Fix: pin mlflow<2.19 and remove custom conda_env to avoid deployment image build failure

Author: Chakradhar886

tutorials/get-started-notebooks/quickstart.ipynb

@@ -255,31 +255,11 @@
     "    # Registering the model to the workspace\n",
     "    print(\"Registering the model via MLFlow\")\n",
     "\n",
-    "    # pin numpy\n",
-    "    conda_env = {\n",
-    "        'name': 'mlflow-env',\n",
-    "        'channels': ['conda-forge'],\n",
-    "        'dependencies': [\n",
-    "            'python=3.10.15',\n",
-    "            'pip<=21.3.1',\n",
-    "            {\n",
-    "                'pip': [\n",
-    "                    'mlflow==2.17.0',\n",
-    "                    'cloudpickle==2.2.1',\n",
-    "                    'pandas==1.5.3',\n",
-    "                    'psutil==5.8.0',\n",
-    "                    'scikit-learn==1.5.2',\n",
-    "                    'numpy==1.26.4',\n",
-    "                ]\n",
-    "            }\n",
-    "        ],\n",
-    "    }\n",
-    "\n",
     "    mlflow.sklearn.log_model(\n",
     "        sk_model=clf,\n",
     "        registered_model_name=args.registered_model_name,\n",
     "        artifact_path=args.registered_model_name,\n",
-    "        conda_env=conda_env,\n",
+    "    )\n",
     "    )\n",
     "\n",
     "    # Saving the model to a file\n",
@@ -721,4 +701,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}