class ZarinaNurzhanova:
def __init__(self):
self.role = ["Backend Developer", "Data Engineer", "ML Engineer"]
self.location = "Kazakhstan 🇰🇿"
self.languages = ["Python", "SQL", "Bash"]
self.currently = "Building scalable data pipelines, ML systems & backend services"
def daily_stack(self):
return {
"backend": ["FastAPI", "Django", "Flask", "asyncio"],
"data": ["Apache Airflow", "Spark", "Kafka", "dbt"],
"ml": ["PyTorch", "scikit-learn", "CatBoost", "XGBoost", "MLflow"],
"storage": ["PostgreSQL", "ClickHouse", "Redis", "S3"],
"devops": ["Docker", "Kubernetes", "GitHub Actions", "Terraform"],
}
def interests(self):
return [
"Machine Learning",
"MLOps",
"Data Engineering",
"Distributed Systems",
"Forecasting",
"LLM Applications"
]
def motto(self):
return "If it's manual, it should be automated."
❤️🔥
Focusing
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gpu_flow
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kazakhstan-dam-data-pipeline
kazakhstan-dam-data-pipeline PublicThis repository contains the full data pipeline and prepared datasets for the master’s thesis: “Developing a Hybrid CNN–ML Method and Probabilistic Intervals for the Day-Ahead Electricity Market of…
Python
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