How do you bridge the gap between data science and data engineering, which is necessary to reliably and repeatedly create production models and deploy them in production? You’ll hear from experts about various aspects of MLOps. They will discuss the unique challenges of deploying ML to production, along with the tools and techniques necessary for success.
10:00am: Introducing Ray Serve: Scalable and Programmable ML Serving Framework, Simon Mo (Anyscale)
10:15am: Building Scalable Natural Language Processing Pipelines with Ray, Qingqing Mao (Dascena)
10:30am: Project Zouwu: Scalable AutoML for Telco Time Series Analysis using Ray and Analytics Zoo, Ding Ding (Intel)
10:45am: Panel discussion and audience Q&A moderated by Dean Wampler (Anyscale)
Introducing Ray Serve: Scalable and Programmable ML Serving Framework, Simon Mo (Anyscale)
After data scientists train a machine learning (ML) model, the model needs to be served for interactive scoring or batch predictions. The go-to solution is often to wrap the model inside a Flask microservice. But when is that not enough? In this talk, Simon will discuss the short-comings of the Flask-only solution and then discuss the more common alternative, the “tensor prediction service” approach used by TFServing, SageMaker, and others. Simon will then introduce an easy-to-use, scalable ML serving system “Ray Serve” that overcomes the deficiencies of the two approaches. Simon will highlight the architectural innovations in Ray Serve.
Building Scalable Natural Language Processing Pipelines with Ray, Qingqing Mao (Primer AI)
At Primer AI, we build machines that can read and write, automating the analysis of very large document datasets. Our clients include some of the world’s largest government agencies, financial institutions, and Fortune 50 companies. It is challenging to build NLP analytical pipelines that are both comprehensive and scalable because different NLP tasks may have different computation requirements and the tasks may have interdependencies. This becomes more challenging when many clients require on-premise deployment with restricted computation capacity. We use Ray to build some of our NLP pipelines. Ray helps us narrow the gap between data science and engineering, and it enables our data scientists to write high-performance data analytics pipelines that can scale.
Project Zouwu: Scalable AutoML for Telco Time Series Analysis using Ray and Analytics Zoo, Ding Ding (Intel)
Time series analysis plays a crucial rule in the telecom applications, such as network quality analysis, network capacity forecast, smart power management, etc. There’s a recent trend to apply machine learning methods (especially neural networks) to such problems, and they are reported to perform better in many cases than traditional methods such as autoregression and exponential smoothing.
However, building the machine learning applications for time series forecasting can be a laborious and knowledge-intensive process. In this talk, we present Project Zouwu, which provides Automated Machine Learning (AutoML) to time series analysis for Telco application. It is built on top of Ray (https://github.com/ray-project/ray) and Analytics Zoo (https://github.com/intel-analytics/analytics-zoo), so as to automate the process of feature generation and selection, model selection and hyper-parameter tuning in a distributed fashion. We will also share some real-world experience and “war stories” of earlier users.