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Let's Get Technical

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Tools We Use:

Our Industries.

Our Specialties

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.

Stack

1. Python: Airflow, Kafka, HDFS'ish Storage
2. SQL-ETL: Data Warehouses (snowflake, etc.)

Industries

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.

Stack

Mostly Python Stack: DVC, MLFlow, digdag or airflow orchestration, Great Expectations, Declarative CI/CD frameworks

Industries

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.

Stack

1. Python: Airflow, Kafka, HDFS'ish Storage;
2. SQL-ETL: Data Warehouses (snowflake, etc.)

Industries

ML & Data Training

To train and build squads to autonomously handle their won data challenges.

Stack

1. Data Science: Data Science Academy working material;
2. Data Engineering: Gap analysis and Course Adaption using platforms such as DataCamp, etc.

Industries

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