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Why Software Engineers Should Be Embracing AI: A Guide to Staying Ahead

ODSC - Open Data Science

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.

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Software Engineering Patterns for Machine Learning

The MLOps Blog

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.

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Top AI/Machine Learning/Data Science Courses from Udacity

Marktechpost

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.

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Azure service cloud summarized: Part I

Mlearning.ai

Learning about the framework of a service cloud platform is time consuming and frustrating because there is a lot of new information from many different computing fields (computer science/database, software engineering/developers, data science/scientific engineering & computing/research).

DevOps 52
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Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart

AWS Machine Learning Blog

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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Learnings From Building the ML Platform at Stitch Fix

The MLOps Blog

Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. At a high level, we are trying to make machine learning initiatives more human capital efficient by enabling teams to more easily get to production and maintain their model pipelines, ETLs, or workflows.

ML 52