Q- is this the last work of my hands on experiment with mlflow oky : 

Q- the work is like this 


Q- i just come across the concept of mlflow when implement my churn prediction notebook 

Q- i know mlflow is end-to-end ml pipeline workflow , 

Q- i know it’s open source from the team “databricks” : which i am huge fan of their work so far  by the way 

Q- i use it in my project by creating a python “virtual” env called “venv” 
And just install it with this command oky 

“Pip install mlflow” 
Q- basically in my note book i just implement in 3 cells : 

Q- each one implement ml classic model (logistic regression , knn , decision Tree) 

Q- i use them on these 3 cells 

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import mlflow
import mlflow.sklearn


def train_and_validate_model(X_train, X_test, y_train, y_test, model=LogisticRegression()):
    with mlflow.start_run():
        # Log model parameters
        mlflow.log_param("model", "LogisticRegression")
        mlflow.log_param("test_size", 0.2)
        mlflow.log_param("random_state", 42)
       
        # Train the model
        model.fit(X_train, y_train)


        # Make predictions
        y_pred = model.predict(X_test)
        y_pred_proba = model.predict_proba(X_test)[:, 1]


        # Calculate performance metrics
        accuracy = accuracy_score(y_test, y_pred)
        precision = precision_score(y_test, y_pred)
        recall = recall_score(y_test, y_pred)
        f1 = f1_score(y_test, y_pred)
        roc_auc = roc_auc_score(y_test, y_pred_proba)


        # Log metrics
        metrics = ['accuracy', 'precision', 'recall', 'f1_score', 'roc_auc']
        for metric, value in zip(metrics, [accuracy, precision, recall, f1, roc_auc]):
            mlflow.log_metric(metric, value)
            print(f"{metric.capitalize()}: {value:.4f}")


        # Log the model
        mlflow.sklearn.log_model(model, "logistic_regression_model")


        return accuracy, precision, recall, f1, roc_auc


# Call the function
result = train_and_validate_model(X_train, X_test, y_train, y_test)



Q- second one is ; 

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import (accuracy_score, precision_score, recall_score,
                             f1_score, roc_auc_score)
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import mlflow
import mlflow.sklearn


def train_and_validate_model(X_train, X_test, y_train, y_test, model):
    with mlflow.start_run():
        mlflow.log_param("model", model.__class__.__name__)
        model.fit(X_train, y_train)
        y_pred = model.predict(X_test)
        if hasattr(model, "predict_proba"):
            y_pred_proba = model.predict_proba(X_test)[:, 1]
        else:
            y_pred_proba = y_pred
        accuracy = accuracy_score(y_test, y_pred)
        precision = precision_score(y_test, y_pred)
        recall = recall_score(y_test, y_pred)
        f1 = f1_score(y_test, y_pred)
        roc_auc = roc_auc_score(y_test, y_pred_proba) if hasattr(model, "predict_proba") else None
        mlflow.log_metric("accuracy", accuracy)
        mlflow.log_metric("precision", precision)
        mlflow.log_metric("recall", recall)
        mlflow.log_metric("f1_score", f1)
        if roc_auc is not None:
            mlflow.log_metric("roc_auc", roc_auc)
        print(f"Model: {model.__class__.__name__}")
        print(f"Accuracy: {accuracy:.4f}")
        print(f"Precision: {precision:.4f}")
        print(f"Recall: {recall:.4f}")
        print(f"F1 Score: {f1:.4f}")
        if roc_auc is not None:
            print(f"ROC AUC: {roc_auc:.4f}")
        mlflow.sklearn.log_model(model, model.__class__.__name__)
        return accuracy, precision, recall, f1, roc_auc


knn = KNeighborsClassifier(n_neighbors=5)
result_knn = train_and_validate_model(X_train, X_test, y_train, y_test, knn)




Q- the third one is ; 

from sklearn.tree import DecisionTreeClassifier
def train_and_validate_decision_tree(X_train,X_test,y_train,y_test,max_depth=None,random_state=None):
    """
    Train a Decision Tree model and validate its performance using various metrics.
    The function also log the model , parameters , and metrics to the MLflow.
     Input:
    - X_train: Training features
    - X_test: Testing features
    - y_train: Training labels
    - y_test: Testing labels
    - max_depth: Maximum depth of the decision tree (default: None)
    - random_state: Random state for reproducibility (default: 42)


    Output:
    - accuracy: Accuracy score
    - precision: Precision score
    - recall: Recall score
    - f1: F1 score
    - roc_auc: ROC AUC score
    """
    with mlflow.start_run():
        model =DecisionTreeClassifier(max_depth=max_depth,random_state=random_state)
        model.fit(X_train,y_train)


