Name: Online: ML Design Patterns and Designing ML Infrastructure
Start: 2021-02-24T18:00:00-05:00
End: 2021-02-24T20:00:00-05:00

Designing, building, deploying, and scaling ML systems can be challenging. By utilizing design patterns, engineers can leverage the best practices that have been proven to be successful. Join us in February to learn about several ML design patterns and their use in production systems.

Agenda
-------------------------------------------------
6:00 PM -- Greetings

6:05 PM -- ML Design Patterns and Designing ML Infrastructure - Lak Lakshmanan

7:30 PM -- Closings

Location
-------------------------------------------------
Zoom and YouTube Streaming
A link will be sent out prior to the event. Please note that Zoom is capped at 100, so if you do not get into Zoom, you will be able to watch via YouTube.

Talks
-------------------------------------------------
ML Design Patterns and Designing ML Infrastructure
Design patterns are formalized best practices to solve common problems when designing a software system. As machine learning moves from being a research discipline to a software one, it is useful to catalog tried-and-proven methods to help engineers tackle frequently occurring problems that crop up during the ML process. In this talk, I will cover five patterns (Workflow Pipelines, Transform, Multimodal Input, Feature Store, Cascade) that are useful in the context of adding flexibility, resilience and reproducibility to ML in production. For data scientists and ML engineers, these patterns provide a way to apply hard-won knowledge from hundreds of ML experts to your own projects.

Anyone designing infrastructure for machine learning will have to be able to provide easy ways for the data engineers, data scientists, and ML engineers to implement these, and other, design patterns.

Speakers
-------------------------------------------------
Lak is the Director for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud's data analytics and machine learning products. He founded Google's Advanced Solutions Lab ML Immersion program and is the author of three O'Reilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA. Lak can be reached on Twitter at @lak_gcp

Resources
-------------------------------------------------
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps 1st Edition
https://www.amazon.com/Machine-Learning-Design-Patterns-Preparation/dp/1098115783

Jason Barbour

Data Works MD

Technology

Web Development

Computer Programming

Data Science

Data Mining

Predictive Analytics

Data Visualization

Cloud Computing

Software Development

Hadoop

Big Data

Machine Learning

Data Analytics

Amazon Web Services

Software Architecture

New Technology

Online:  ML Design Patterns and Designing ML Infrastructure

Online event

Share

Data Works MD

Online: ML Design Patterns and Designing ML Infrastructure

Data Works MD

Details

Members are also interested in