Data Science Dojo

OCTOBER 31, 2024

Applied Machine Learning Scientist Description : Applied ML Scientists focus on translating algorithms into scalable, real-world applications. Demand for applied ML scientists remains high, as more companies focus on AI-driven solutions for scalability.

Data Science 195

Data Science Data Scientist Machine Learning Machine Learning 195

AWS Machine Learning Blog

FEBRUARY 21, 2025

Rockets legacy data science environment challenges Rockets previous data science solution was built around Apache Spark and combined the use of a legacy version of the Hadoop environment and vendor-provided Data Science Experience development tools. Apache HBase was employed to offer real-time key-based access to data.

Data Science 69

Data Science AWS Hadoop Data Scientist 69

The MLOps Blog

MAY 31, 2023

And eCommerce companies have a ton of use cases where ML can help. The problem is, with more ML models and systems in production, you need to set up more infrastructure to reliably manage everything. And because of that, many companies decide to centralize this effort in an internal ML platform. But how to build it?

ML 59

ML ML Algorithm Machine Learning 59

Becoming Human

JANUARY 23, 2023

First understand ML and DL so, in Machine learning and Deep learning we perform some mathematical operations on data and make the models, and these models help us to predict future outcomes. After understanding data science let’s discuss the second concern “ Data Science vs AI ”. So, it looks like magic but it’s not magic.

Data Science 105

Data Science Big Data Big Data Deep Learning 105

The MLOps Blog

JANUARY 23, 2023

Dolt LakeFS Delta Lake Pachyderm Git-like versioning Database tool Data lake Data pipelines Experiment tracking Integration with cloud platforms Integrations with ML tools Examples of data version control tools in ML DVC Data Version Control DVC is a version control system for data and machine learning teams. DVC Git LFS neptune.ai

ML 52

ML ML Data Lakes Machine Learning 52

Mlearning.ai

MAY 16, 2023

ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. ETL Design Pattern Here is an example of how the ETL design pattern can be used in a real-world scenario: A healthcare organization wants to analyze patient data to improve patient outcomes and operational efficiency.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

phData

FEBRUARY 25, 2025

With so many different ways to get data into Snowflakefrom traditional ETL tools to APIs, batch processing, and streaming datait can quickly become overwhelming to choose the right approach. In our Hadoop era, we extensively leveraged Apache NiFi to integrate large ERP systems and centralize business-critical data.

Data Pipeline 52

Data Pipeline ETL Data Engineering Data Engineer 52