Published Jun 3, 2020

Jordan Edwards: ML Engineering and DevOps on AzureML

Principal Program Manager Jordan Edwards delves into the intricacies of ML engineering and DevOps on AzureML, offering insights on enterprise governance, the latest advancements in ML tools, and how innovations in model management and collaboration are streamlining AI development.

Episode Highlights

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Episode Highlights

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Dexa/Machine Learning Street Talk (MLST)

Jordan Edwards: ML Engineering and DevOps on AzureML

Topics covered

Popular Clips

Machine Learning Pipelines

ML DevOps Insights

Model Deployment Strategies

ML Ops Maturity

Enterprise Model Governance

Model Ensembling Techniques

ML Data Flows

ML Inference Schema

Testing Deep Learning Models

Evolution of MLOps

Time Travel Advice

Evolution of ML DevOps

Machine Learning Innovations

Machine Learning Challenges

Episode Highlights

Model Governance

ML Innovations

ML DevOps

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Jordan Edwards: ML Engineering and DevOps on AzureML

Topics covered

Popular Clips

Episode Highlights

Model GovernanceJordan Edwards explores enterprise governance in machine learning, focusing on strategies for managing models across large organizations. He discusses the importance of unified data catalogs and the challenges of deep learning, emphasizing the need for specialized algorithms.

Model Governance

ML InnovationsRecent advancements in machine learning tools are enhancing privacy and fairness, with Microsoft's Fairlearn and Seal leading the charge. These tools, along with computational advancements like data augmentation and GPU utilization, are reshaping the landscape of AI development.

ML Innovations

ML DevOpsJordan Edwards, Principal Program Manager for AzureML, shares insights on transforming ML DevOps into a streamlined process for software engineering. He discusses the challenges and innovations in managing machine learning deployments, emphasizing the need for governance and collaboration.

ML DevOps

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