Unite.AI

OCTOBER 30, 2023

As emerging DevOps trends redefine software development, companies leverage advanced capabilities to speed up their AI adoption. That’s why, you need to embrace the dynamic duo of AI and DevOps to stay competitive and stay relevant. How does DevOps expedite AI? Poor data can distort AI responses.

DevOps 308

DevOps Software Development Automation Artificial Intelligence 308

Unite.AI

SEPTEMBER 30, 2024

The result will be greater innovation and new benchmarks for speed and quality in software development. AI-powered QA is also becoming central to DevOps. Processes will become more efficient, and collaboration between development and QA teams will improve.

Automation 315

Automation AI AI DevOps 315

Towards AI

AUGUST 16, 2023

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps.

Machine Learning 98

Machine Learning Software Development Data Drift Data Science 98

AWS Machine Learning Blog

JULY 8, 2024

The Data Quality Check part of the pipeline creates baseline statistics for the monitoring task in the inference pipeline. Within this pipeline, SageMaker on-demand Data Quality Monitor steps are incorporated to detect any drift when compared to the input data.

Data Scientist 110

Data Scientist ML Engineer Machine Learning Data Science 110

AWS Machine Learning Blog

FEBRUARY 21, 2023

With this option, you are testing the new model and minimizing the risks of a low-performing model, and you can compare both models’ performance with the same data. SageMaker deployment guardrails Guardrails are an essential part of software development. She is also the Co-Director of Women In Big Data (WiBD), Denver chapter.

ML 81

ML Software Development Automation Metadata 81

Marktechpost

JULY 17, 2023

The advantages of using synthetic data include easing restrictions when using private or controlled data, adjusting the data requirements to specific circumstances that cannot be met with accurate data, and producing datasets for DevOps teams to use for software testing and quality assurance.

Machine Learning 52

Machine Learning Data Scientist Computer Vision Deep Learning 52

The MLOps Blog

APRIL 17, 2023

Then you must specify, analyze, test, and manage them throughout the software development lifecycle. Creating user stories , analyzing them, and validating requirements are all parts of requirements development that deserve their own article. Varying workflows so users can decide what they want to track.

Metadata 52

Metadata Data Scientist Explainability ML 52