Published Nov 27, 2019

ML Ops

Dive into the world of ML Ops with Kyle Polich and Damian Brady as they unravel the complexities of managing machine learning models in production, address data drift challenges, and draw insightful parallels between the evolution of ML Ops and software engineering practices, while tackling core data engineering hurdles for efficient model deployment.

Episode Highlights

Topics covered

Episode Highlights

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Dexa/Kyle Polich

ML Ops

Topics covered

Popular Clips

Future MLOps Vision

Machine Learning Challenges

Model Deployment Challenges

Online Presence

Data Model Retraining

Data Drift Techniques

Model Deployment Challenges

Model Change Management

Data Engineering Challenges

Evolution of ML Ops

Model Deployment Options

Data Tooling Trends

Jupyter Notebook Source Control

Model Training Insights

Episode Highlights

Model Management

ML Ops Evolution

Data Engineering

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ML Ops

Topics covered

Popular Clips

Episode Highlights

Model ManagementThe conversation with Damian Brady explores the intricacies of managing machine learning models in production, focusing on change management and data drift techniques. These strategies are essential for ensuring models remain effective and provide value over time.

Model Management

ML Ops EvolutionIn this episode of the Data Skeptic, Kyle Polich and Damian Brady explore the evolution and future of machine learning operations, drawing parallels to the development of software engineering practices.

ML Ops Evolution

Data EngineeringThe discussion with Damian Brady explores the engineering challenges and model training insights in machine learning operations. He addresses data immutability, source control issues, and the complexities of deploying models efficiently.

Data Engineering

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