Focus on sale.

Shimoku's blog. Where we will talk about artificial intelligence and machine learning focused on selling more.

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;‍
The secret sauce
In this pursuit of becoming independent as a data professional, the first thing that comes to our (in American accent “our” sounds like “R”) mind consists of building the full stack on our own, with Python
In Python we trust
What is the secret sauce to create agile data apps, AI apps or any kind of data products, then? To dig into it we’d better review Plotly capabilities to grasp all the key concepts:

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