClient()
+    name = f"projects/{project_id}/secrets/{secret_id}/versions/latest"
+    response = client.access_secret_version(request={"name": name})
+    return response.payload.data.decode("UTF-8")
 
 
 # Project vars
 GCS_BUCKET = os.environ.get("GCS_BUCKET", "data-bucket-dev")
 GCP_PROJECT = os.environ.get("GCP_PROJECT", "passculture-data-ehp")
 ENV_SHORT_NAME = os.environ.get("ENV_SHORT_NAME", "dev")
+SA_ACCOUNT = f"algo-training-{ENV_SHORT_NAME}"
+
 # API_LOCAL is string to match terraform boolean handling
 API_LOCAL = os.environ.get("API_LOCAL", False)
 IS_API_LOCAL = API_LOCAL == "True"
+
 # API
 API_SECRET_KET_SECRET_ID = os.environ.get(
     "API_SECRET_KET_SECRET_ID", "api-papillon-auth-secret-key-dev"
@@ -30,15 +29,10 @@ def access_secret(project_id, secret_id, version_id="latest", default=None):
 HASH_ALGORITHM = os.environ.get("VALIDATION_LOGIN_KEY", "HS256")
 LOGIN_TOKEN_EXPIRATION = os.environ.get("LOGIN_TOKEN_EXPIRATION", 30)
 
-if IS_API_LOCAL:
-    API_USER = "user_local"
-    API_PWD = "pwd_local"
-
-else:
-    API_USER_SECRET_ID = os.environ.get("API_USER_SECRET_ID", "api-papillon-user-dev")
-    API_USER = access_secret(GCP_PROJECT, API_USER_SECRET_ID)
-    API_PWD_SECRET_ID = os.environ.get("API_PWD_SECRET_ID", "api-papillon-password-dev")
-    API_PWD = access_secret(GCP_PROJECT, API_PWD_SECRET_ID)
+API_USER_SECRET_ID = os.environ.get("API_USER_SECRET_ID", "api-papillon-user-dev")
+API_PWD_SECRET_ID = os.environ.get("API_PWD_SECRET_ID", "api-papillon-password-dev")
+API_USER = access_secret(GCP_PROJECT, API_USER_SECRET_ID)
+API_PWD = access_secret(GCP_PROJECT, API_PWD_SECRET_ID)
 users_db = {
     API_USER: {
         "username": API_USER,
@@ -46,10 +40,11 @@ def access_secret(project_id, secret_id, version_id="latest", default=None):
         "disabled": False,
     }
 }
-# Configs
+
 # logger
 cloud_trace_context = contextvars.ContextVar("cloud_trace_context", default="")
 http_request_context = contextvars.ContextVar("http_request_context", default={})
+
 # MLFlow
 MLFLOW_SECRET_ID = os.environ.get("MLFLOW_SECRET_ID", "mlflow_client_id")
 MLFLOW_CLIENT_ID = access_secret(GCP_PROJECT, MLFLOW_SECRET_ID)
@@ -59,10 +54,3 @@ def access_secret(project_id, secret_id, version_id="latest", default=None):
 # Model metadata
 MODEL_DEFAULT = os.environ.get("MODEL_DEFAULT", "compliance_model_dev")
 MODEL_STAGE = os.environ.get("MODEL_STAGE", "Production")
-COMPLIANCE_MODEL_PATH = os.environ.get(
-    "COMPLIANCE_MODEL_PATH", "pcpapillon/local_model/compliance_model.cb"
-)
-OFFER_CATEGORISATION_MODEL_PATH = os.environ.get(
-    "OFFER_CATEGORISATION_MODEL_PATH",
-    "pcpapillon/local_model/offer_categorisation_model.cb",
-)
diff --git a/apps/fraud/compliance/api/src/pcpapillon/utils/model_handler.py b/apps/fraud/compliance/api/src/pcpapillon/utils/model_handler.py
index 83856849..324283fc 100644
--- a/apps/fraud/compliance/api/src/pcpapillon/utils/model_handler.py
+++ b/apps/fraud/compliance/api/src/pcpapillon/utils/model_handler.py
@@ -1,18 +1,16 @@
 import hashlib
 import pickle
+import re
 from dataclasses import dataclass
 from typing import Union
 
 import mlflow
 import mlflow.pyfunc
-from catboost import CatBoostClassifier
 from main import custom_logger
 from mlflow import MlflowClient
-from pcpapillon.utils.constants import MODEL_PATHS, ModelName, ModelType
+from pcpapillon.utils.constants import ModelName, ModelType
 from pcpapillon.utils.env_vars import (
     ENV_SHORT_NAME,
-    IS_API_LOCAL,
-    MLFLOW_CLIENT_ID,
 )
 from pcpapillon.utils.tools import connect_remote_mlflow
 from sentence_transformers import SentenceTransformer
@@ -25,38 +23,37 @@ class ModelWithMetadata:
 
