update session learning objectives

s5_continuous_integration/README.md

@@ -10,15 +10,15 @@
   <img src="../figures/icons/cml.png" width="130">
 </p>
 
-Continues integration is an sub discipline of the general field of *Continues X*. Continuous X is one of the core
-elements of modern Devops, and by extension MLOps. Continuous X assumes that we have a (long) developer pipeline
+Continues integration is a sub-discipline of the general field of *Continues X*. Continuous X is one of the core
+elements of modern DevOps, and by extension MLOps. Continuous X assumes that we have a (long) developer pipeline
 (see image below) where we want to make some changes to our code e.g:
 
 * Update our training data or data processing
 * Update our model architecture
 * Something else...
 
-Basically any code change we will expect will have a influence on the final result. The problem with
+Basically, any code change we will expect will have a influence on the final result. The problem with
 doing changes to the start of our pipeline is that we want the change to propagate all the way through
 to the end of the pipeline.
 
@@ -29,9 +29,9 @@ to the end of the pipeline.
 </figcaption>
 </figure>
 
-This is where *continuous X* comes into play. The word *continuous* here refer to the fact that the
-pipeline should *continuously* be updated as we make code changes. You can also choose to think of this
-as *automatization* of processes. The *X* then covers that the process we need to go through to
+This is where *continuous X* comes into play. The word *continuous* here refers to the fact that the
+pipeline should *continuously* be updated as we make code changes. You can also choose to think of this as the
+*automatization* of processes. The *X* then covers that the process we need to go through to
 automate steps in the pipeline, depends on where we are in the pipeline e.g. the tools needed to
 do continuous integration is different from the tools need to do continuous delivery.
 
@@ -40,12 +40,6 @@ takes care of the first part of the developer pipeline that has to do with the c
 testing. This is paramount to step in automatization as we would rather catch bugs in the beginning of our pipeline
 than in the end.
 
-* How to write unittests for our applications
-* How to automate tests being run on code changes
-* How to secure we do not commit code that does not follow our code standards
-* How we can automate building of docker images
-* How we can automate training of our machine learning pipeline
-
 !!! tip "Learning objectives"
 
     The learning objectives of this session are:

s6_the_cloud/README.md

@@ -53,4 +53,4 @@ or going through the [general docs](https://cloud.google.com/docs).
     * Being able to start different compute instances and work with them
     * Know how to setup continues integration workflows for building of docker images
     * Knowledge about how to store data and containers/artifacts in cloud buckets
-    * Being able to train simple deep learning models using a combination of services
+    * Being able to train simple Deep Learning models using a combination of cloud services

s7_deployment/README.md

@@ -23,3 +23,12 @@ contained such that people can just execute without looking (too hard) at the co
 
 In this session we try to look at methods specialized towards deployment of models on your local machine and
 also how to deploy services in the cloud.
+
+!!! tip "Learning objectives"
+
+    The learning objectives of this session are:
+
+    * Understand the basics of requests and APIs
+    * Can create custom APIs using the framework `fastapi` and run it locally
+    * Knowledge about serverless deployments and how to deploy custom APIs using both serverless functions and
+      serverless containers

s8_monitoring/README.md

@@ -36,3 +36,11 @@ drifted away from the distribution of data your model was trained on. Such monit
 learning applications and needs to be handled separately.
 
 We are in this session going to see examples of both kinds of monitoring.
+
+!!! tip "Learning objectives"
+
+    The learning objectives of this session are:
+
+    * Understand the concepts of data drifting in machine learning applications
+    * Can detect data drifting using the `evidently` framework
+    * Understand the importance of different system level monitoring and can conceptually implement it

s9_scalable_applications/README.md

@@ -29,3 +29,13 @@ that framework, but may require you do seek out how to specifically to it.
 
 If you manage to complete all modules in this session, feel free to checkout the *extra* module on scalable
 [hyperparameter optimization](../s10_extra/hyperparameters.md).
+
+!!! tip "Learning objectives"
+
+    The learning objectives of this session are:
+
+    * Understand how data loading during training can be parallelized and have experimented with it
+    * Understand the different paradigms for distributed training and can run multi-gpu experiments using the
+      framework `pytorch-lightning`
+    * Knowledge of different ways, including quantization, pruning, architecture tuning etc. to improve inference
+      speed