YoVDO

One Does Not Simply Put Machine Learning into Production

Offered By: GOTO Conferences via YouTube

Tags

GOTO Conferences Courses Data Science Courses Machine Learning Courses Reinforcement Learning Courses Interpretability Courses Kubeflow Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore the challenges and considerations of implementing machine learning in production environments in this insightful conference talk from GOTO Copenhagen 2017. Delve into the important dimensions of accuracy, cost, maintainability, and interpretability, and understand the trade-offs between them. Learn about the technical challenges that arise when infusing existing products with machine learning capabilities or building ML-first products. Gain valuable insights from Henrik Brink, author of "Real-World Machine Learning," as he shares his expertise on successfully integrating machine learning into production systems. Discover practical strategies and best practices for overcoming obstacles and maximizing the potential of machine learning in real-world applications.

Syllabus

One does not simply put Machine Learning into Production • Henrik Brink • GOTO 2017


Taught by

GOTO Conferences

Related Courses

AWS Engenheiro de ML AWS Associate 2.1: Como definir uma estratégia de modelagem (Português) | AWS ML Engineer Associate 2.1 Choose a Modeling Approach (Portuguese)
Amazon Web Services via AWS Skill Builder
AWS ML Engineer Associate 2.1 Choose a Modeling Approach (Korean)
Amazon Web Services via AWS Skill Builder
AWS ML Engineer Associate 2.1 Choose a Modeling Approach (Simplified Chinese)
Amazon Web Services via AWS Skill Builder
Responsible AI for Developers: Interpretability & Transparency - Polski
Google Cloud via Coursera
Responsible AI for Developers: Interpretability & Transparency - 日本語版
Google Cloud via Coursera