AI/Machine Learning Engineer who has worked on various aspects of Data Science/ML problems from brainstorming, prototyping, to productionizing and maintaining solutions, primarily specializing in chipping away the "Hidden Technical Debt in Machine Learning"

I have worked in the following areas/with the following tools (also few buzzwords for the search engines):
Machine Learning, Data Engineering, Data Analysis, Data Modelling, Algorithms, Classification, Regression, Supervised, Unsupervised, Deep Learning, Evaluation, Hyperparameter Tuning, Deployment/Productionizing, Monitoring/Maintenance, DevOps, MLOps, Distributed Computing, CI/CD, Testing, TDD
Python, R, C, C++, Pandas, Numpy, Dask, Scikit Learn, Tensorflow, Keras, Flask, Django
Git, Bash, Docker, Kubernetes, Helm, Kops, Kubeflow, MLflow
AWS, GCP, Sagemaker, API Gateway, AI Platform, ML Engine, S3, Cloud Storage, EC2, VM, ECR, GCR

A bit of a background on how I got into the field and why I am still here -> I have always been passionate about Computers and the ability to make them solve problems for us. I value encountering challenging situations, enjoy analyzing & solving problems and seeing the results. The work done by computers tend to have these characteristics in abundance. I am fascinated by that and want to solve as many problems as I can. Code is a by product of this phenomenon.
Over time, I have acquired technical skills to code in a bunch of languages, tools. But again, I value the solving problems more than the tools used for that purpose.

Strong believer in 👉🏾 "AI is the new Electricity".