Numeric results with bulletproof confidence: this is what companies actually sell when promoting their machine learning product. Yet this seems out of reach when the product is both generic and complex, with much of the inner calculations hidden from the end user. So how can code improvements or changes in core component performance be tested at scale? Implementing API and Load Tests is time-consuming, but thorough: defining parameters, building infrastructure and debugging. The bugs may be real, but they can also be a result of poor infrastructure implementation (who is testing the testers?). In this session we will discuss how Airflow can help scale up testing in a stable and sustainable way.

For most ML-based SaaS companies, the need to fulfill each customer’s KPI will usually be addressed by matching a dedicated model. Along with the benefits of optimizing the model’s performance, a model per customer solution carries a heavy production complexity with it. In this manner, incorporating up-to-date data as well as new features and capabilities as part of a model’s retraining process can become a major production bottleneck. In this talk, we will see how Riskified scaled up modeling operations based on MLOps ideas, and focus on how we used Airflow as our ML pipeline orchestrator. We will dive into how we wrap Airflow as an internal service, the goals we started with, the obstacles along the way and finally - how we solved them. You will receive tools for how to set up your own Airflow-based continuous training ML pipeline, and how we adjusted it such that ML engineers and data scientists would be able to collaborate and work in parallel using the same pipeline.

Get your ticket for this workshop Tensorflow Extended (TFX) can run machine learning pipelines on Airflow, but all the steps are run by default in the same workers where the Airflow DAG is running. This can lead to an excessive usage of resources, and breaks the assumption that Airflow is a scheduler; it becomes also the data processing platform. In this session, we will see how to use TFX with third party services, on top of Google Cloud Platform. The data processing steps can be run in Dataflow, Spark, Flink and other runners (parallelizing the processing of data and scaling up to petabytes), and the training steps can be run in Vertex or other external services. After this workshop, you will have learnt how to externalize any TFX heavyweight computing outside Airflow, while maintaining Airflow as the orchestrator for your machine learning pipelines.

At Credit Karma, we enable financial progress for more than 100 million of our members by recommending them personalized financial products when they interact with our application. In this talk we are introducing our machine learning platform to build interactive and production model-building workflows to serve relevant financial products to Credit Karma users. Vega, Credit Karma’s Machine Learning Platform, has 3 major components: 1) QueryProcessor for feature and training data generation, backed by Google BigQuery, 2) PipelineProcessor for feature transformations, offline scoring and model-analysis, backed by Apache Beam 3) ModelProcessor for running Tensorflow and Scikit models, backed by Google AI Platform, which provides data scientists the flexibility to explore different kinds of machine learning or deep learning models, ranging from gradient boosted trees to neural network with complex structures Vega exposed a unified Python API for Feature Generation, Modeling ETL, Model Training and Model Analysis. Vega supports writing interactive notebooks and python scripts to run these components in local mode with sampled data and in cloud mode for large scale distributed computing. Vega provides the ability to chain the processors provided by data scientists through Python code to define the entire workflow. Then it automatically generates the execution plan for deploying the workflow on Apache Airflow for running offline model experiments and refreshes. Overall, with the unified python API and automated Airflow DAG generation, Vega has improved the efficiency of ML Engineering. Using Airflow we deploy more than 20K features and 100 models daily

This workshop is sold out Hands on workshop showing how easy it is to deploy Airflow in a public Cloud. Workshop consists of 3 parts: Setting up Airflow environment and CI/CD for DAG deployment Authoring a DAG Troubleshoot Airflow DAG/Task execution failures This workshop will be based on Cloud Composer ( https://cloud.google.com/composer ) This workshop is mostly targeted at Airflow newbies and users who would like to learn more about Cloud Composer and how to develop DAGs using Google Cloud Platform services like BigQuery, Vertex AI, Dataflow.