Data Quality DevOps Software Development 
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.
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.
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Unite.AI
NOVEMBER 21, 2024
Archana Joshi brings over 24 years of experience in the IT services industry, with expertise in AI (including generative AI), Agile and DevOps methodologies, and green software initiatives. As the software development landscape evolves, we are leveraging GenAI to automate those repetitive tasks that can bog teams down.
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.
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.
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.
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.
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