A guide to successfully applied Machine Learning

It is relatively simple to build predictive models. ​However, it is NOT simple to deploy, scale, and maintain multiple models across an enterprise.​

Today, a successful Machine Learning project requires 90% percent engineering and 10% science. Enterprises hire Data Scientists to develop end-to-end Machine Learning solutions. They are required to…

Distributed Machine Learning with Python and Dask

Multi Node Distributed Training with Dask

Data Science is an experimental science. We can have an idea about which algorithm will perform “well” on a validation or test set, but how will we actually know until we try it? How many times have you heard a colleague say: “Which algorithm works best depends on the data…

Distributed Machine Learning with Python and Dask

An approach to scale your data wrangling tasks

If you have assembled a standalone cluster and have enough knowledge about how Dask works in the background, it’s time for some practical Data Science. By the way, you can find all my source code and data in my GitHub account.

What are we trying to predict?

I want to classify the reviews of Amazon products…

Distributed Machine Learning with Python and Dask

Dask is a package for Python which will “scale” your data science skills.

Collaboratively written by Pablo Salvador Lopez and Aadarsh Vadakattu

Dask will help you scale your Data Science skills using Python. You will be able to work with BIG DATA and scale your code, boosting your productivity.

Introduction To Dask

Welcome Data Science lover! You are interested in this article because either you have…

Pablo Salvador Lopez

Data Scientist/ ML engineer at Pkglobal. Problem-solving is most of what I do. ML ENTHUSIAST.

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