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traditional ml model


How Automated Machine Learning Addresses the Challenges of Traditional ML Models?

#artificialintelligence

Machine learning is a subsidiary of artificial intelligence which aids the systems to learn through consequent experience.


How Automated Machine Learning Addresses the Challenges of Traditional ML Models?

#artificialintelligence

Automated Machine learning is considered as a suitable and comprehensive approach to address and eradicate the challenges associated with machine learning algorithms and models. Automated machine learning ensures end-to-end automation of the ML algorithm and model. It is designed to conduct automated data analysis, so that accurate and précise results can be achieved. Automated Machine learning algorithm unburdens the data scientists, as it not only cleans and collects the data but also automatically trains the models as well. Through its automated feature engineering attribute, AutoML automatically collects the data, extracts meaningful information, and detects any distorted data in the entire process.


How to run your ML model Predictions 50 times faster?

#artificialintelligence

With the advent of so many computing and serving frameworks, it is getting stressful day by day for the developers to put a model into production. If the question of what model performs best on my data was not enough, now the question is what framework to choose for serving a model trained with Sklearn or LightGBM or PyTorch. And new frameworks are being added as each day passes. So is it imperative for a Data Scientist to learn a different framework because a Data Engineer is comfortable with that, or conversely, does a Data Engineer need to learn a new platform that the Data Scientist favors? Add to that the factor of speed and performance that these various frameworks offer, and the question suddenly becomes even more complicated.