PAPIs 2018 has ended

Sign up or log in to bookmark your favorites and sync them to your phone or calendar.

ML Engineering & ScienceOps [clear filter]
Tuesday, October 16

11:00am EDT

Production Machine Learning with a Domain Specific Language
Many currently available machine learning (ML) platforms focus on algorithms, but gloss over many of the other difficult parts of operating a scalable, production quality ML training and prediction system. We describe a machine learning platform that focuses on abstracting away the most difficult parts of operationalizing ML system including flexible yet performant feature extraction via a custom-designed domain-specific language(DSL), a low-latency model prediction service using ensembles of models in a single prediction, and a model management system for tracking versions of a model over time.

avatar for Zachary Kozick

Zachary Kozick

Sr Software Engineer, ML Platform, Nanigans
Zach Kozick is a Sr Software Engineer who has worked at Nanigans for over 5 years. He's made significant contributions to Nanigans' data ingestion and processing infrastructure, and has lead development of their in-house machine learning framework NanML, the subject of his talk... Read More →

Tuesday October 16, 2018 11:00am - 11:20am EDT
Deborah Sampson

11:30am EDT

DevOps for AI Applications
With the booming adoption of AI applications there is a need to better integrate the process of creating, updating and maintaining ML models in a standard Continuous Integration/Continuous Deployment (CI/CD) pipeline. A CI/CD pipeline in software development provides control of releasing the right version to the right environment, ability to rollback in case of an error and ability to manage the process. In this talk, we will walk over the process of automating model operationalization and deployment across different environments from our learnings from customers and internal products.

avatar for Richin Jain

Richin Jain

Software Engineer, Microsoft
Richin is a Software Engineer in the Cloud AI team at Microsoft. He focuses on building AI and ML solutions to solve real business problems for enterprise customers in multiple domains. Prior to Microsoft, he worked on data analytics and identity management at Nokia/HERE Technolo... Read More →

Tuesday October 16, 2018 11:30am - 11:50am EDT
Deborah Sampson

12:00pm EDT

Designing Services for Recommending Jobs to College Students
At WayUp, the leading platform for connecting college students and young professionals to internships, part-time jobs, and entry-level roles, our technology recommends job listings and other content to users immediately after they join the site and create a profile. In this talk, I discuss how constraints from this business model and site design are reflected in our technical design for job and content recommendation systems. I'll cover separation of concerns, API design, data flow, batch and real-time processes, DevOps, metrics, recommender algorithms, and the impact on user engagement.

avatar for Harlan D. Harris

Harlan D. Harris

Director of Data Science, WayUp
Harlan Harris has a PhD in Computer Science/Machine Learning from the University of Illinois, and worked as a Cognitive Psychology researcher before turning to industry. He is currently Director of Data Science at WayUp, has worked at Kaplan Test Prep, the Advisory Board Company... Read More →

Tuesday October 16, 2018 12:00pm - 12:20pm EDT
Deborah Sampson

2:00pm EDT

How to Go From Data Science to Data Operations
According to Google developers, "Only a small fraction of real-world ML systems are composed of the ML code. The required surrounding infrastructure is vast and complex."  By focusing on DataOps your teams will be able to deliver faster, with higher quality, using the tools that they love. The topics covered will include:
  • Data science challenges and DataOps definitions.
  • The four As of DataOps
    • Automate and monitor pipelines
    • Automate deployments
    • Automate and monitor quality
    • Automate sandboxes

avatar for Gil Benghiat

Gil Benghiat

Founder, VP Products, DataKitchen
Gil Benghiat, co-founder of DataKitchen, a company on a mission to enable analytic teams to deliver value quickly and with high quality using the tools they love. Gil's‚ career has always been data-oriented starting with network data at AT&T Bell Laboratories, any data at Sybase... Read More →

Tuesday October 16, 2018 2:00pm - 2:20pm EDT
Deborah Sampson

2:30pm EDT

A Config-based Framework for Productionized Machine Learning
For a data scientist, building correct models quickly and moving them to production safely can be challenging. Especially with messy data, catching and debugging data quality issues in model-building is also challenging. This talk will discuss the "Clover transform framework", a config-based production machine learning framework to allow easy and fast generation of features and models, for fast iteration speeds, easy parameter tuning, algorithm choice, monitoring, and auditability, using the same config for both development iteration and running in production.

avatar for Melanie Goetz

Melanie Goetz

Head of Machine Learning, Clover Health
Melanie runs the Machine Learning team at Clover Health, a health insurance startup in San Francisco that uses machine learning to keep members healthy and out of the hospital. She studied Linguistics and Math/CS at MIT and ML at UPenn. Previously, she worked in Japan on machine translation... Read More →

Tuesday October 16, 2018 2:30pm - 2:50pm EDT
Deborah Sampson

3:00pm EDT

Unpredictable predictions of self-driving cars AI
No matter how good your Machine Learning model is trained, the inference output space leaves a wide range for appearing irrelevant and unexpected results when real world gives a model an unforeseen challenge. Those error inferences may lead to accidental outcomes, there are notorious cases we all know. 
The solution is robust monitoring for the edge cases and implementing the Active Learning concept into businesses' AI/ML operations for those cases to be handled and learned.
The talk will be dedicated to practical solutions and their implementation into business operations.

avatar for Iskandar Sitdikov

Iskandar Sitdikov

ML/Software engineer, Hydrosphere.io
Iskandar Sitdikov is Hydrosphere.io ML engineer with rich practical background both in Machine Lerarning and Big Data fields. His latest tasks lie in the area of research and prototyping data anomalies and concept drifts detection methods in ML production.

Tuesday October 16, 2018 3:00pm - 3:20pm EDT
Deborah Sampson

Twitter Feed