ml

Accelerate innovation in the enterprise with distributed ML and DL (sponsored by BlueData)

Accelerate innovation in the enterprise with distributed ML and DL (sponsored by BlueData)

January 4, 2020

Nanda Vijaydev shares practical examples ofand lessons learned fromML/DL use cases in financial services, healthcare, and other industries. You'll learn how to quickly deploy containerized multinode environments for TensorFlow and other ML/DL tools in a multitenant architecture either on-premises, in the cloud, or in a hybrid environment.
Automated ML: A journey from CRISPR.ML to Azure ML (sponsored by Microsoft Azure)

Automated ML: A journey from CRISPR.ML to Azure ML (sponsored by Microsoft Azure)

January 3, 2020

Automated ML is at the forefront of Microsofts push to make Azure ML an end-to-end solution for anyone who wants to build and train models that make predictions from data and then deploy them anywhere. Join Danielle Dean for a surprising conversation about a data scientists dilemma, a researchers ingenuity, and how cloud, data, and AI came together to help build automated ML.
Bringing your machine learning to production with ML Ops (sponsored by Microsoft)

Bringing your machine learning to production with ML Ops (sponsored by Microsoft)

January 2, 2020

Sarah Bird offers an overview of ML Ops (DevOps for machine learning), sharing solutions and best practices for an end-to-end pipeline for data preparation, model training, and model deployment while maintaining a comprehensive audit trail. Join in to learn how to build a cohesive and friction-free ecosystem for data scientists and app developers to collaborate together and maximize impact.
Build, train, and deploy ML with Kubeflow: Using AI to label GitHub issues

Build, train, and deploy ML with Kubeflow: Using AI to label GitHub issues

January 2, 2020

Turning ML into magical products often requires complex distributed systems that bring with them a unique ML-specific set of infrastructure problems. Using AI to label GitHub issues as an example, Jeremy Lewi and Hamel Husain demonstrate how to use Kubeflow and Kubernetes to build and deploy ML products.
Building a production-scale ML platform

Building a production-scale ML platform

January 2, 2020

Yu Dong offers an overview of the why, what, and how of building a production-scale ML platform based on ongoing ML research trends and industry adoptions.
Executive Briefing: Overview of data governance

Executive Briefing: Overview of data governance

December 31, 2019

Effective data governance is foundational for AI adoption in enterprise, but it's an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more. Join in to learn what impact machine learning has on data governance and vice versa.
Fraud detection without feature engineering

Fraud detection without feature engineering

December 31, 2019

Pamela Vagata explains how Stripe has applied deep learning techniques to predict fraud from raw behavioral data. Join in to learn how the deep learning model outperforms a feature-engineered model both on predictive performance and in the effort spent on data engineering, model construction, tuning, and maintenance.
Is AI human-ready?

Is AI human-ready?

December 30, 2019

Aleksander Madry discusses major roadblocks that prevent current AI frameworks from having a broad impact and outlines approaches to addressing these issues and making AI frameworks truly human-ready.
One-click deployment for containerized ML and DL environments

One-click deployment for containerized ML and DL environments

December 29, 2019

Nanda Vijaydev explains how to spin up instant ML/DL environments using containersall while ensuring enterprise-grade security and performance. Find out how to provide your data science teams with on-demand access to the tools and data they need, whether on-premises or in the cloud.
Open source tools for machine learning model and dataset versioning

Open source tools for machine learning model and dataset versioning

December 29, 2019

ML model and dataset versioning is an essential first step in the direction of establishing a good process. Dmitry Petrov and Ivan Shcheklein explore open source tools for ML models and datasets versioning, from traditional Git to tools like Git-LFS and Git-annex and the ML project-specific tool Data Version Control or DVC.org.
Operationalizing real-time ML and DL with GigaSpaces, Intel Analytics Zoo, and Optane DC Persistent Memory

Operationalizing real-time ML and DL with GigaSpaces, Intel Analytics Zoo, and Optane DC Persistent Memory

December 29, 2019

Yoav Einav and Vin Costello explain how to achieve faster analytical processing, leveraging in-memory performance for the cost of flash with persistent memory (~300% faster than SSD); smarter insights at optimized TCO, scaling the speed layer capacity for smarter real-time analytics with 7x lower footprint); and Agile automation of your ML and DL model CI/CD pipeline, for faster time to market.
Unifying Twitter around a single ML platform

Unifying Twitter around a single ML platform

December 28, 2019

Twitter is a large company with many ML use cases. Historically, there have been many ways to productionize ML at Twitter. Yi Zhuang and Nicholas Leonard describe the setup and benefits of a unified ML platform for production and explain how the Twitter Cortex team brings together users of various ML tools.
Using machine learning to automate car damage assessment and document workflows

Using machine learning to automate car damage assessment and document workflows

December 27, 2019

Vladimir Starostenkov and Siarhei Sukhadolski discuss two ML solutions from Altoros: one was developed to facilitate the process of assessing car damage right at the accident scene, while the second helps to automate recognition, extraction, and analysis. Join in to see how to integrate both solutions into the existing workflows of insurance, car rental, and maintenance services.
Architecting Enterprise Platforms for CI/CD, Cloud and SRE with AI and Analytics