        # log the model parameters
        mlflow.log_param("model","DecisionTreeClassifier")
        mlflow.log_param("max_depth",max_depth)
        mlflow.log_param("random_state",random_state)


        # make predictions
        y_pred=model.predict(X_test)
        y_pred_proba=model.predict_proba(X_test)[:,1]
       
        # Calculate evaluation metrics
        accuracy=accuracy_score(y_test,y_pred)
        precision=precision_score(y_test,y_pred)
        recall=recall_score(y_test,y_pred)
        f1=f1_score(y_test,y_pred)
        roc_auc= roc_auc_score(y_test,y_pred_proba)


        # log the metrics
        metrics=['accuracy','precision','recall','f1','roc_auc']
        for metric , value in zip(metrics,[accuracy,precision,recall,f1,roc_auc]):
            mlflow.log_metric(metric,value)
            print(f"{metric.capitalize()}:{value:.4f}")
        # log the model
        mlflow.sklearn.log_model(model,"decision_tree_model")
        return accuracy,precision,recall,f1,roc_auc
results=train_and_validate_decision_tree(X_train,X_test,y_train,y_test,max_depth=5,random_state=42)





Q- and then i run this command 


mlflow ui 


Q- and i get this url : 

http://127.0.0.1:5000


Q- this so far i learned : 


Q-  this all the information i know so far : 


Key Features and Components of MLflow
Four Main Components: MLflow consists of four primary components: Tracking, Projects, Models, and Registry, each serving distinct purposes in the ML lifecycle.
Experiment Tracking: The Tracking component allows you to log parameters, metrics, and artifacts from your experiments, making it easier to compare different runs and reproduce results later.
Model Packaging: The Models component provides a standard format for packaging machine learning models, enabling easy deployment across various platforms.


—> for the model Packaging : by you definition it’s the component provides as a standard format for packaging the ml models , enabling easy deployment access various platforms 


Q- but i think i need to more context and hands one tutor 
Q- that use my code as the context here 

Q-to show the model Packaging : component in action here oky : 

Q- help me a create one like the best medium article oky : 



# Mastering MLflow: A Hands-On Guide to Model Packaging and Deployment

In the world of machine learning, creating a model is just the first step. The real challenge often lies in packaging and deploying your model effectively. This is where MLflow, and specifically its Model Packaging component, comes into play. In this tutorial, we'll explore how to use MLflow to package and deploy your machine learning models, using a real-world churn prediction project as our example.

## Understanding MLflow Model Packaging

MLflow's Model Packaging component provides a standard format for bundling your machine learning models. This standardization makes it easier to deploy your models across various platforms, from local environments to cloud services.

Key benefits of MLflow Model Packaging include:
1. Consistency across different ML frameworks
2. Easy versioning and tracking of model artifacts
3. Simplified deployment process

Let's dive into how we can use this powerful feature in our churn prediction project.

## Our Churn Prediction Project

We've built three models for predicting customer churn:
1. Logistic Regression
2. K-Nearest Neighbors (KNN)
3. Decision Tree

Each model is trained and logged using MLflow Tracking. Now, let's see how we can package these models for deployment.

## Step 1: Logging the Model

The first step in model packaging happens during the training process. Let's look at how we've logged our Logistic Regression model:

```python
def train_and_validate_model(X_train, X_test, y_train, y_test, model=LogisticRegression()):
    with mlflow.start_run():
        # ... (training code)

        # Log the model
        mlflow.sklearn.log_model(model, "logistic_regression_model")

        return accuracy, precision, recall, f1, roc_auc
```

The key line here is `mlflow.sklearn.log_model(model, "logistic_regression_model")`. This function call does several things:
1. Serializes the model
2. Logs the model as an artifact
3. Creates a directory structure that MLflow uses for deployment

## Step 2: Examining the Logged Model

After running your experiment, you can examine the logged model using the MLflow UI. Here's how:

1. Run `mlflow ui` in your terminal
2. Open `http://127.0.0.1:5000` in your browser
3. Navigate to your experiment run
4. Under the "Artifacts" section, you'll see your logged model

Click on the model to explore its structure. You'll notice files like `conda.yaml` (for reproducing the environment) and `MLmodel` (metadata about your model).

## Step 3: Loading the Model for Deployment

Once your model is logged, you can easily load it for deployment or further use. Here's how you might load the Logistic Regression model:

```python
import mlflow.sklearn

# Replace with your actual run ID
run_id = "your_run_id_here"

loaded_model = mlflow.sklearn.load_model(f"runs:/{run_id}/logistic_regression_model")
```

This loaded model is now ready for making predictions!

