Let’s Get Technical

Get to know some of the tools that we use and how we apply our expertise on Machine Learning, Data Science and Data Engineering.

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Let’s Get Technical

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Tools We Use

Get to Know Our Process

Our Specialities

atmo

Machine Learning Engineering & MLOps

In mature teams, where the ML product is already generating value or needing to be built in the right way. We create production-ready ecosystems of ML modeling with the right MLOps, using code and data versioning, data cataloging (the data Science way), feature store implementations, ML data orchestration pipelines, data verification, continuous technical debt reduction, among other good practices.

What Industries is it for?

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Telco
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Public Sector
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Pharma

Mostly Python Stack:

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DVC
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MLFlow
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DigDag or Airflow Orchestration
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Great expectations
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Declarative CI/CD
atmo

Data Engineering & Big Data

We also build general, performant, data pipeline infrastructures for regular BI and analytics purposes. We ensure data quality and deliver operational analytics by orchestrating reliable data ETLs or streaming solutions. From customized scrapping systems, complex dashboards or full data architecture.

What Industries is it for?

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Pharma
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SaaS
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Public Sector

Our typical Data Engineering Stack:

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Airflow or DigDag
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Kafka
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HDFS Storage
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Performant SQL-ETL Pipelines
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Data Lake Layer
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Versioned Data Warehouse Layer
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Cloud Deployment
atmo

ML & Data Training

We train and build data squads to autonomously master their own data challenges. Our learning paths have trained more than 200 users on the most up-to-date tech stack in the DS and DE space.

What Industries is it for?

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Telco
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Government
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Banking
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Industrial

Stack:

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Foundational Course

We believe that there is no Data Science or Engineering without some good solid foundations on programming and software engineering. Our Foundational course teaches OOP, Linting, CI/CD Pipelines and Testing frameworks with Python.
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Data Science

Our data science path teaches the most important Python implentations of famous data science and machine learning models. We also go in depth into the mathematical explanations of the models so that students are able to know which model to apply in different situations.
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Data Engineering

Our data engineering path focuses on teaching Airflow, Docker and dbt technologies, helping our customers making the transition to an open source tech stack.

Client Success