 
 class ModelHandler:
-    MODEL_STAGE = "Production"
-
     def __init__(self) -> None:
-        if not IS_API_LOCAL:
-            custom_logger.info("Connecting to mlflow")
-            connect_remote_mlflow(MLFLOW_CLIENT_ID)
-            self.mlflow_client = MlflowClient()
-        else:
-            custom_logger.info("Local API, no mlflow client")
-            self.mlflow_client = None
+        custom_logger.info("Connecting to mlflow")
+        connect_remote_mlflow()
+        self.mlflow_client = MlflowClient()
 
     def get_model_with_metadata_by_name(
-        self, model_name, model_type=ModelType.DEFAULT
+        self, model_name: str, model_type=ModelType.DEFAULT
     ) -> ModelWithMetadata:
-        if model_type == ModelType.DEFAULT:
-            loaded_model = mlflow.catboost.load_model(
-                model_uri=f"models:/{self._get_mlflow_model_name(model_name)}/{self.MODEL_STAGE}"
+        if model_name == ModelName.COMPLIANCE:
+            loaded_model = mlflow.pyfunc.load_model(
+                model_uri=f"models:/{self._get_mlflow_model_name(model_name)}"
             )
             model_hash = self.get_model_hash_from_mlflow(model_name=model_name)
-            return ModelWithMetadata(model=loaded_model, model_identifier=model_hash)
-
-        elif model_type == ModelType.LOCAL:
-            model = CatBoostClassifier(one_hot_max_size=65)
-            loaded_model = model.load_model(MODEL_PATHS[model_name])
-            model_hash = self._get_hash(obj=loaded_model)
-            return ModelWithMetadata(model=loaded_model, model_identifier=model_hash)
 
-        elif model_type == ModelType.PREPROCESSING:
-            return ModelWithMetadata(
-                model=SentenceTransformer(model_name),
-                model_identifier=model_name,
+        elif model_name == ModelName.OFFER_CATEGORISATION:
+            loaded_model = mlflow.catboost.load_model(
+                model_uri=f"models:/{self._get_mlflow_model_name(model_name)}"
             )
+            model_hash = self.get_model_hash_from_mlflow(model_name=model_name)
+        else:
+            if model_type == ModelType.PREPROCESSING:
+                loaded_model = SentenceTransformer(model_name, device="cpu")
+                model_hash = f"hash_preproc_{model_name}"
+            else:
+                raise ValueError(
+                    f"Model name {model_name} not found with type {model_type}"
+                )
+        return ModelWithMetadata(
+            model=loaded_model,
+            model_identifier=model_hash,
+        )
 
     @staticmethod
     def _get_hash(obj):
@@ -64,20 +61,19 @@ def _get_hash(obj):
         return hashlib.md5(obj_bytes).hexdigest()
 
     def get_model_hash_from_mlflow(self, model_name: str):
+        SPLIT_PATTERN = "/|@"
+
         mlflow_model_name = self._get_mlflow_model_name(model_name=model_name)
-        if not self.mlflow_client:
-            raise ValueError("No mlflow client connected")
+        mlflow_model_name_stripped = re.split(SPLIT_PATTERN, mlflow_model_name)[0]
 
         model_version = self.mlflow_client.get_latest_versions(
-            mlflow_model_name, stages=[self.MODEL_STAGE]
+            mlflow_model_name_stripped
         )
-
         return self._get_hash(model_version)
 
     @staticmethod
     def _get_mlflow_model_name(model_name: ModelName):
         if model_name == ModelName.COMPLIANCE:
-            return f"{model_name.value}_default_{ENV_SHORT_NAME}"
+            return f"api_{model_name.value}_default_{ENV_SHORT_NAME}@production"
         elif model_name == ModelName.OFFER_CATEGORISATION:
-            return f"{model_name.value}_{ENV_SHORT_NAME}"
-        raise ValueError(f"Only {ModelName.__members__} are registered in mlflow")
+            return f"{model_name.value}_{ENV_SHORT_NAME}/production"
diff --git a/apps/fraud/compliance/api/src/pcpapillon/utils/tools.py b/apps/fraud/compliance/api/src/pcpapillon/utils/tools.py
index e743887d..68e0f693 100644
--- a/apps/fraud/compliance/api/src/pcpapillon/utils/tools.py
+++ b/apps/fraud/compliance/api/src/pcpapillon/utils/tools.py
@@ -1,15 +1,25 @@
+import json
 import os
 
 import mlflow
 from google.auth.transport.requests import Request
-from google.oauth2 import id_token
-from pcpapillon.utils.env_vars import MLFLOW_TRACKING_TOKEN, MLFLOW_URL
+from google.oauth2 import service_account
+from pcpapillon.utils.env_vars import (
+    GCP_PROJECT,
+    MLFLOW_CLIENT_ID,
+    MLFLOW_URL,
+    SA_ACCOUNT,
+    access_secret,
+)
 