## Step 4: Serving Your Model

MLflow makes it easy to serve your model as a REST API. Here's how you can do it:

1. Find your run ID in the MLflow UI
2. In your terminal, run:

```bash
mlflow models serve -m runs:/<run_id>/logistic_regression_model -p 1234
```

This command starts a server on port 1234 that can receive prediction requests.

## Step 5: Making Predictions

With your model served, you can now make predictions. Here's a Python script to do so:

```python
import requests
import json

# Prepare your data
data = {"columns": ["feature1", "feature2", ...], "data": [[1, 2, ...]]}

# Make a POST request to your served model
response = requests.post("http://127.0.0.1:1234/invocations", json=data)

# Print the predictions
print(json.loads(response.text))
```

## Conclusion

MLflow's Model Packaging component simplifies the process of taking your model from experimentation to production. By providing a standard format for packaging models, MLflow enables easy versioning, reproducibility, and deployment across various platforms.

In this tutorial, we've seen how to:
1. Log a model during training
2. Examine the logged model using the MLflow UI
3. Load a logged model for deployment
4. Serve a model as a REST API
5. Make predictions using the served model

As you continue your journey with MLflow, explore more advanced features like model versioning with MLflow Model Registry and automated deployment pipelines. Happy modeling!


Q- oky i like your tutor so 

The firs step i do : 

→ i navigate to the url like this : http://127.0.0.1:5000/#/experiments/0/runs/d5b5a55f74bc4d4ca5f412e2f6e5a18b/artifacts

→ go the artifact section 


→ and i got this MLmodel : like this : 

artifact_path: logistic_regression_model
flavors:
  python_function:
    env:
      conda: conda.yaml
      virtualenv: python_env.yaml
    loader_module: mlflow.sklearn
    model_path: model.pkl
    predict_fn: predict
    python_version: 3.11.5
  sklearn:
    code: null
    pickled_model: model.pkl
    serialization_format: cloudpickle
    sklearn_version: 1.5.2
mlflow_version: 2.17.0
model_size_bytes: 1212
model_uuid: 45233bea5b244a118d2f3c0c807ae179
run_id: d5b5a55f74bc4d4ca5f412e2f6e5a18b
utc_time_created: '2024-10-16 15:32:59.433804'






→ then i go to the vs code folder and i create a file in the main folder called : 

load.py like this 

import mlflow.sklearn


# Replace with your actual run ID
run_id = "d5b5a55f74bc4d4ca5f412e2f6e5a18b"


loaded_model = mlflow.sklearn.load_model(f"runs:/{run_id}/logistic_regression_model")


→ then i got the terminal and go run these two commands : 


(venv) PS C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction> mlflow models serve -m runs:/<run_id>/logistic_regression_model -p 1234
Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Scripts\mlflow.exe\__main__.py", line 7, in <module>
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1157, in __call__
    return self.main(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1078, in main
    rv = self.invoke(ctx)
         ^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1688, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1688, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1434, in invoke
    return ctx.invoke(self.callback, **ctx.params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 783, in invoke
    return __callback(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\models\cli.py", line 103, in serve
    return get_flavor_backend(
           ^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\models\flavor_backend_registry.py", line 44, in get_flavor_backend
    artifact_repo = get_artifact_repository(root_uri)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\store\artifact\artifact_repository_registry.py", line 132, in get_artifact_repository        
    return _artifact_repository_registry.get_artifact_repository(artifact_uri)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\store\artifact\artifact_repository_registry.py", line 77, in get_artifact_repository
    return repository(artifact_uri)
           ^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\store\artifact\runs_artifact_repo.py", line 26, in __init__
    uri = RunsArtifactRepository.get_underlying_uri(artifact_uri)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\store\artifact\runs_artifact_repo.py", line 39, in get_underlying_uri
    uri = get_artifact_uri(run_id, artifact_path, tracking_uri)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\tracking\artifact_utils.py", line 52, in get_artifact_uri
    run = store.get_run(run_id)
          ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\store\tracking\file_store.py", line 692, in get_run
    _validate_run_id(run_id)
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\utils\validation.py", line 377, in _validate_run_id
    raise MlflowException(invalid_value(path, run_id), error_code=INVALID_PARAMETER_VALUE)
mlflow.exceptions.MlflowException: Invalid value "<run_id>" for parameter 'run_id' supplied.
(venv) PS C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction> mlflow models serve -m runs:/<d5b5a55f74bc4d4ca5f412e2f6e5a18b>/logistic_regression_model -p 1234
Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Scripts\mlflow.exe\__main__.py", line 7, in <module>
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1157, in __call__
    return self.main(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1078, in main
    rv = self.invoke(ctx)
         ^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1688, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1688, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1434, in invoke
    return ctx.invoke(self.callback, **ctx.params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 783, in invoke
    return __callback(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\models\cli.py", line 103, in serve
    return get_flavor_backend(
           ^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\models\flavor_backend_registry.py", line 44, in get_flavor_backend
    artifact_repo = get_artifact_repository(root_uri)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\store\artifact\artifact_repository_registry.py", line 132, in get_artifact_repository        
    return _artifact_repository_registry.get_artifact_repository(artifact_uri)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\store\artifact\artifact_repository_registry.py", line 77, in get_artifact_repository
    return repository(artifact_uri)
           ^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\store\artifact\runs_artifact_repo.py", line 26, in __init__
    uri = RunsArtifactRepository.get_underlying_uri(artifact_uri)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\store\artifact\runs_artifact_repo.py", line 39, in get_underlying_uri
    uri = get_artifact_uri(run_id, artifact_path, tracking_uri)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\tracking\artifact_utils.py", line 52, in get_artifact_uri
    run = store.get_run(run_id)
          ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\store\tracking\file_store.py", line 692, in get_run
    _validate_run_id(run_id)
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\utils\validation.py", line 377, in _validate_run_id
    raise MlflowException(invalid_value(path, run_id), error_code=INVALID_PARAMETER_VALUE)
mlflow.exceptions.MlflowException: Invalid value "<d5b5a55f74bc4d4ca5f412e2f6e5a18b>" for parameter 'run_id' supplied.


