diff --git a/classifier-e2e/run.ipynb b/classifier-e2e/run_full.ipynb
similarity index 76%
rename from classifier-e2e/run.ipynb
rename to classifier-e2e/run_full.ipynb
index 31d9292b..1edcd747 100644
--- a/classifier-e2e/run.ipynb
+++ b/classifier-e2e/run_full.ipynb
@@ -1,32 +1,19 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pre-demo recommendations\n",
-    "! zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io \n",
-    "! zenml model delete breast_cancer_classifier -y"
-   ]
-  },
   {
    "cell_type": "markdown",
-   "id": "63ab391a",
+   "id": "63f7ab34",
    "metadata": {},
    "source": [
-    "# Intro to MLOps using ZenML\n",
+    "# 🌍 Overview\n",
     "\n",
-    "## 🌍 Overview\n",
-    "\n",
-    "This repository is a minimalistic MLOps project intended as a starting point to learn how to put ML workflows in production. It features: \n",
+    "This demo is a minimalistic MLOps project intended to showcase how to put ML workflows in production. It features: \n",
     "\n",
     "- A feature engineering pipeline that loads data and prepares it for training.\n",
     "- A training pipeline that loads the preprocessed dataset and trains a model.\n",
     "- A batch inference pipeline that runs predictions on the trained model with new data.\n",
-    "\n",
-    "Follow along this notebook to understand how you can use ZenML to productionalize your ML workflows!\n",
+    "- A stack switching and leveraging of Sagemaker step operator to outsource training to Cloud\n",
+    "- An analysis of training artifacts and their lineage (including connection with W&B)\n",
     "\n",
     "<img src=\"_assets/pipeline_overview.png\" width=\"50%\" alt=\"Pipelines Overview\">"
    ]
@@ -49,7 +36,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!zenml integration install sklearn xgboost -y\n",
+    "! pip3 install -r requirements.txt\n",
+    "! zenml integration install sklearn xgboost -y\n",
+    "! zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io \n",
+    "! zenml model delete breast_cancer_classifier -y\n",
     "\n",
     "import IPython\n",
     "IPython.Application.instance().kernel.do_shutdown(restart=True)"
@@ -64,24 +54,6 @@
     "the end of the installation, the notebook kernel will automatically restart."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "e3955ff1",
-   "metadata": {},
-   "source": [
-    "Optional: If you are using [ZenML Cloud](https://zenml.io/cloud), execute the following cell with your tenant URL. Otherwise ignore."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e2587315",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -91,7 +63,6 @@
    "source": [
     "# Initialize ZenML and set the default stack\n",
     "!zenml init\n",
-    "\n",
     "!zenml stack set local-sagemaker-step-operator-wandb"
    ]
   },
@@ -112,19 +83,13 @@
     "from zenml.logger import get_logger\n",
     "from uuid import UUID\n",
     "\n",
-    "from typing import Optional, List\n",
-    "\n",
     "from zenml import pipeline\n",
     "\n",
     "from steps import (\n",
     "    data_loader,\n",
-    "    data_preprocessor,\n",
-    "    data_splitter,\n",
-    "    model_evaluator,\n",
     "    inference_preprocessor\n",
     ")\n",
-    "\n",
-    "from zenml.logger import get_logger\n",
+    "from pipelines import feature_engineering, training\n",
     "\n",
     "logger = get_logger(__name__)\n",
     "\n",
@@ -236,39 +201,97 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "b50a9537",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "@pipeline\n",
-    "def feature_engineering(\n",
-    "    test_size: float = 0.3,\n",
-    "    drop_na: Optional[bool] = None,\n",
-    "    normalize: Optional[bool] = None,\n",
-    "    drop_columns: Optional[List[str]] = None,\n",
-    "    target: Optional[str] = \"target\",\n",
-    "    random_state: int = 17\n",
-    "):\n",
-    "    \"\"\"Feature engineering pipeline.\"\"\"\n",
-    "    # Link all the steps together by calling them and passing the output\n",
-    "    # of one step as the input of the next step.\n",
-    "    raw_data = data_loader(random_state=random_state, target=target)\n",
-    "    dataset_trn, dataset_tst = data_splitter(\n",
-    "        dataset=raw_data,\n",
-    "        test_size=test_size,\n",
-    "    )\n",
-    "    dataset_trn, dataset_tst, _ = data_preprocessor(\n",
-    "        dataset_trn=dataset_trn,\n",
-    "        dataset_tst=dataset_tst,\n",
-    "        drop_na=drop_na,\n",
-    "        normalize=normalize,\n",
-    "        drop_columns=drop_columns,\n",
-    "        target=target,\n",
-    "        random_state=random_state,\n",
-    "    )\n",
-    "\n",
-    "    return dataset_trn, dataset_tst"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[0;31m# Apache Software License 2.0\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m# Copyright (c) ZenML GmbH 2024. All rights reserved.\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m# you may not use this file except in compliance with the License.\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m# You may obtain a copy of the License at\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m# http://www.apache.org/licenses/LICENSE-2.0\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m# Unless required by applicable law or agreed to in writing, software\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m# distributed under the License is distributed on an \"AS IS\" BASIS,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m# See the License for the specific language governing permissions and\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m# limitations under the License.\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0msteps\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mdata_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mdata_preprocessor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mdata_splitter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mzenml\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mzenml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_logger\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0mlogger\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_logger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mfeature_engineering\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mtest_size\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mdrop_na\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mnormalize\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mdrop_columns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"target\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mrandom_state\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0;34m\"\"\"\u001b[0m\n",
+      "\u001b[0;34m    Feature engineering pipeline.\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m    This is a pipeline that loads the data, processes it and splits\u001b[0m\n",
+      "\u001b[0;34m    it into train and test sets.\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m    Args:\u001b[0m\n",
+      "\u001b[0;34m        test_size: Size of holdout set for training 0.0..1.0\u001b[0m\n",
+      "\u001b[0;34m        drop_na: If `True` NA values will be removed from dataset\u001b[0m\n",
+      "\u001b[0;34m        normalize: If `True` dataset will be normalized with MinMaxScaler\u001b[0m\n",
+      "\u001b[0;34m        drop_columns: List of columns to drop from dataset\u001b[0m\n",
+      "\u001b[0;34m        target: Name of target column in dataset\u001b[0m\n",
+      "\u001b[0;34m        random_state: Random state to configure the data loader\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m    Returns:\u001b[0m\n",
+      "\u001b[0;34m        The processed datasets (dataset_trn, dataset_tst).\u001b[0m\n",
+      "\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0;31m# Link all the steps together by calling them and passing the output\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0;31m# of one step as the input of the next step.\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0;32mif\u001b[0m \u001b[0mrandom_state\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mraw_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mdataset_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_tst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_splitter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mdataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mdataset_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_tst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_preprocessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mdataset_trn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset_trn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mdataset_tst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset_tst\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mdrop_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_na\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mdrop_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_columns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_tst\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# let's see how feature engineering pipeline is implemented\n",
+    "%pycat pipelines/feature_engineering.py"
    ]
   },
   {
@@ -351,7 +374,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "client = Client()\n",
     "run = client.get_pipeline(\"feature_engineering\").last_run\n",
     "print(run.name)"
    ]
@@ -485,42 +507,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from sklearn.base import ClassifierMixin\n",
-    "from sklearn.linear_model import SGDClassifier\n",
-    "from typing_extensions import Annotated\n",
-    "from zenml import ArtifactConfig, step\n",
-    "from zenml.logger import get_logger\n",
-    "\n",
-    "logger = get_logger(__name__)\n",
-    "\n",
-    "\n",
-    "@step\n",
-    "def model_trainer(\n",
-    "    dataset_trn: pd.DataFrame,\n",
-    "    model_type: str = \"sgd\",\n",
-    ") -> Annotated[\n",
-    "    ClassifierMixin,\n",
-    "    ArtifactConfig(name=\"breast_cancer_classifier\", is_model_artifact=True),\n",
-    "]:\n",
-    "    \"\"\"Configure and train a model on the training dataset.\"\"\"\n",
-    "    target = \"target\"\n",
-    "    if model_type == \"sgd\":\n",
-    "        model = SGDClassifier()\n",
-    "    elif model_type == \"xgboost\":\n",
-    "        from xgboost import XGBClassifier\n",
-    "\n",
-    "        model = XGBClassifier()\n",
-    "    else:\n",
-    "        raise ValueError(f\"Unknown model type {model_type}\")\n",
-    "\n",
-    "    logger.info(f\"Training model {model}...\")\n",
-    "\n",
-    "    model.fit(\n",
-    "        dataset_trn.drop(columns=[target]),\n",
-    "        dataset_trn[target],\n",
-    "    )\n",
-    "    return model"
+    "# let's have a look at training pipeline\n",
+    "%pycat steps/inference_predict.py"
    ]
   },
   {
@@ -550,35 +538,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "@pipeline\n",
-    "def training(\n",
-    "    train_dataset_id: Optional[UUID] = None,\n",
-    "    test_dataset_id: Optional[UUID] = None,\n",
-    "    model_type: str = \"sgd\",\n",
-    "    min_train_accuracy: float = 0.0,\n",
-    "    min_test_accuracy: float = 0.0,\n",
-    "):\n",
-    "    \"\"\"Model training pipeline.\"\"\" \n",
-    "    if train_dataset_id is None or test_dataset_id is None:\n",
-    "        # If we dont pass the IDs, this will run the feature engineering pipeline   \n",
-    "        dataset_trn, dataset_tst = feature_engineering()\n",
-    "    else:\n",
-    "        # Load the datasets from an older pipeline\n",
-    "        dataset_trn = client.get_artifact_version(name_id_or_prefix=train_dataset_id)\n",
-    "        dataset_tst = client.get_artifact_version(name_id_or_prefix=test_dataset_id)\n",
-    "\n",
-    "    trained_model = model_trainer(\n",
-    "        dataset_trn=dataset_trn,\n",
-    "        model_type=model_type,\n",
-    "    )\n",
-    "\n",
-    "    model_evaluator(\n",
-    "        model=trained_model,\n",
-    "        dataset_trn=dataset_trn,\n",
-    "        dataset_tst=dataset_tst,\n",
-    "        min_train_accuracy=min_train_accuracy,\n",
-    "        min_test_accuracy=min_test_accuracy,\n",
-    "    )"
+    "# let's have a look at training pipeline\n",
+    "%pycat pipelines/inference.py"
    ]
   },
   {