ETL ML Software Engineer 
ODSC - Open Data Science
OCTOBER 9, 2024
The rapid evolution of AI is transforming nearly every industry/domain, and software engineering is no exception. But how so with software engineering you may ask? These technologies are helping engineers accelerate development, improve software quality, and streamline processes, just to name a few.
The MLOps Blog
SEPTEMBER 7, 2023
This situation is not different in the ML world. Data Scientists and ML Engineers typically write lots and lots of code. Building a mental model for ETL components Learn the art of constructing a mental representation of the components within an ETL process.
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The MLOps Blog
AUGUST 3, 2023
This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Stefan: Yeah.
AWS Machine Learning Blog
APRIL 24, 2023
In this post, we explore how AWS customer Pro360 used the Amazon Comprehend custom classification API , which enables you to easily build custom text classification models using your business-specific labels without requiring you to learn machine learning (ML), to improve customer experience and reduce operational costs. overall accuracy.
Marktechpost
JULY 5, 2024
It covers advanced topics, including scikit-learn for machine learning, statistical modeling, software engineering practices, and data engineering with ETL and NLP pipelines. The program culminates in a capstone project where learners apply their skills to solve a real-world data science challenge.
The MLOps Blog
JANUARY 23, 2023
Data version control in machine learning vs conventional software engineering Data version control in machine learning and conventional software engineering have some similarities, but there are also some key differences to consider. This is where data versioning comes in. DVC Git LFS neptune.ai neptune.ai
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.
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