 
-def connect_remote_mlflow(client_id):
-    if not MLFLOW_TRACKING_TOKEN:
-        os.environ["MLFLOW_TRACKING_TOKEN"] = id_token.fetch_id_token(
-            Request(), client_id
-        )
-    uri = MLFLOW_URL
-    mlflow.set_tracking_uri(uri)
+def connect_remote_mlflow():
+    service_account_dict = json.loads(access_secret(GCP_PROJECT, SA_ACCOUNT))
+
+    id_token_credentials = service_account.IDTokenCredentials.from_service_account_info(
+        service_account_dict, target_audience=MLFLOW_CLIENT_ID
+    )
+    id_token_credentials.refresh(Request())
+
+    os.environ["MLFLOW_TRACKING_TOKEN"] = id_token_credentials.token
+    mlflow.set_tracking_uri(MLFLOW_URL)
diff --git a/apps/fraud/compliance/api/src/pcpapillon/views/compliance.py b/apps/fraud/compliance/api/src/pcpapillon/views/compliance.py
index e7085edc..e9018b1c 100644
--- a/apps/fraud/compliance/api/src/pcpapillon/views/compliance.py
+++ b/apps/fraud/compliance/api/src/pcpapillon/views/compliance.py
@@ -1,14 +1,13 @@
 from fastapi import APIRouter, Depends
 from fastapi_versioning import version
 from main import custom_logger, setup_trace
-from pcpapillon.core.compliance.compliance_model import (
+from pcpapillon.core.compliance_model import (
     ComplianceModel,
 )
 from pcpapillon.utils.data_model import (
     ComplianceInput,
     ComplianceOutput,
 )
-from pcpapillon.utils.env_vars import IS_API_LOCAL
 from pcpapillon.utils.scheduler import init_scheduler
 
 compliance_router = APIRouter(tags=["compliance"])
@@ -16,10 +15,9 @@
 
 # Init model and scheduler
 compliance_model = ComplianceModel()
-if not IS_API_LOCAL:
-    compliance_scheduler = init_scheduler(
-        compliance_model.reload_model_if_newer_is_available, time_interval=600
-    )
+compliance_scheduler = init_scheduler(
+    compliance_model.reload_model_if_newer_is_available, time_interval=600
+)
 
 
 @compliance_router.post(
@@ -35,19 +33,7 @@ def model_compliance_scoring(scoring_input: ComplianceInput):
         "scoring_input": scoring_input.dict(),
     }
 
-    (
-        proba_validation,
-        proba_rejection,
-        top_validation,
-        top_rejection,
-    ) = compliance_model.predict(data=scoring_input.dict())
+    predictions = compliance_model.predict(data=scoring_input)
 
-    validation_response_dict = {
-        "offer_id": scoring_input.dict()["offer_id"],
-        "probability_validated": proba_validation,
-        "validation_main_features": top_validation,
-        "probability_rejected": proba_rejection,
-        "rejection_main_features": top_rejection,
-    }
-    custom_logger.info(validation_response_dict, extra=log_extra_data)
-    return validation_response_dict
+    custom_logger.info(predictions.dict(), extra=log_extra_data)
+    return predictions
diff --git a/apps/fraud/compliance/api/src/pcpapillon/views/offer_categorisation.py b/apps/fraud/compliance/api/src/pcpapillon/views/offer_categorisation.py
index bc8c6fe0..e05223ac 100644
--- a/apps/fraud/compliance/api/src/pcpapillon/views/offer_categorisation.py
+++ b/apps/fraud/compliance/api/src/pcpapillon/views/offer_categorisation.py
@@ -1,7 +1,7 @@
 from fastapi import APIRouter, Depends
 from fastapi_versioning import version
 from main import custom_logger, setup_trace
-from pcpapillon.core.offer_categorisation.offer_categorisation_model import (
+from pcpapillon.core.offer_categorisation_model import (
     OfferCategorisationModel,
 )
 from pcpapillon.utils.data_model import (
diff --git a/apps/recommendation/api/Makefile b/apps/recommendation/api/Makefile
index 148b9d2a..df380400 100644
--- a/apps/recommendation/api/Makefile
+++ b/apps/recommendation/api/Makefile
@@ -9,4 +9,4 @@ install:
 	@eval "$$(pyenv init -)" && pyenv activate api-data-reco && pip install -r requirements.txt
 
 start:
-	@eval "$$(pyenv init -)" && cd src && API_LOCAL=1 uvicorn main:app --reload
\ No newline at end of file
+	@eval "$$(pyenv init -)" && cd src && API_LOCAL=1 uvicorn main:app --reload
diff --git a/apps/recommendation/api/requirements.in b/apps/recommendation/api/requirements.in
index e200fb7b..eff28f4e 100644
--- a/apps/recommendation/api/requirements.in
+++ b/apps/recommendation/api/requirements.in
@@ -19,4 +19,4 @@ pytest-asyncio==0.23.7
 pytest-postgresql==6.0.0
 aiocache==0.12.2
 python-dateutil==2.9.0.post0
-httpx==0.27.0
\ No newline at end of file
+httpx==0.27.0