diff --git a/2023 - Amsterdam/Day 1/1 - Foo (main)/15:10 - Power Users, Long Tail Users, and Everything In Between: Choosing Meaningful Metrics and KPIs for Product Strategy/slides.pdf.pdf b/2023 - Amsterdam/Day 1/1 - Foo (main)/15:10 - Power Users, Long Tail Users, and Everything In Between: Choosing Meaningful Metrics and KPIs for Product Strategy/slides.pdf.pdf
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diff --git a/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/10:30 - Designing a Machine Learning System/README.md b/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/10:30 - Designing a Machine Learning System/README.md
index c3ca5e3..c447977 100644
--- a/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/10:30 - Designing a Machine Learning System/README.md	
+++ b/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/10:30 - Designing a Machine Learning System/README.md	
@@ -1,5 +1,6 @@
 # Designing a Machine Learning System
 by Jetze Schuurmans, Roy van Santen
 * [Talk info](https://amsterdam2023.pydata.org/cfp/talk/N8DYS7/)
+* [Link to slide content](https://xebia.com/blog/how-ml-system-design-helps-us-to-make-better-ml-products/)
 ## Abstract
 Are you a machine learning practitioner struggling with designing, reasoning, and communicating about ML systems? Then this session is for you! With the industry moving towards end-to-end ML teams to enable them to implement MLOps practices, it is paramount for you to understand ML from a systems perspective. In this hands-on session, you will gain a thorough understanding of the technical intricacies of designing valuable, reliable and scalable ML systems.
diff --git a/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/12:00 - Uncertainty visualization with ArviZ/README.md b/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/12:00 - Uncertainty visualization with ArviZ/README.md
index bf4d14b..7f9a8ea 100644
--- a/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/12:00 - Uncertainty visualization with ArviZ/README.md	
+++ b/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/12:00 - Uncertainty visualization with ArviZ/README.md	
@@ -1,5 +1,6 @@
 # Uncertainty visualization with ArviZ
 by Oriol Abril Pla
 * [Talk info](https://amsterdam2023.pydata.org/cfp/talk/DH3N3R/)
+* [Link to Slides](https://oriolabril.github.io/visualitzacio_incertesa/en.html)
 ## Abstract
 Learn how to visualize uncertainty in parameters or predictions using mutiple visualizations adapted to your data and task
diff --git a/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/13:30 - Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro/README.md b/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/13:30 - Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro/README.md
index 68680fa..0d9998d 100644
--- a/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/13:30 - Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro/README.md	
+++ b/2023 - Amsterdam/Day 1/4 - Hello, World! (Tutorials)/13:30 - Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro/README.md	
@@ -1,7 +1,8 @@
-# Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro
-by sktime community
-* [Talk info](https://amsterdam2023.pydata.org/cfp/talk/F8EW7P/)
-## Abstract
+# Probabilistic predictions: probabilistic forecasting with sktime and probabilistic regression with skpro
+by sktime community
+* [Talk info](https://amsterdam2023.pydata.org/cfp/talk/F8EW7P/)
+* [Link to Tutorial](https://github.com/sktime/sktime-tutorial-pydata-Amsterdam-2023)
+## Abstract
 Probabilistic predictions are predictions that include some statements about uncertainty of the prediction, e.g., prediction intervals that make statements about a likely range of values that a prediction can take.
 This workshop gives an introduction on making probabilistic predictions with the sktime and skpro python packages, for forecasting and supervised regression. Both packages are sklearn-compatible, built using skbase, with composable and modular interfaces.
-The presentation includes a practical primer of different types of probabilistic predictions, algorithms and estimators, and evaluation workflows, with python code examples.
+The presentation includes a practical primer of different types of probabilistic predictions, algorithms and estimators, and evaluation workflows, with python code examples.