Fix instance type and cleanup notebook

Author: brightsparc

README.md

@@ -134,7 +134,7 @@ This section outlines cost considerations for running the Drift Detection Pipeli
 - **SageMaker** – Prices vary based on EC2 instance usage for the Notebook Instances, Model Hosting, Model Training and Model Monitoring; each charged per hour of use. For more information, see [Amazon SageMaker Pricing](https://aws.amazon.com/sagemaker/pricing/).
   - The three `mml.m5.xlarge` *baseline, training and evaluation jobs* run for approx 20 minutes at $0.23 an hour, and cost less than $1.
   - The one `ml.t2.medium` instance for staging *hosting* endpoint costs $0.056 per hour, or $1.34 per day.
-  - The two `ml.t2.large` instances for production *hosting* endpoint at 2 x $0.111 per hour, or $5.33 per day.
+  - The two `ml.m5.large` instances for production *hosting* endpoint at 2 x $0.115 per hour, or $5.52 per day.
   - The one `ml.m5.xlarge` instance for *model monitor* schedule at $0.23 an hour, and cost less than $1 per day.
 - **S3** – Low cost, prices will vary depending on the size of the models/artifacts stored. The first 50 TB each month will cost only $0.023 per GB stored. For more information, see [Amazon S3 Pricing](https://aws.amazon.com/s3/pricing/).
 - **Lambda** - Low cost, $0.20 per 1 million request see [AWS Lambda Pricing](https://aws.amazon.com/lambda/pricing/).

build_pipeline/drift-detection.ipynb

@@ -374,8 +374,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from botocore.exceptions import WaiterError\n",
-    "\n",
     "predictor = wait_for_predictor(\"prod\")"
    ]
   },

deployment_pipeline/prod-config.json

@@ -1,7 +1,7 @@
 {
     "stage_name": "prod",
     "instance_count": 2,
-    "instance_type": "ml.t2.large",
+    "instance_type": "ml.m5.large",
     "auto_scaling": {
         "min_capacity": 2,
         "max_capacity": 4