Add pointers to instructions for the exercise solutions

19_training_and_deploying_at_scale.ipynb

@@ -1164,6 +1164,74 @@
    "source": [
     "And that's all for today! Hope you found this useful. 😊"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise Solutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. to 8.\n",
+    "\n",
+    "See Appendix A."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9.\n",
+    "_Exercise: Train a model (any model you like) and deploy it to TF Serving or Google Cloud AI Platform. Write the client code to query it using the REST API or the gRPC API. Update the model and deploy the new version. Your client code will now query the new version. Roll back to the first version._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Please follow the steps in the [Deploying TensorFlow models to TensorFlow Serving](http://localhost:8888/notebooks/19_training_and_deploying_at_scale.ipynb#Deploying-TensorFlow-models-to-TensorFlow-Serving-(TFS)) section above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 10.\n",
+    "_Exercise: Train any model across multiple GPUs on the same machine using the `MirroredStrategy` (if you do not have access to GPUs, you can use Colaboratory with a GPU Runtime and create two virtual GPUs). Train the model again using the `CentralStorageStrategy `and compare the training time._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Please follow the steps in the [Distributed Training](#Distributed-Training) section above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 11.\n",
+    "_Exercise: Train a small model on Google Cloud AI Platform, using black box hyperparameter tuning._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Please follow the instructions on pages 716-717 of the book. You can also read [this documentation page](https://cloud.google.com/ai-platform/training/docs/hyperparameter-tuning-overview) and go through the example in this nice [blog post](https://towardsdatascience.com/how-to-do-bayesian-hyper-parameter-tuning-on-a-blackbox-model-882009552c6d) by Lak Lakshmanan."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {