Hany Hossny
,
January 6, 2023 
Machine Learning vs Traditional Modelling/Programming Data science is the process of building models that analyze historical data and learn the pattern bridging the input and the output in a way that can be repeated using future data coming as input and predicting...
Hany Hossny
,
January 6, 2023 
I needed this list of questions when I was interviewing for data science or ML engineering roles. This list aim to help data science leaders interview DS/ML engineers and help DS/ML engineers to study what is important and ace their interviews. What is the...
Hany Hossny
,
January 6, 2023 
Once the ML model is deployed to production, many issues occur in runtime reducing the predictive accuracy and shortening the lifetime. Without monitoring, the model can easily hit any of the runtime bombs and fail due to data drift, concept drift, anomalies, etc....
Hany Hossny
,
January 4, 2023 
ML operations aim to accelerate, scale and sustain model development, deployment and maintenace. MLOps is a typical Dev-Ops process, which is the sequence of actions taking an ML project from development to production, except MLOPs have a different nature than...
Hany Hossny
,
January 4, 2023 
Businesses around the globe use machine learning to predict their sales, profits, costs, performance, and use these predictions to build actionable insights. These insights help decision-makers to take the right decision according to the current situation after...
Hany Hossny
,
December 24, 2022 
The Data Science Process Data science projects start with business needs and go through data analysis, model development, deployment to production, quality assurance, and after-deployment support. The image below illustrates the key steps and actions...
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