Data Science DevOps ETL 
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.
AWS Machine Learning Blog
JANUARY 5, 2024
Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems.
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Marktechpost
JULY 5, 2024
Programming for Data Science with Python This course series teaches essential programming skills for data analysis, including SQL fundamentals for querying databases and Unix shell basics. Students also learn Python programming, from fundamentals to data manipulation with NumPy and Pandas, along with version control using Git.
AWS Machine Learning Blog
FEBRUARY 2, 2024
The embeddings are captured in Amazon Simple Storage Service (Amazon S3) via Amazon Kinesis Data Firehose , and we run a combination of AWS Glue extract, transform, and load (ETL) jobs and Jupyter notebooks to perform the embedding analysis. Set the parameters for the ETL job as follows and run the job: Set --job_type to BASELINE.
Mlearning.ai
APRIL 24, 2023
Over the past few years Data Science has MIGRATED from individual computers to service cloud platforms. I just finished learning Azure’s service cloud platform using Coursera and the Microsoft Learning Path for Data Science. It will take a couple of months but it is worth it!
The MLOps Blog
SEPTEMBER 7, 2023
From writing code for doing exploratory analysis, experimentation code for modeling, ETLs for creating training datasets, Airflow (or similar) code to generate DAGs, REST APIs, streaming jobs, monitoring jobs, etc. Related post MLOps Is an Extension of DevOps. Explore how these principles can elevate the quality of your ETL work.
AWS Machine Learning Blog
SEPTEMBER 1, 2023
These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.
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