Big data engineer
Dataconomy
MAY 26, 2025
Designing big data architecture They create big data architectures tailored to the organization, selecting suitable technologies to build and maintain scalable data processing systems.
Computer Science Data Lakes Data Modeling 
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Using Azure ML to Train a Serengeti Data Model, Fast Option Pricing with DL, and How To Connect a…
ODSC - Open Data Science
MARCH 30, 2023
Using Azure ML to Train a Serengeti Data Model, Fast Option Pricing with DL, and How To Connect a GPU to a Container Using Azure ML to Train a Serengeti Data Model for Animal Identification In this article, we will cover how you can train a model using Notebooks in Azure Machine Learning Studio.
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How Carrier predicts HVAC faults using AWS Glue and Amazon SageMaker
AWS Machine Learning Blog
SEPTEMBER 5, 2023
The portal combines these predictive alerts with other insights we derive from our AWS-based data lake in order to give our dealers more clarity into equipment health across their entire client base. Yingwei received his PhD in computer science from Texas A&M University. candidate in computer science at UNC-Charlotte.
Data science vs data analytics: Unpacking the differences
IBM Journey to AI blog
SEPTEMBER 19, 2023
Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. js and Tableau Data science, data analytics and IBM Practicing data science isn’t without its challenges.
How to use foundation models and trusted governance to manage AI workflow risk
IBM Journey to AI blog
OCTOBER 16, 2023
It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Open-source projects, academic institutions, startups and legacy tech companies all contributed to the development of foundation models. It can be used with both on-premise and multi-cloud environments.
MLOps and DevOps: Why Data Makes It Different
O'Reilly Media
OCTOBER 19, 2021
We need robust versioning for data, models, code, and preferably even the internal state of applications—think Git on steroids to answer inevitable questions: What changed? ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses.
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