AI @Scale

Virtual 9:00am - 11:00am

Event is Full
Read More Read Less

@Scale brings thousands of engineers together throughout the year to discuss complex engineering challenges and to work on the development of new solutions. We're committed to providing a safe and welcoming environment — one that encourages collaboration and sparks innovation.

Every @Scale event participant has the right to enjoy his or her experience without fear of harassment, discrimination, or condescension. The @Scale code of conduct outlines the behavior that we support and don't support at @Scale events and conferences. We expect participants to follow these rules at all @Scale event venues, online communities, and event-related social activities. These guidelines will keep the @Scale community a safe and enjoyable one for everyone.

Be welcoming. Everyone is welcome at @Scale events, inclusive of (but not limited to) gender, gender identity or expression, sexual orientation, body size, differing abilities, ethnicity, national origin, language, religion, political beliefs, socioeconomic status, age, color and neurodiversity. We have a zero-tolerance policy for discrimination.

Choose your words carefully. Treat one another with respect and in a professional manner. We're here to collaborate. Conflict is not part of the equation.

Know where the line is, and don't cross it. Harassment, threats, or intimidation of any kind will not be tolerated. This includes verbal, physical, sexual (such as sexualized imagery on clothing, presentations, in print, or onscreen), written, or any other form of aggression (whether outright, subtle, or micro). Behavior that is offensive, as determined by @Scale organizers, security staff, or conference management, will not be tolerated. Participants who are asked to stop a behavior or an action are expected to comply immediately or will be asked to leave.

Don't be afraid to call out bad behavior. If you're the target of harmful or offensive behavior, or if you witness someone else being harassed, threatened, or intimidated, don't look away. Tell an @Scale staff member, a security staff member, or a conference organizer immediately. Please notify our event staff, security staff, or conference organizers of any harmful or offensive behavior that you've experienced or witnessed in any form, whether in person or online.

We at @Scale want our events to be safe for everyone, and we have a zero-tolerance policy for violations of our code of conduct. @Scale conference organizers will investigate any allegation of problematic behavior, and we will respond accordingly. We reserve the right to take any follow-up actions we determine are needed. These include being warned, being refused admittance, being ejected from the conference with no refund, and being banned from future @Scale events.

Event is Full
Agenda
9:00am

Azure Cognitive Services @Scale

Azure Cognitive Services sits at the core of many essential products and services at Microsoft for internal and external workloads. Anand’s talk describes the hardware and software infrastructure that supports Ai services at global scale. Azure Cognitive Services workloads are extremely diverse: services require many different types of models in practice. This diversity has implications at all layers in the system stack. In addition, the computational requirements are also intense, leveraging both GPUs and CPUs for real-time inference. Addressing these and other emerging challenges continues to require diverse efforts that span algorithms, software, and hardware design. In this talk, Anand also walks through some of the challenges, including data privacy, deep customization, and bias correction, and discusses solutions they have built to tackle these challenges.
9:20am

High Performance Observability Across the ML Lifecycle

The scale and breadth of ML applications have increased dramatically thanks to scalable model-training and serving technologies. Builders of enterprise ML systems often have to contend with both real-time inference and massive amounts of data, prompting increasing investment in tools for MLOps and ML Observability. Data logging is a critical component of a robust ML pipeline, as it provides essential insights into the system’s health and performance. However, performant logging and monitoring for ML systems has proven ineffective within existing DevOps and data sampling approaches. Alessya will discuss the WhyLabs solution to this problem: using statistical fingerprinting and data profiling to scale to TB-sized data with an open-source data logging library, whylogs. She will present the WhyLabs Observability platform that runs on top of whylogs, providing out-of-the-box monitoring and anomaly detection to proactively address data-related failures across the entire ML lifecycle.
9:35am

F3: Next-generation Feature Framework at Facebook

We will discuss the next generation feature framework in development at Facebook. This new framework enables efficient experimentation in building machine learning features to semantically model behaviors and intent of users, and leverages compiler technology to unify batch and streaming processing of these features in an expressive language. It also automatically optimizes underlying data pipelines and applies privacy enforcement at scale.
9:50am

Live Panel / Audience Q&A Featuring Monday's Speakers

10:40am

Live Panel / Audience Q&A: Women in Engineering

9:00am

Netflix's Human-Centric Approach to ML Infrastructure

Netflix's unique culture affords it's data scientists extraordinary freedom of choice in ML tools and libraries. At the same time, they are responsible for building, deploying, and operating complex ML workflows autonomously without the need to be significantly experienced with systems or data engineering. Metaflow, our ML framework (now open-source at metaflow.org), provides them with delightful abstractions to manage their project's lifecycle end-to-end, leveraging the strengths of the cloud: elastic compute and high-throughput storage. In this talk, we present our human-centric design principles that enable the autonomy our users enjoy.
9:20am

Large Scale Machine Learning Using SQL in BigQuery

Google BigQuery is a petabyte-scale serverless cloud data warehouse that enables scalable machine learning using SQL. In this talk, we take a look at how enabling data analysts and other SQL users to perform machine learning tasks can accelerate business decision-making and intelligence. We also present challenges in democratizing ML in large scale data warehouses such as BigQuery. We describe how a combination of general purpose SQL query engine and dedicated machine learning infrastructure can create a robust infrastructure for performing machine learning tasks.
9:35am

Mastercook: Large scale concurrent model development in ads ranking

We will discuss a novel model development process and tools we introduced to ads ranking machine learning teams, where a single model can be concurrently developed by dozens of engineers, whose changes to the model are centralized collected, combined, tested and launched.
 

Large Scale Machine Learning Using SQL in BigQuery

Google BigQuery is a petabyte-scale serverless cloud data warehouse that enables scalable machine learning using SQL. In this talk, we take a look at how enabling data analysts and other SQL users to perform machine learning tasks can accelerate business decision-making and intelligence. We also present challenges in democratizing ML in large scale data warehouses such as BigQuery. We describe how a combination of general purpose SQL query engine and dedicated machine learning infrastructure can create a robust infrastructure for performing machine learning tasks.
9:50am

Flyte: Making MLOps and DataOps a reality

Flyte is the backbone for large-scale Machine Learning and Data Processing (ETL) pipelines at Lyft. It is used across business critical applications ranging from ETA, Pricing, Mapping, Autonomous etc. At its core it is a Kubernetes native workflow engine that executes 1M+ pipelines and 40M+ containers per month. Flyte abstracts complex infrastructure management from its users and provides a declarative fabric to connect disparate compute technologies. This increases productivity and thus product velocity by enabling them to focus on business logic. Flyte has made it possible to build higher-level platforms at Lyft, further reducing the barriers to entry for non-infrastructure engineers. The talk will focus on: Motivation and tenets for building Flyte, and parts of the Data Stack tackled by it. Architecture of Flyte and its specification language to orchestrate compute and manage data flow across disparate systems like Spark, Flink, Tensorflow, Hive etc. Use-cases where Flyte can be leveraged Extensibility of the Flyte and the burgeoning ecosystem.
10:05am

Live Panel / Audience Q&A Featuring Tuesday's Speakers

Join the @Scale Mailing List and Get the Latest News & Event Info

Code of Conduct

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy