Panel: What Do Engineers Not Get About Working with Data Scientists?
ML is increasingly making its way into production to power customer-facing applications and business processes. This transition from batch to operational ML raises new organizational challenges. Data scientists and engineers now have to work …
Extending Open Source Feature Stores to Fit Adyen
We walk you through how we adopted Feast at Adyen. We’ll discuss the decisions we made because of infra and tech constraints, and the customizations we added— in particular for our open source project, spark-offline-store, which was adopted into …
Streaming is just an implementation detail
Microservices are stream processing; whether you’re using Redis, Kafka, or gRPC, you continuously handle events and manage consistency. And given that these are some of the most challenging problems in databases, you’re probably not doing a very good …
Effective ML System Development
In order to efficiently deliver and maintain ML systems, the adoption of MLOps practices is a must. In recent times, the ML community has embraced and modified ideas originating from software engineering with reasonable success. Software 2.0 (AI/ML) …
Data Observability for Machine Learning Teams
Once models go to production, observability becomes key to ensuring reliable performance over time. But what’s the difference between “ML Observability” and “Data Observability”, and how can ML Engineering teams apply them to maintain model …
Managing the Flywheel of ML Data
The ML Engineer’s life has become significantly easier over the past few years, but ML projects are still too tedious and complex. Feature stores have recently emerged as an important product category within the MLOps ecosystem. They solve part of …
Machine Learning Platform for Online Prediction and Continual Learning
This talk breaks down stage-by-stage requirements and challenges for online prediction and fully automated, on-demand continual learning. We’ll also discuss key design decisions a company might face when building or adopting a machine learning …
ML Design Patterns for Data Engineers
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 three …
Panel: Challenges of Operationalizing ML
Our panel discussion will focus on the main challenges of building and deploying ML applications. We’ll discuss common pitfalls, development best practices, and the latest trends in tooling to effectively operationalize ML
Exploiting the Data Code: Duality Applying Modern Software Development Practices to Data with Dali
Most large software projects in existence today are the result of the collaborative efforts of hundreds or even thousands of developers. These projects consist of millions of lines of code and leverage a plethora of reusable libraries and services …
Building a Best-in-Class Customer Experience Platform – The Hux Journey – Deloitte Digital
New technologies have been advancing rapidly across the areas of frictionless data ingestion, customer data management, identity resolution, feature stores, MLOps and customer interaction orchestration. Over the same period many large enterprises …
Real-time Personalization of QuickBooks using Clickstream Data
In this session, we will talk about Intuit’s real-time personalization ML pipeline. We will use a self-help use case to show how Intuit provides proactive self-help to millions of users by personalizing content based on user behavior to increase …