This episode discusses the growing need for machine learning explainability across various business verticals. Then it introduces Amazon SageMaker Debugger, a service that provides visibility into the ML model training process for real-time and offline analysis. https://aws.amazon.com/blogs/aws/amazon-sagemaker-debugger-debug-your-machine-learning-models/
Apr 2026
#754: Accelerating healthcare decisions with agents
In this episode of the AWS Podcast, you'll hear how Cohere Health®, uses Amazon Bedrock AgentCore to optimize the accuracy and efficiency of health plan medical necessity reviews. Cohere Review Resolve™ analyzes both structured and unstructured data–such as clinical records, pati ... Show More
35m 55s
Dec 2017
Mercedes Benz Machine Learning Research
<p class="p1"><span class="s1">This episode features an interview with Rigel Smiroldo recorded at NIPS 2017 in Long Beach California.<span class="Apple-converted-space"> </span> We discuss data privacy, machine learning use cases, model deployment, and end-to-end machine learning ... Show More
27m 5s
Jan 2024
Careers, Skills, and the Evolution of AI (Ep. 248)
<p>!!WARNING!!</p>
<p>Due to some technical issues the volume is not always constant during the show. I sincerely apologise for any inconvenience
Francesco </p>
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<p>In this episode, I speak with Richie Cotton, Data Evangelist at DataCamp, as he delves into the d ... Show More
32m 27s
Dec 2023
706: AI and ML - The Pieces Explained
In this episode of Syntax, Wes and Scott talk about understanding the integration of different components in AI models, the choice between traditional models and Language Learning Models (LLM), the relevance of the Hugging Face library, demystify Llama, discuss spaces in AI, and ... Show More
33m 1s
