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Building Threat Detection Systems: An MLE's Perspective

Building Threat Detection Systems: An MLE's Perspective

Jeremy Thomas Jordan
Jeremy Thomas Jordan
The (Mostly) Art and Science of Designing an ML Feature

The (Mostly) Art and Science of Designing an ML Feature

Siwei Kang

Popular topics
# Interview
# Presentation
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# Monitoring
# Scaling
# Panel
# Cultural Side
# Deployment
# ML Orchestration
# Feature Stores
# Data Science
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# Leverage
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# ML Workflow
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All (204)
Video (204)
Most Recent
Marouen Hizaoui
Mo Basirati
Marouen Hizaoui & Mo Basirati · Nov 24th, 2022

MLOps in Practice: Common Challenges and Lessons Learned

The different practices and approaches that would ensure the success of your data products, distilled from doing MLOps at different clients from different industries and different levels of maturity: - It's much more than technical stuff - The "best" tool is not always the best solution - Integrating MLOps in system and infrastructure with different levels of maturity
# Machine Learning Reply
# MLOps Integration
# Levels of Maturity
MLOps in Practice: Common Challenges and Lessons Learned
Stefano Bosisio
Stefano Bosisio · Nov 24th, 2022

Applying ML in Trustpilot: Lessons Learned when MLOps Wasn't Even a Thing

Do you want to start tackling Machine Learning Operations (MLOps) and know more about it? Or do you want to find more data engineering and MLOps solutions for your business needs? In this talk we will be dealing with MLOps principles and practices, lessons learned and the architectural solutions implemented in Trustpilot. In the last 3 years Machine Learning (ML) engineers, data scientists, and data engineers have joint efforts to bring Trustpilot to have a more robust and consistent use of artificial intelligence (AI) under MLOps principles. From identifying and defining MLOps gaps within the data science journey and listening to business needs, this joint collaboration effort resulted in the creation of the MLOps infrastructure within the Google Cloud Platform (GCP). As a result, the entire ML journey has been drastically improved, easing models prototyping, data retrieval processes and lowering models development and deployment time from months to weeks.
# Trustpilot
# Google Cloud Platform
# Model Deployment
Applying ML in Trustpilot: Lessons Learned when MLOps Wasn't Even a Thing
Aparna Dhinakaran
Jason Lopatecki
Aparna Dhinakaran & Jason Lopatecki · Nov 22nd, 2022

Monitoring Unstructured Data

Monitoring embeddings on unstructured data is not an easy feat let's be honest. Most of us know what it is but don't understand it one hundred percent. Thanks to Aparna and Jason of Arize for breaking down embedding so clearly. At the end of this Lightning talk, we get to see a demo of how Arize deals with unstructured data and how you can use Arize to combat that.
# Unstructured Data
# Embedding
# Arize
Monitoring Unstructured Data
Chip Huyen
Chip Huyen · Nov 22nd, 2022

Real-time Machine Learning with Chip Huyen

We talk about forcing functions and how you can supercharge learning by putting yourself into a situation where you have a responsibility to others to learn.
# Real-time Machine Learning
# Accountability
# MLOps Practice
Real-time Machine Learning with Chip Huyen
Peeyush Agarwal
Peeyush Agarwal · Nov 22nd, 2022

Scaling Real-time Machine Learning at Chime

In this Lighting Talk, Peeyush Agarwal explains 2 key pieces of the ML infrastructure at Chime. Peeyush goes into detail about the current feature store design and feature monitoring process along with the ML monitoring setup. This Lighting Talk is brought to you by reach out to them for all of your ML monitoring needs.
# Lighting Talk
# ML Infrastructure
# Chime
Scaling Real-time Machine Learning at Chime
Adam Sroka
Adam Sroka · Nov 17th, 2022

So, What is MLOps Anyway?

Adam gives an overview of MLOps and: - why it's important - where it's going - where it's come from - what the major challenges are - how maturity models can be dangerous
So, What is MLOps Anyway?
Can AI be truly creative? Can model ML models augment human creativity? How do we leverage ML for creating works of Art, Music, and even Jokes? This presentation answers these questions and more by carefully looking at various open-source ML Models and understanding their strengths, drawbacks, and societal implications. It also includes live demos with code walkthroughs of creative projects using open-source MusicVAE, GAN Art, Neural StyleTransfer, SketchRNN, StableDiffusion, Open AI Whisper, and transformer-based models for joke generation.
# Creative AI
# Leverage
# Open Source
Creative AI - Using ML to Create Art, Music, and Jokes
Catarina Silva
Luis Silva
Jorge Pessoa
Pedro Coelho
Catarina Silva, Luis Silva, Jorge Pessoa & Pedro Coelho · Nov 10th, 2022

MLOps Roundtable

The Lisboa MLOps Community fires up another Meetup in Lisbon on the 27th of October. After taking inspiration from prior roundtables from Deep Learning Sessions Portugal, they discussed multiple subjects within MLOps in a roundtable fashion, along with their primary focus: ML Platform thinking.
# Functional-oriented teams vs Market-oriented teams
# Data governance
# Agile
MLOps Roundtable
Ian Schweer
Ian Schweer · Nov 10th, 2022

What is Data / ML Like on League?

If you're not an avid gamer yourself, you have never really seen how ML might be used in the gaming space. It's so interesting to see the things that are different like full stories of players' games from start to finish.
# Video Game
# Data Space
# Riot
# Analysis vs. Production Code Quality
What is Data / ML Like on League?
Ethan Rosenthal
Ethan Rosenthal · Nov 8th, 2022

Let's Continue Bundling into the Database

The relationship between ML Engineers and Product Managers is something that we don't talk about enough. We've got to get this right. If we don't get this right, either you're not focusing on the business problems in the right way or the Product Managers are not going to understand the tech appropriately to help make the right decisions.
# Large Language Models
# Database Bundling
# Feature Stores
# Trade-offs
Let's Continue Bundling into the Database
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