First, it was Docker, then Next it was Kubernetes. KFServing + Vision + Mysql for an end-to-end architecture on GCP. Next for project purpose worked on Vision + AWS. Worked on AWS Redshit for AI / ML and Stored Procedures. Then Again jumped on to Azure Synapse, Azure Cognitive Vision + Media Services. On and off Picked up on optimization/routing/pulp / seems interesting. Domain was mostly CVM, Supply Chain, Retail and picking up on trends
When you look back from time to time there is a lot of stuff to catch up on the vision, new approaches, ML is giving more MLOps, Feature stores, Vision new tracking techniques, Everything transformer, bert. Expertise in Vision vs Data vs NLP everything needs time, experimentation, and constant exploration.
Domain Learning, Cloud Exposure, Data Learning, ML Techniques, Vision updates :) The list is never-ending!!!
Bookmark your learning's time to time. Looking back it feels like explored everything a bit but still so many unknowns. As always sometimes all these dots will help to paint a big picture. After an experiment you know a bit of implementation, The curiosity goes once you get the crux of it. The same TSQL, SQL Basics, performance tuning translated from RDBMS in 2000 to CAP theorem, the advent of NoSQL, large scale parallel processing with Hadoop, Columnar indexes, JSON models, Making everything read-only with Spark RDD. What is a limitation today that will be a motivation for something tomorrow?
What we know is a spark, curiosity and the ability to build the perspective connecting old and new things, learning the required pieces building up a good business use case is the satisfaction of learning!!!
Keep Going!!!
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