Published Jul 20, 2020

MLOps Meetup #26 // How to Leverage ML Tooling Ecosystem // Mariya Davydova - Head of Product at Neu.ro

Mariya Davydova, Head of Product at Neu.ro, delves into optimizing machine learning workflows through efficient model management, data strategies, and leveraging the ML tooling ecosystem. She offers valuable insights on integrating tools like Docker, Airflow, and Selden to ensure model reliability and streamline operations in ML projects.

Episode Highlights

Topics covered

Efficient Model Management
01
Data Management Strategies
02
ML Tooling Ecosystem
03

MLOps Meetup #26 // How to Leverage ML Tooling Ecosystem // Mariya Davydova - Head of Product at Neu.ro

Topics covered

Popular Clips

Managing Dependencies

Building ML Pipeline

ML Tools Integration

ML Tooling Ecosystem

Internet Struggles

Model Monitoring Insights

Challenges in Production

Streamlining ML Development

Production Tools Overview

Data Versioning Challenges

Challenges in MLOps

Efficient Model Management

Streamlined MLOps Platform

Model Clustering

Episode Highlights

Efficient Model Management

Data Management Strategies

ML Tooling Ecosystem

MLOps Meetup #26 // How to Leverage ML Tooling Ecosystem // Mariya Davydova - Head of Product at Neu.ro

Topics covered

Popular Clips

Episode Highlights

Efficient Model ManagementMariya Davydova explores model versioning and deployment strategies, emphasizing the importance of efficient management and monitoring tools. She shares insights on using Docker, Airflow, and Selden to streamline ML workflows and ensure model reliability.

Efficient Model Management

Data Management StrategiesMariya Davydova explores the intricacies of data versioning and storage abstractions in machine learning projects. She shares insights on overcoming challenges in managing data versions and optimizing workflows through efficient storage solutions.

Data Management Strategies

ML Tooling EcosystemMariya Davydova, Head of Product at Neu.ro, discusses leveraging the ML tooling ecosystem to optimize workflows. She highlights the integration of various tools to enhance efficiency and address challenges in machine learning operations.

ML Tooling Ecosystem