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Facebook Marketplace Machine Learning System Design Review
# Facebook
# Vector Search
# Embedding

Facebook Marketplace Machine Learning System Design Review

How Aurora Accelerates Autonomous Vehicle ML Model Development Using Kubeflow

How Aurora Accelerates Autonomous Vehicle ML Model Development Using Kubeflow

Ankit Aggarwal, Vinay Anantharaman & Maurizio Vitale

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All (223)
Video (223)
Most Recent
Murtuza Shergadwala
Murtuza Shergadwala · Jan 17th, 2023

Approaches to Fairness and XAI

The field of Explainable Artificial Intelligence (XAI) is continuously evolving, with an increasing focus on providing model-centric explanations in a human-centric manner. However, better frameworks and training for users are needed to fully utilize the potential of XAI tools. Additionally, there is a discrepancy in the approach to fairness in XAI, with the industry approaching it from a regulatory standpoint, while academia is engaging in more discussion and research on the topic.
# Explainable Artificial Intelligence
# Fiddler AI
# Decision-making Behaviors
Approaches to Fairness and XAI
Stephen Bailey
Stephen Bailey · Jan 17th, 2023

Airflow Sucks for MLOps

Stephen discusses his experience working with data platforms, particularly the challenges of training and sharing knowledge among different stakeholders. This talk highlights the importance of having clear priorities and a sense of practicality and mentions the use of modular job design and data classification to make it easier for end users to understand which data to use. Stephen also mentions the importance of being able to move quickly and not getting bogged down in the quest for perfection. We recommend Stephen's blog post "Airflow's Problem" for further reading.
Airflow Sucks for MLOps
Sakib Dadi
Sakib Dadi · Jan 10th, 2023

The Evolution of ML Infrastructure

The toolkit and infrastructure empowering machine learning practitioners are advancing as ML adoption accelerates. We'll go through the current landscape of ML tooling, startups, and new projects from an investor's perspective.
# ML Infrastructure
# ML Adoption
# Landscape of ML
# ML Investing
The Evolution of ML Infrastructure
Tochukwu Nwoke
Stephen Oladele
Tochukwu Nwoke & Stephen Oladele · Jan 7th, 2023

First MLOps Community in Africa

The First MLOps Community in Africa - MLOps Lagos Community Meetup features a discussion with experts about their experiences with MLOps at their companies. The MLOps Community was introduced with its goals and presents on scaling machine learning innovation with MLOps, XAI, and A/B testing. The meetup concludes with a panel featuring practitioners and students sharing their experiences with MLOps.
# ML Innovation
# XAI Testing
# A/B Testing
First MLOps Community in Africa
Steven Fines
Steven Fines · Jan 5th, 2023

The Motivation for MLOps

As an emerging sub-discipline, MLOps still needs to prove itself inside many enterprises where ML may present significant opportunities. Focused on the needs of the ML and Enterprise Architect this talk discusses some of the key areas which motivate the adoption of an MLOps approach to handling ML solution development and operations.
# MLOps Motivation
# ML Architect
# Enterprise Architect
The Motivation for MLOps
Rafael d'Arce
Rafael d'Arce · Dec 29th, 2022

Version-controlled ML Pipelines with Pachyderm

Pachyderm provides Kubernetes-native pipeline orchestration with data versioning and lineage, which can greatly improve the reproducibility and auditability of your ML experiments. Pachyderm helps with experimentation by allowing data scientists to track and compare results from different experiments.
# Pachyderm
# Kubernetes
# ML Pipelines
Version-controlled ML Pipelines with Pachyderm
Sascha Heyer
Sascha Heyer · Dec 29th, 2022

Vertex AI Workshop

Machine learning in the Cloud can be easy and flexible. Join us for a 1.5 hours hands-on workshop around Google Vertex AI: 1. Train models with Vertex AI Training 2. Serve models with Vertex AI Endpoints 3. Putting it together by using Vertex AI Pipelines (including Vertex AI Model Registry and Vertex AI Experiments)
# Vertex AI
# Google Cloud
# MLOps Workshop
Vertex AI Workshop
Foundation models are rightfully being compared to other game-changing industrial advances like steam engines or electric motors. They’re core to the transition of AI from a bespoke, less predictable science to an industrialized, democratized practice. Before they can achieve this impact, however, we need to bridge the cost, quality, and control gaps. Snorkel Flow Foundation Model Suite is the fastest way for AI/ML teams to put foundation models to use. For some projects, this means fine-tuning a foundation model for production dramatically faster by creating programmatically labeling training data. For others, the optimal solution will be using Snorkel Flow’s distill, combine, and correct approach to extract the most relevant knowledge from foundation models and encode that value into the right-sized models for your use case. AI/ML teams can determine which Foundation Model Suite capabilities to use (and in what combination) to optimize for cost, quality, and control using Snorkel Flow’s integrated workflow for programmatic labeling, model training, and rapid-guided iteration.
# Foundational Models
# Snorkel AI
# Foundation Model Suite
Foundational Models are the Future but... with Alex Ratner CEO of Snorkel AI
Dattaraj Rao
Dattaraj Rao · Dec 22nd, 2022

Explainability in the MLOps Cycle

When it comes to Dattaraj's interest, you'll hear about his top 3 areas in Machine Learning. What he sees as up and coming, what he's investing his company's time into and where he invests his own time. Learn more about rule-based systems, deploying rule-based systems , and how to incorporate systems into more systems. there is no difference between ML systems and deploying models. It's just that this machine learning model is much smarter than traditional rule based models.
# MLOps Cycle
# Rule-bases Systems
# Deployment
Explainability in the MLOps Cycle
Stephanie Kirmer
 Nikhil Patel
Stephanie Kirmer & Nikhil Patel · Dec 22nd, 2022

Leveraging Open Source Tools in ML

Setting Healthy Boundaries: Generating Geofences at Scale with Machine Learning Stephanie describes how project44 developed a new technique for generating geofences for customer facilities using historical data combined with machine learning. The audience learns how Stephanie and her team did it, including the pitfalls of deploying a product like this to production. She makes a special point to discuss the varied open source tools the team used including DBSCAN, Dask, geopandas, folium, Airflow, and more. Cars-Forge: Spot EC2s Made Easy Nikhil showcases cars-forge, a tool developed at that was recently open sourced. Cars-forge is a command line tool that starts EC2 instances and runs scripts on them. Additionally, Nikhil also previews Skelebot, a command-line tool for developing ML projects and executing them in docker.
# Geofences at Scale
# Spot EC2
# project44
# Toast Inc
Leveraging Open Source Tools in ML
MLflow Pipelines: Opinionated ML Pipelines in MLflow
Xiangrui Meng
Monzo Machine Learning Case Study
Neal Lathia
More than a Cache: Turning Redis into a Composable, ML Data Platform
Samuel Partee
Platform Thinking: A Lemonade Case Study
Orr Shilon, Demetrios Brinkmann & Vishnu Rachakonda