50+ days of Machine Learning

 Hello :) Today is Day 137! A quick summary of today: attended 'data analysis' and 'business tech' sessions during Day 2 of AWS Summit in Seoul I tried to take more notes with my laptop during the session today. First session - 'Data strategy for successful GenAI on AWS' A GenAI app is like an iceberg.  Under the water it consists of among other things data storage, database, data lake, data manipulation, data governance.  The session introduced us to RAG, and how we can either use an out-of-the-box language model, pre-train a model, or train a model from scratch.  As for a vector search AWS offers plenty of options: Amazon OpenSearch Service, OpenSearch Serverless, Aurora PostgreSQL, RDS for PostgreSQL, DocumentDB, DynamoDB via zero ETL, MemoryDB for redis, Neptune. As for a database, there are plenty of structured and nonstructured dbs.  Amazon Bedrock - allows for using out-of-the-box models for GenAI applications. It gives us the ability to use a simple API ...

 Hello :) Today is Day 136! A quick summary of today: Attended day 1 of AWS summit Seoul and sat in the 'banking, finance and fintech' talks - everything was related to AI (not surprisingly haha) It was all in Korean, so below I will try to give a general overview of the attended sessions + some extra activities that I did.  Something funny and interesting. There was a booth where you took a picture and your face was put on a medieval/pre-historic/renaissance figure. It is an event in Korea so I was curious whether it can recognise white people faces, and below is supposed to be me Haha. Interesting ^^ There was also a football kick booth where I queued for around 30mins to shoot twice and to try to kick and score with the highest speed (I placed rank 24 with 72km/h kick on target). They also had a model that takes a picture and puts a football jersey on the person Next, about the sessions.  They were all in Korean (the slides as well) and even though I took pictures putt...

 Hello :) Today is Day 135! A quick summary of today: covered module 2 of the mlops-zoomcamp by DataTalks club about experiment tracking and model management cut 2 more videos for the Scottish dataset project Firstly, about using mlflow for MLOps Maybe this is because I am starting to learn about MLOps for the 1st time and I don't know other tools, but WOW mlflow is amazing. Below are my notes from the module 2 lectures. Full code on my github repo. First, some important concepts ML experiment: the process of building an ML model experiment run: each trial in an ML experiment run artifact: any file that is associated with an ML run experiment metadata What's experiment tracking? the process of keeping track of all the relevant info from an ML experiment (could include source code, environment, data, model, hyperparams, metrics, other - these can vary) Why is experiment tracking important? reproducability organization optimization Why can't we just use an excel spreadsheet...