Architecting Enterprise Platforms for CI/CD, Cloud and SRE with AI and Analytics

December 26, 2019

Are machines ready to write, test, debug and patch enterprise software? What are the emerging capabilities? Is it a fantasy or fast happening already? How are enterprises strategizing and retooling?Gi …
Building high-performance text classifiers on a limited labeling budget

Building high-performance text classifiers on a limited labeling budget

December 26, 2019

Robert Horton, Mario Inchiosa, and Ali Zaidi demonstrate how to use three cutting-edge machine learning techniquestransfer learning from pretrained language models, active learning to make more effective use of a limited labeling budget, and hyperparameter tuning to maximize model performanceto up your modeling game.
Executive Briefing: Overview of data governance

Executive Briefing: Overview of data governance

December 25, 2019

Effective data governance is foundational for AI adoption in enterprise, but it's an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more. Join in to learn what impact machine learning has on data governance and vice versa.
Faster ML over joins of tables

Faster ML over joins of tables

December 24, 2019

Arun Kumar details recent techniques to accelerate ML over data that is the output of joins of multiple tables. Using ideas from query optimization and learning theory, Arun demonstrates how to avoid joins before ML to reduce runtimes and memory and storage footprints. Along the way, he explores open source software prototypes and sample ML code in both R and Python.
Federated learning

Federated learning

December 24, 2019

Imagine building a model whose training data is collected on edge devices such as cell phones or sensors. Each device collects data unlike any other, and the data cannot leave the device because of privacy concerns or unreliable network access. This challenging situation is known as federated learning. Mike Lee Williams discusses the algorithmic solutions and the product opportunities.
Lightning Talk: Kuber-What-Es?! Misadventures in Building UIs for K8s-Based ML Platforms

Lightning Talk: Kuber-What-Es?! Misadventures in Building UIs for K8s-Based ML Platforms

December 24, 2019

Last year, our team set out to build a machine learning platform for launching hyperparameter optimization jobs. However, after our launch, the kubernetes-based machine learning platform wasn't a hit …
MLflow: An open platform to simplify the machine learning lifecycle

MLflow: An open platform to simplify the machine learning lifecycle

December 22, 2019

Developing applications that leverage machine learning is difficult. Practitioners need to be able to reproduce their model development pipelines, as well as deploy models and monitor their health in production. Corey Zumar offers an overview of MLflow, which simplies this process by managing, reproducing, and operationalizing machine learning through a suite of model tracking and deployment APIs.
The journey toward a self-service data platform at Netflix

The journey toward a self-service data platform at Netflix

December 20, 2019

The Netflix data platform is a massive-scale, cloud-only suite of tools and technologies. It includes big data tech (Spark and Flink), enabling services (federated metadata management), and machine learning support. But with power comes complexity. Kurt Brown explains how Netflix is working toward an easier, "self-service" data platform without sacrificing any enabling capabilities.
Introducing KFServing: Serverless Model Serving on Kubernetes

Introducing KFServing: Serverless Model Serving on Kubernetes

December 17, 2019

Production-grade serving of ML models is a challenging task for data scientists. In this talk, we'll discuss how KFServing powers some real-world examples of inference in production at Bloomberg, whic …
Running Large-Scale Stateful Workloads On Kubernetes at Lyft

Running Large-Scale Stateful Workloads On Kubernetes at Lyft

December 9, 2019

Along with core services, K8s at Lyft also forms the base to run a large variety of data processing stateful data processing jobs which includes Spark, Flink and other jobs via various ML and Data pro …
Panel: Enterprise-grade, On-prem Kubeflow in the Financial Sector

Panel: Enterprise-grade, On-prem Kubeflow in the Financial Sector

December 5, 2019

This presentation will explore the journeys of two ML architects from JPMorgan Chase and US Bank, who have deployed Kubeflow into their on-premise environments. These subject matter experts will revie …
Kubeflow: Multi-Tenant, Self-Serve, Accelerated Platform for Practitioners

Kubeflow: Multi-Tenant, Self-Serve, Accelerated Platform for Practitioners

December 4, 2019

The kubeflow platform provides a self-serve multi-tenant platform on k8s for ML developers. Users can train their models using accelerated hardware in an isolated environment. Jobs can be configured a …
Kubernetizing Big Data and ML Workloads at Uber

Kubernetizing Big Data and ML Workloads at Uber

November 25, 2019

Uber relies on Big Data and ML to make business critical decisions such as pricing, trip ETA, etc. Today, those workloads such as Hive and Spark are running on YARN. To save millions of dollars by eff …
Tutorial: From Notebook to Kubeflow Pipelines: An End-to-End Data Science Workflow

Tutorial: From Notebook to Kubeflow Pipelines: An End-to-End Data Science Workflow

November 22, 2019

Please bring your laptop fully charged as we will have limited charging stations available in the room.This session targets data scientists and ML engineers who want to leverage Kubernetes to scale up …
Towards Kubeflow 1.0, Bringing a Cloud Native Platform For ML to Kubernetes

Towards Kubeflow 1.0, Bringing a Cloud Native Platform For ML to Kubernetes

November 5, 2019

In December of 2017, a small number of folks from a handful of companies introduced Kubeflow; an open, cloud native platform for machine learning. The project has gained a lot of momentum with hundred …