Q- first following my steps where are the error exactly please ? 

Q-explain the error if you find it as discussion between mlflow experts 

Q- then give step by step tutor to fix it 

Q- oky i got this tutor 

“You've almost got it! The issue lies in how you're using the run_id in the mlflow models serve command. Let's break down the error and how to fix it, explained as a conversation between MLflow experts:
Expert 1: "Hey, I'm seeing this MlflowException: Invalid value "<run_id>" for parameter 'run_id' supplied. error when I try to serve my model. I'm using mlflow models serve -m runs:/<run_id>/logistic_regression_model -p 1234. Any ideas?"
Expert 2: "Ah, yes, the classic angle bracket problem! You're not supposed to include the literal angle brackets < and > in the command. The <run_id> is a placeholder meant to be replaced with your actual run ID."
Expert 1: "Oh, I see! So, if my run ID is d5b5a55f74bc4d4ca5f412e2f6e5a18b, the command should be mlflow models serve -m runs:/d5b5a55f74bc4d4ca5f412e2f6e5a18b/logistic_regression_model -p 1234?"
Expert 2: "Exactly! The runs:/ prefix tells MLflow to look for the model within your MLflow tracking server's run artifacts. Without the correct run ID, it can't find the model."
Step-by-step fix:
Get the Correct Run ID: Navigate to your run in the MLflow UI (http://127.0.0.1:5000). The run ID is part of the URL and displayed prominently on the run page. In your example, it's d5b5a55f74bc4d4ca5f412e2f6e5a18b.
Construct the Correct Command: Replace <run_id> with the actual run ID without the angle brackets:
      mlflow models serve -m runs:/d5b5a55f74bc4d4ca5f412e2f6e5a18b/logistic_regression_model -p 1234
   
 content_copy Use code with caution.Bash
Run the Command: Execute this corrected command in your terminal. This will start a local server hosting your model.
Verify the Server: You should see output in the terminal indicating the server is running and listening on port 1234.
Test with load.py and Prediction Script: Now, your load.py script should work correctly. You can also test making predictions using the Python script provided in the tutorial, ensuring you replace "your_run_id_here" with d5b5a55f74bc4d4ca5f412e2f6e5a18b.
This should resolve the issue and allow you to serve your model successfully! Remember, those angle brackets are placeholders for explanation, not part of the actual command syntax.
“ 

Q- and rerun this command : 
(venv) PS C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction> mlflow models serve -m runs:/d5b5a55f74bc4d4ca5f412e2f6e5a18b/logistic_regression_model -p 1234 
2024/10/16 19:52:57 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function'
Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Scripts\mlflow.exe\__main__.py", line 7, in <module>
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1157, in __call__
    return self.main(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1078, in main
    rv = self.invoke(ctx)
         ^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1688, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1688, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 1434, in invoke
    return ctx.invoke(self.callback, **ctx.params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\click\core.py", line 783, in invoke
    return __callback(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\models\cli.py", line 105, in serve
    ).serve(
      ^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\pyfunc\backend.py", line 284, in serve
    return self.prepare_env(local_path).execute(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\pyfunc\backend.py", line 130, in prepare_env
    activate_cmd = _get_or_create_virtualenv(
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\utils\virtualenv.py", line 352, in _get_or_create_virtualenv
    _validate_pyenv_is_available()
  File "C:\Users\PCS\Desktop\mlops\Mlops-Customer-Prediction\venv\Lib\site-packages\mlflow\utils\virtualenv.py", line 62, in _validate_pyenv_is_available
    raise MlflowException(
mlflow.exceptions.MlflowException: Could not find the pyenv binary. See https://github.com/pyenv-win/pyenv-win#installation for installation instructions.


