chore(content): finetuning

next/content/talks/2024_HaystackUS.md

@@ -2,7 +2,9 @@
 title: "Zucchini or Cucumber? Benchmarking Embeddings for Similar Image Retrieval thanks to your weekly Grocery shopping"
 description: "In this talk, we present the process of building a benchmark, the results of our evaluation, and the lessons we learned from those to improve our Image Recommendation API. You'll learn what makes a dataset suitable for evaluating a problem, how to pick the right metrics to evaluate, and other useful tips to evaluate your own recommender systems on other specific domains based on any available data!"
 date: '2024-04-24'
+context: "Haystack US Conference 2024"
 slides: https://alg.li/haystack24-slides
 video: https://www.youtube.com/watch?_hsmi=308021690&v=4xF0R1wG0Zk
 event: https://haystackconf.com/2024/
+org: "OpenSource Connections"
 ---

next/content/talks/2024_MICES.md

@@ -4,7 +4,7 @@ description: "What data do you use to fuel your product carousels? Textual attri
 date: '2024-06-12'
 slides: https://alg.li/mices24
 video: https://www.youtube.com/watch?v=un5PKNsdHPM
-event: https://mices.co/
 context: "MICES Conference in Berlin"
+event: https://mices.co/
 org: "OpenSource Connections"
 ---

next/content/talks/dx_for_ml.md

@@ -4,5 +4,6 @@ description: "In a Machine Learning product or API, what makes a good Developer
 date: '2022-09-25'
 context: Anil Kumar Krishnashetty's Podcast
 video: https://www.youtube.com/watch?v=_c_U1O9SRqk
+event: https://www.youtube.com/@ANILKUMARKRISHNASHETTY
 slides: https://medium.com/@anilbms75/developer-experience-from-machine-learning-engineer-perspective-2c81b8ce46be
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+---
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next/content/talks/ecir.md

 ---
 title: "ECIR23: Building a Business-Aware Image Retrieval API"
-description: "In 2023, users expect to be understood across various interaction modes. From YouTube transcript search to Pinterest image match, they are used to search interfaces understanding different modalities beyond pure text.  
-In this talk, I share learnings building an image recommendation API for various business needs, showing how you can leverage powerful computer vision models to serve different goals by packaging it into a flexible API and deploying it to a global audience."
-context: "ECIR conference organized by the British Computer Society"
+description: "In 2023, users expect to be understood across various interaction modes. From YouTube transcript search to Pinterest image match, they are used to search interfaces understanding different modalities beyond pure text. In this talk, I share learnings building an image recommendation API for various business needs, showing how you can leverage powerful computer vision models to serve different goals by packaging it into a flexible API and deploying it to a global audience."
+context: ECIR conference
+org: British Computer Society
 date: '2023-04-06'
 slides: https://alg.li/ecir23-slides
 event: https://ecir2023.org/
----
+---
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