title: Setup Mlflow backend (SQLite) and artifact (Azure Blob Storage) stores
body: <p>I would like to setup Mlflow to have the following components :</p>
  <ul>
  <li>Backend store (local) : using a SQLite database locally to store Mlflow entities (run_id, params, metrics...)</li>
  <li>Artifact store (remote) : using a blob storage on my Azure Data Lake Storage Gen2 to store the output files (versioned datasets, serialized models, images, ...) related to my model</li>
  <li>Tracking server : by using something that looks like this command</li>
  </ul>
  <p>z</p>
  <pre><code>mlflow server --backend-store-uri sqlite:///C:\sqlite\db\mlruns.db --default-artifact-root wasbs://container-name@storage_account_name.blob.core.windows.net/mlartifacts -h 0.0.0.0 -p 8000
  </code></pre>
  <p>Where mlruns.db is a database that I created in SQLite (inside a db folder) and mlartifacts is the folder I created inside the blob container to receive all the output files.</p>
  <p>I run this command and then I do and mlflow run (or a kedro run as I'm using Kedro) but almost nothing happens. The database is populated with 12 tables but all empty while nothing happens inside the Data lake.</p>
  <p>What I want should look like <a href="https://www.mlflow.org/docs/latest/tracking.html#scenario-4-mlflow-with-remote-tracking-server-backend-and-artifact-stores" rel="nofollow noreferrer">Scenario 4</a> in the documentation.</p>
  <p>For the artifact store, I couldn't find detailed instructions. I tried to look at Mlflow's documentation <a href="https://www.mlflow.org/docs/latest/tracking.html#azure-blob-storage" rel="nofollow noreferrer">here</a> but this is not very helpful (i'm still a beginner). They say that:</p>
  <blockquote>
  <p>MLflow expects Azure Storage access credentials in the AZURE_STORAGE_CONNECTION_STRING, AZURE_STORAGE_ACCESS_KEY environment variables or having your credentials configured such that the DefaultAzureCredential(). class can pick them up.</p>
  </blockquote>
  <p>However, even when adding the env variables, nothing seems to be stored in the data lake. I created the two env variables (on Windows 10):</p>
  <ul>
  <li><p>AZURE_STORAGE_ACCESS_KEY = wasbs://container-name@storage_account_name.blob.core.windows.net/mlartifacts</p>
  </li>
  <li><p>AZURE_STORAGE_CONNECTION_STRING =  DefaultEndpointsProtocol=https;AccountName=storagesample;AccountKey=. I got it by following this path on Azure Portal : Storage account/Access keys/Connection string (took the one of key 2).</p>
  </li>
  </ul>
  <p>They also state that :</p>
  <blockquote>
  <p>Also, you must run pip install azure-storage-blob separately (on both your client and the server) to access Azure Blob Storage. Finally, if you want to use DefaultAzureCredential, you must pip install azure-identity; MLflow does not declare a dependency on these packages by default.</p>
  </blockquote>
  <p>I added them in my project requirements, but what do they mean exactly by installing on both the client and the server ? How azure-identity helps in the setup ?</p>
  <p>Could you please help me with a step by step instructions on how to make the complete setup ?</p>
  <p>Thank you in advance !</p>