Q- i got this error again : 

Q- explain the erro like 2 experts desiccson 

Q- create a step by step tutor to fix it ?

Let me help break down this error and provide a solution through an expert discussion and tutorial.

**Expert Discussion:**

Expert 1: "I see the issue here - the error message `Could not find the pyenv binary` is coming up when trying to serve the model. This is happening because MLflow is trying to create an isolated environment for serving the model and it's looking for pyenv to manage Python versions."

Expert 2: "Exactly! On Windows, MLflow expects pyenv-win to be installed for managing Python environments. It's a dependency that's not automatically installed with MLflow. The good news is we can fix this by either installing pyenv-win or using an alternative approach with conda."

Expert 1: "Right, and for Windows users, I usually recommend going with the conda approach since it's more stable and widely used in the data science community. Should we guide them through setting up with conda?"

Expert 2: "Absolutely! Using conda will give us a more reliable environment management solution. Let's create a step-by-step guide for them."

**Step-by-Step Tutorial to Fix the Error:**

1. **Install Miniconda** (if not already installed):
   ```bash
   # Download Miniconda installer from:
   https://docs.conda.io/en/latest/miniconda.html
   
   # Run the installer and follow the prompts
   ```

2. **Create a New Conda Environment**:
   ```bash
   # Open a new terminal/command prompt
   conda create -n mlflow-serve python=3.8
   conda activate mlflow-serve
   ```

3. **Install Required Packages in the New Environment**:
   ```bash
   conda install scikit-learn pandas numpy
   pip install mlflow
   ```

4. **Export Your Model with Conda Environment**:
   Let's modify your training code to include conda environment information. Create a new file called `train_with_conda.py`:



```python
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
import mlflow
import mlflow.sklearn

def train_and_validate_model(X_train, X_test, y_train, y_test, model=LogisticRegression()):
    # Define the conda environment
    conda_env = {
        'name': 'mlflow-env',
        'channels': ['defaults', 'conda-forge'],
        'dependencies': [
            'python=3.8',
            'scikit-learn',
            'pandas',
            'numpy',
            {'pip': ['mlflow']}
        ]
    }

    with mlflow.start_run():
        # Log parameters
        mlflow.log_param("model", "LogisticRegression")
        
        # Train the model
        model.fit(X_train, y_train)
        
        # Make predictions
        y_pred = model.predict(X_test)
        y_pred_proba = model.predict_proba(X_test)[:, 1]
        
        # Calculate and log metrics
        metrics = {
            'accuracy': accuracy_score(y_test, y_pred),
            'precision': precision_score(y_test, y_pred),
            'recall': recall_score(y_test, y_pred),
            'f1': f1_score(y_test, y_pred),
            'roc_auc': roc_auc_score(y_test, y_pred_proba)
        }
        
        for metric_name, value in metrics.items():
            mlflow.log_metric(metric_name, value)
            print(f"{metric_name}: {value:.4f}")
        
        # Log the model with conda environment
        mlflow.sklearn.log_model(
            model, 
            "logistic_regression_model",
            conda_env=conda_env
        )
        
        return metrics

```

5. **Run the Training Script**:
   ```bash
   # Activate your conda environment
   conda activate mlflow-serve
   
   # Run the training script
   python train_with_conda.py
   ```

6. **Get the New Run ID**:
   - Open MLflow UI: `mlflow ui`
   - Navigate to http://127.0.0.1:5000
   - Find your latest run and copy the Run ID

7. **Serve the Model**:
   ```bash
   # Make sure you're in the conda environment
   conda activate mlflow-serve
   
   # Serve the model (replace with your new run ID)
   mlflow models serve -m runs:/YOUR_NEW_RUN_ID/logistic_regression_model -p 1234
   ```

8. **Test the Served Model**:
   Create a new file `test_prediction.py`:

```python
import requests
import json
import numpy as np

# Prepare your test data
# Replace these features with your actual feature names and values
test_data = {
    "columns": ["feature1", "feature2", "feature3"],
    "data": [[0.1, 0.2, 0.3]]
}

# Make prediction request
response = requests.post(
    "http://127.0.0.1:1234/invocations",
    headers={"Content-Type": "application/json"},
    data=json.dumps(test_data)
)

# Print the prediction
print("Prediction:", response.json())

```

Expert 1: "One important note - make sure to keep the conda environment active throughout the serving process. If you close your terminal, you'll need to reactivate it with `conda activate mlflow-serve`."

