Updated the class diagrams

program/cohort.ipynb

@@ -221,7 +221,11 @@
  "source": [
  "## Session 1 - Production Machine Learning is Different\n",
  "\n",
- "In this session we'll run Exploratory Data Analysis on the [Penguins dataset](https://www.kaggle.com/parulpandey/palmer-archipelago-antarctica-penguin-data) and we'll build a simple [SageMaker Pipeline](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html) with one step to split and transform the data. We'll use a [Scikit-Learn Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) for the transformations, and a [Processing Step](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#step-type-processing) with a [SKLearnProcessor](https://sagemaker.readthedocs.io/en/stable/frameworks/sklearn/sagemaker.sklearn.html#scikit-learn-processor) to execute a preprocessing script. Check the [SageMaker Pipelines Overview](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html) for an introduction to the fundamental components of a SageMaker Pipeline.\n"
+ "In this session we'll run Exploratory Data Analysis on the [Penguins dataset](https://www.kaggle.com/parulpandey/palmer-archipelago-antarctica-penguin-data) and we'll build a simple [SageMaker Pipeline](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html) with one step to split and transform the data. \n",
+ "\n",
+ "<a href=\"images/training.png\" target=\"_blank\"> <img src=\"images/training.png\" alt=\"Training\" style=\"max-width: 750px;\" /></a>\n",
+ "\n",
+ "We'll use a [Scikit-Learn Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) for the transformations, and a [Processing Step](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#step-type-processing) with a [SKLearnProcessor](https://sagemaker.readthedocs.io/en/stable/frameworks/sklearn/sagemaker.sklearn.html#scikit-learn-processor) to execute a preprocessing script. Check the [SageMaker Pipelines Overview](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html) for an introduction to the fundamental components of a SageMaker Pipeline.\n"
  ]
  },
  {
@@ -1759,6 +1763,8 @@
  "\n",
  "This session extends the [SageMaker Pipeline](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html) we built in the previous session with a step to train a model. We'll explore the [Training Step](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#step-type-training) and the [Tuning Step](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#step-type-tuning).\n",
  "\n",
+ "<a href=\"images/training.png\" target=\"_blank\"> <img src=\"images/training.png\" alt=\"Training\" style=\"max-width: 750px;\" /></a>\n",
+ "\n",
  "We'll introduce [Amazon SageMaker Experiments](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) and use them during training. For more information about this topic, check the [SageMaker Experiments' SDK documentation](https://sagemaker.readthedocs.io/en/v2.174.0/experiments/sagemaker.experiments.html).\n"
  ]
  },
@@ -2339,6 +2345,8 @@
  "\n",
  "This session extends the [SageMaker Pipeline](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html) with a step to evaluate the model and register it if it reaches a predefined accuracy threshold.\n",
  "\n",
+ "<a href=\"images/training.png\" target=\"_blank\"> <img src=\"images/training.png\" alt=\"Training\" style=\"max-width: 750px;\" /></a>\n",
+ "\n",
  "We'll use a [Processing Step](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#step-type-processing) to execute an evaluation script. We'll use a [Condition Step](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#step-type-condition) to determine whether the model's accuracy is above a threshold, and a [Model Step](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#step-type-model) to register the model in the [SageMaker Model Registry](https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html).\n"
  ]
  },
@@ -3142,7 +3150,7 @@
  "\n",
  "In this session we'll explore how to deploy a model to a SageMaker Endpoint and how to use a SageMaker Inference Pipeline to control the data that goes in and comes out of the endpoint.\n",
  "\n",
- "<img src='images/deployment.png' alt='Deployment Pipeline' width=\"730\" />\n",
+ "<a href=\"images/deployment.png\" target=\"_blank\"> <img src=\"images/deployment.png\" alt=\"Deployment\" style=\"max-width: 750px;\" /></a>\n",
  "\n",
  "Let's start by defining the name of the endpoint where we'll deploy the model and creating a constant pointing to the location where we'll store the data that the endpoint will capture:"
  ]
@@ -4766,7 +4774,7 @@
  "\n",
  "In this session we'll set up a monitoring process to analyze the quality of the data our endpoint receives and the endpoint predictions. For this, we need to check the data received by the endpoint, generate ground truth labels, and compare them with a baseline performance.\n",
  "\n",
- "<img src=\"images/monitoring.png\" alt=\"Data and Model Monitoring\" width=\"730\" />\n",
+ "<a href=\"images/monitoring.png\" target=\"_blank\"> <img src=\"images/monitoring.png\" alt=\"Monitoring\" style=\"max-width: 750px;\" /></a>\n",
  "\n",
  "To enable this functionality, we need a couple of steps:\n",
  "\n",