Focus on high tech

Shimoku's blog. Where we will talk about artificial intelligence and machine learning for businesses.
eCharts and Shimoku
Create eCharts components with Shimoku and Python.
Shimoku sponsors Aspar Team in MotoGP
Shimoku sponsors the legendary Aspar Team in MotoGP
Product updates Q1-Q2
We believe in a tool that allows anyone to create Machine Learning that can reach business users frictionlessly. In that pursuit we are driven by the following features to change the world
The breakout
welcome to the new Data Product era‍That is why we thought of a unique architecture, one that would give me, and anyone else in the same situation, the autonomy a data specialist (data analyst, data scientist, data engineer, or some IT persona with an interest in analytics)
A word on AI Apps
Data products and the data mesh importance has emerged as a consequence of a key factor: the increase in the demand for Artificial Intelligence in the past 5 years.
The handcuffs
while working on data projects I was tired that I couldn't easily share Machine Learning insights that could boost my clients’ business performance without a full-stack IT team.I was tired of how slow it was to show data analytics to people in a professional way with a progressive-web app technology (PWA from now on).
The 80% data cleaning fallacy
It is common to hear that the real fight is data cleaning when developing a data product, because it takes about 80% of our time.
The roadblock
We, the data specialists, can do amazing things that rarely arrive at production level. It is like we have been unfinished as a workforce.
The status quo of Data Apps & AI Apps
To build a data app one needs:‍The client tier, where the Front-End developer struggles to create the new charts the product demands;‍

Do you have any questions?
Frequently Asked Questions

Don't be left in doubt. We know that we are a complex and
new product, any doubt is welcome.

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