Expert 2: "Good point! And if users encounter any issues with conda, they can always check their conda installation with `conda --version` and ensure all paths are properly set in their system environment variables."

Would you like me to explain any part of this solution in more detail?
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
import mlflow
import mlflow.sklearn

def train_and_validate_model(X_train, X_test, y_train, y_test, model=LogisticRegression()):
    # Define the conda environment
    conda_env = {
        'name': 'mlflow-env',
        'channels': ['defaults', 'conda-forge'],
        'dependencies': [
            'python=3.8',
            'scikit-learn',
            'pandas',
            'numpy',
            {'pip': ['mlflow']}
        ]
    }

    with mlflow.start_run():
        # Log parameters
        mlflow.log_param("model", "LogisticRegression")
        
        # Train the model
        model.fit(X_train, y_train)
        
        # Make predictions
        y_pred = model.predict(X_test)
        y_pred_proba = model.predict_proba(X_test)[:, 1]
        
        # Calculate and log metrics
        metrics = {
            'accuracy': accuracy_score(y_test, y_pred),
            'precision': precision_score(y_test, y_pred),
            'recall': recall_score(y_test, y_pred),
            'f1': f1_score(y_test, y_pred),
            'roc_auc': roc_auc_score(y_test, y_pred_proba)
        }
        
        for metric_name, value in metrics.items():
            mlflow.log_metric(metric_name, value)
            print(f"{metric_name}: {value:.4f}")
        
        # Log the model with conda environment
        mlflow.sklearn.log_model(
            model, 
            "logistic_regression_model",
            conda_env=conda_env
        )
        
        return metrics

Q- i just come across the concept of mlflow when implement my churn prediction notebook 

Q- i know mlflow is end-to-end ml pipeline workflow , 

Q- i know it’s open source from the team “databricks” : which i am huge fan of their work so far  by the way 

Q- i use it in my project by creating a python “virtual” env called “venv” 
And just install it with this command oky 

“Pip install mlflow” 
Q- basically in my note book i just implement in 3 cells : 

Q- each one implement ml classic model (logistic regression , knn , decision Tree) 

Q- i use them on these 3 cells 

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import mlflow
import mlflow.sklearn


def train_and_validate_model(X_train, X_test, y_train, y_test, model=LogisticRegression()):
    with mlflow.start_run():
        # Log model parameters
        mlflow.log_param("model", "LogisticRegression")
        mlflow.log_param("test_size", 0.2)
        mlflow.log_param("random_state", 42)
       
        # Train the model
        model.fit(X_train, y_train)


        # Make predictions
        y_pred = model.predict(X_test)
        y_pred_proba = model.predict_proba(X_test)[:, 1]


        # Calculate performance metrics
        accuracy = accuracy_score(y_test, y_pred)
        precision = precision_score(y_test, y_pred)
        recall = recall_score(y_test, y_pred)
        f1 = f1_score(y_test, y_pred)
        roc_auc = roc_auc_score(y_test, y_pred_proba)


        # Log metrics
        metrics = ['accuracy', 'precision', 'recall', 'f1_score', 'roc_auc']
        for metric, value in zip(metrics, [accuracy, precision, recall, f1, roc_auc]):
            mlflow.log_metric(metric, value)
            print(f"{metric.capitalize()}: {value:.4f}")


        # Log the model
        mlflow.sklearn.log_model(model, "logistic_regression_model")


        return accuracy, precision, recall, f1, roc_auc


# Call the function
result = train_and_validate_model(X_train, X_test, y_train, y_test)



Q- second one is ; 

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import (accuracy_score, precision_score, recall_score,
                             f1_score, roc_auc_score)
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import mlflow
import mlflow.sklearn


def train_and_validate_model(X_train, X_test, y_train, y_test, model):
    with mlflow.start_run():
        mlflow.log_param("model", model.__class__.__name__)
        model.fit(X_train, y_train)
        y_pred = model.predict(X_test)
        if hasattr(model, "predict_proba"):
            y_pred_proba = model.predict_proba(X_test)[:, 1]
        else:
            y_pred_proba = y_pred
        accuracy = accuracy_score(y_test, y_pred)
        precision = precision_score(y_test, y_pred)
        recall = recall_score(y_test, y_pred)
        f1 = f1_score(y_test, y_pred)
        roc_auc = roc_auc_score(y_test, y_pred_proba) if hasattr(model, "predict_proba") else None
        mlflow.log_metric("accuracy", accuracy)
        mlflow.log_metric("precision", precision)
        mlflow.log_metric("recall", recall)
        mlflow.log_metric("f1_score", f1)
        if roc_auc is not None:
            mlflow.log_metric("roc_auc", roc_auc)
        print(f"Model: {model.__class__.__name__}")
        print(f"Accuracy: {accuracy:.4f}")
        print(f"Precision: {precision:.4f}")
        print(f"Recall: {recall:.4f}")
        print(f"F1 Score: {f1:.4f}")
        if roc_auc is not None:
            print(f"ROC AUC: {roc_auc:.4f}")
        mlflow.sklearn.log_model(model, model.__class__.__name__)
        return accuracy, precision, recall, f1, roc_auc


knn = KNeighborsClassifier(n_neighbors=5)
result_knn = train_and_validate_model(X_train, X_test, y_train, y_test, knn)




Q- the third one is ; 

from sklearn.tree import DecisionTreeClassifier
def train_and_validate_decision_tree(X_train,X_test,y_train,y_test,max_depth=None,random_state=None):
    """
    Train a Decision Tree model and validate its performance using various metrics.
    The function also log the model , parameters , and metrics to the MLflow.
     Input:
    - X_train: Training features
    - X_test: Testing features
    - y_train: Training labels
    - y_test: Testing labels
    - max_depth: Maximum depth of the decision tree (default: None)
    - random_state: Random state for reproducibility (default: 42)


    Output:
    - accuracy: Accuracy score
    - precision: Precision score
    - recall: Recall score
    - f1: F1 score
    - roc_auc: ROC AUC score
    """
    with mlflow.start_run():
        model =DecisionTreeClassifier(max_depth=max_depth,random_state=random_state)
        model.fit(X_train,y_train)


        # log the model parameters
        mlflow.log_param("model","DecisionTreeClassifier")
        mlflow.log_param("max_depth",max_depth)
        mlflow.log_param("random_state",random_state)


        # make predictions
        y_pred=model.predict(X_test)
        y_pred_proba=model.predict_proba(X_test)[:,1]
       
        # Calculate evaluation metrics
        accuracy=accuracy_score(y_test,y_pred)
        precision=precision_score(y_test,y_pred)
        recall=recall_score(y_test,y_pred)
        f1=f1_score(y_test,y_pred)
        roc_auc= roc_auc_score(y_test,y_pred_proba)


        # log the metrics
        metrics=['accuracy','precision','recall','f1','roc_auc']
        for metric , value in zip(metrics,[accuracy,precision,recall,f1,roc_auc]):
            mlflow.log_metric(metric,value)
            print(f"{metric.capitalize()}:{value:.4f}")
        # log the model
        mlflow.sklearn.log_model(model,"decision_tree_model")
        return accuracy,precision,recall,f1,roc_auc
results=train_and_validate_decision_tree(X_train,X_test,y_train,y_test,max_depth=5,random_state=42)





Q- and then i run this command 


mlflow ui 


Q- and i get this url : 

http://127.0.0.1:5000


Q- this so far i learned : 


Q-  this all the information i know so far : 



******************

Q- searching the net i come arooc this note 
“MLflow Projects: Projects allow you to organize your code in a reproducible way, specifying dependencies and entry points for running your ML tasks.
“

Q- given the context of my work 



Q- how to get hands one tutor about this point ?

Q- make each part of it as question & answer style oky 

Let's explore MLflow Projects and how they can help organize your churn prediction code.
Q: I have three separate cells in my notebook, each training a different model (Logistic Regression, KNN, Decision Tree). How can MLflow Projects help me organize this better?
A: MLflow Projects provide a structured format for packaging reusable ML code. Instead of having scattered cells, you can organize your code into a project directory with an MLproject file that defines how to run your code. This makes it easier to share, reproduce, and chain together different ML tasks.
Q: What is an MLproject file?
A: It's a YAML or JSON file that describes your ML project. It specifies:
Name: The name of your project.
Entry points: Different ways to run your code (e.g., training a specific model, making predictions). Each entry point defines the command to execute, parameters, and dependencies.
Environment: How to set up the environment (e.g., using a conda environment, docker container, or system environment).
Q: Can you show me an example of an MLproject file for my churn prediction notebook?
A: Let's say you create a directory called churn_project. Inside, you place your training code in Python files (e.g., train_logistic.py, train_knn.py, train_decision_tree.py). Your MLproject file (also inside churn_project) could look like this:
     name: Churn Prediction

entry_points:
  train_logistic:
    parameters:
      data_path: {type: path, description: Path to the input data}
    command: "python train_logistic.py {data_path}"
  train_knn:
    parameters:
      data_path: {type: path, description: Path to the input data}
      n_neighbors: {type: int, default: 5, description: Number of neighbors for KNN}
    command: "python train_knn.py {data_path} --n_neighbors {n_neighbors}"
  train_decision_tree:
    parameters:
      data_path: {type: path, description: Path to the input data}
      max_depth: {type: int, default: None, description: Max depth of the tree}
    command: "python train_decision_tree.py {data_path} --max_depth {max_depth}"
   
content_copy Use code with caution.Yaml
Q: How do I run an MLflow Project?
A: You can run a project using the mlflow run command:
     mlflow run churn_project -P data_path=path/to/your/data.csv -P n_neighbors=7 -e train_knn
   
content_copy Use code with caution.Bash
This command tells MLflow to:
Find the churn_project directory (you can also specify a Git URI or a packaged project).
Run the train_knn entry point.
Pass the data_path and n_neighbors parameters to your script.
Q: How should I structure my Python training scripts?
A: Each script should handle the training for a single model. For example, train_knn.py could look like this:
     import click # For command-line argument parsing
import mlflow
import mlflow.sklearn
from sklearn.neighbors import KNeighborsClassifier
# ... (rest of your import statements and data loading code) ...


@click.command()
@click.option("--data_path", required=True, type=click.Path(exists=True))
@click.option("--n_neighbors", default=5, type=int)
def train_knn(data_path, n_neighbors):
    with mlflow.start_run():
        # Load data from data_path
        # ...
        knn = KNeighborsClassifier(n_neighbors=n_neighbors)
        # Train and log your model as you did before
        # ...

if __name__ == "__main__":
    train_knn()
   
content_copy Use code with caution.Python
Q: What are the benefits of using MLflow Projects?
A:
Reproducibility: Clearly defines the environment and dependencies, making it easy for others (or your future self) to rerun the code.
Organization: Structures your code into reusable components.
Parameterization: Makes it easy to run experiments with different parameter settings.
Collaboration: Simplifies sharing and collaborating on ML workflows.
Integration with MLflow Tracking: Automatically logs parameters, metrics, and models to the MLflow tracking server.
By adopting MLflow Projects, you'll have a more organized and reproducible way to manage your churn prediction experiments, leading to more efficient model development and deployment.
Q- add your own enhancement or addition notes to this given that in my project : 

I have build a logistic regression model as you know like this 

“logistic_regression_model/MLmodel566B
Path: file:///c:/Users/PCS/Desktop/mlops/Mlops-Customer-Prediction/mlruns/0/d5b5a55f74bc4d4ca5f412e2f6e5a18b/artifacts/logistic_regression_model/MLmodel
artifact_path: logistic_regression_model
flavors:
  python_function:
    env:
      conda: conda.yaml
      virtualenv: python_env.yaml
    loader_module: mlflow.sklearn
    model_path: model.pkl
    predict_fn: predict
    python_version: 3.11.5
  sklearn:
    code: null
    pickled_model: model.pkl
    serialization_format: cloudpickle
    sklearn_version: 1.5.2
mlflow_version: 2.17.0
model_size_bytes: 1212
model_uuid: 45233bea5b244a118d2f3c0c807ae179
run_id: d5b5a55f74bc4d4ca5f412e2f6e5a18b
utc_time_created: '2024-10-16 15:32:59.433804'
“
name: churn_prediction

conda_env: conda.yaml

entry_points:
  main:
    parameters:
      data_path: path
      model_type: {type: str, default: "logistic"}
      C: {type: float, default: 1.0}
      max_iter: {type: int, default: 100}
      n_neighbors: {type: int, default: 5}
      max_depth: {type: int, default: 5}
      random_state: {type: int, default: 42}
    command: "python main.py --data-path {data_path} --model-type {model_type} --C {C} --max-iter {max_iter} --n-neighbors {n_neighbors} --max-depth {max_depth} --random-state {random_state}"

  train_logistic:
    parameters:
      data_path: path
      C: {type: float, default: 1.0}
      max_iter: {type: int, default: 100}
      random_state: {type: int, default: 42}
    command: "python models/logistic_regression.py --data-path {data_path} --C {C} --max-iter {max_iter} --random-state {random_state}"

Q- so can you help to transform my hands one work into a well formeted tutor and article 

Q- i want to be an activate learning style oky :