Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

Sponsored sessions

 
9:20am9:25am Wednesday, March 27, 2019
Location: Ballroom
Average rating: **...
(2.78, 27 ratings)
The journey to AI begins with data and making intelligent use of it. Dinesh Nirmal shares a strategic framework for streamlining your data assets, a framework that takes into account the current state of your existing data structures, the new technologies driving enterprise, the complexities of business processes, and at the foundation, the elements required in an AI-fluent data platform. Read more.
9:25am9:30am Wednesday, March 27, 2019
Location: Ballroom
Jed Dougherty (Dataiku)
Average rating: ****.
(4.37, 41 ratings)
One widely accepted definition of AI is that it means going beyond simple statistics to mimic human skills in perception, learning, interaction, and decision making. Jed Dougherty tightens up this definition by sharing examples on a matrix that breaks down the different parts of that definition and how they might manifest themselves in data science projects at different levels. Read more.
11:00am11:40am Wednesday, March 27, 2019
Location: 2014
Secondary topics:  Jupyter
Alan Chin (IBM), LUCIANO RESENDE (IBM)
Average rating: ****.
(4.75, 4 ratings)
Alan Chin and Luciano Resende explain how to introduce Jupyter Enterprise Gateway into new and existing notebook environments to enable a "bring your own notebook" model while simultaneously optimizing resources consumed by the notebook kernels running across managed clusters within the enterprise. Read more.
11:00am11:40am Wednesday, March 27, 2019
Location: 2007
Chris Bush (Levi's )
Average rating: ****.
(4.50, 2 ratings)
Building a data science practice in any environment is difficult. Integrating data science into a long-standing company with established processes, complex business operations, and global scale creates additional layers of complexity that need to be navigated. Chris Bush explains how Levi’s is tackling this challenge and shares the company's continuing evolution to leverage data science. Read more.
11:00am11:40am Wednesday, March 27, 2019
Location: 2003
Ian Swanson (Oracle)
Average rating: ***..
(3.00, 2 ratings)
Being an AI-­driven enterprise earlier than a competitor is an opportunity within your reach. Join in to find out how, as Ian Swanson dives into problem domains, platform differentiators, ease of use, automation, and scale and shares best practices on quick starts with the right infrastructure choices. Read more.
11:00am11:40am Wednesday, March 27, 2019
Location: 2005
Average rating: ****.
(4.50, 4 ratings)
Sam Lightstone discusses how AI is fundamentally changing computer science and the practice of coding. Join in to discover what machine learning means today and explore recent advances in hardware and software and breakthrough innovations. Read more.
11:00am11:40am Wednesday, March 27, 2019
Location: 2022
Average rating: ****.
(4.33, 3 ratings)
Raghu Chakravarth explores key considerations when building an Agile data warehouse and outlines a reference architecture for hybrid data. Read more.
11:50am12:30pm Wednesday, March 27, 2019
Location: 2003
Sarah Gates (SAS)
Average rating: ***..
(3.50, 2 ratings)
SAS empowers you with choice and control, helping you uncover insights from any data for better, faster decisions regardless of language.  Sarah Gates shares methods for accelerating the analytics lifecycle, improving data preparation, quality, and governance, automating and speeding up time-consuming tasks, and quickly creating, selecting, and deploying models—be it one or thousands. Read more.
11:50am12:30pm Wednesday, March 27, 2019
Location: 2014
Secondary topics:  Jupyter
Omoju Miller (GitHub)
Average rating: ***..
(3.50, 10 ratings)
GitHub has a relatively nascent ML group. Its major challenge is to integrate ML product building processes into a mature product engineering org. This means that it's responsible for building end-to-end ML products, from ETL to production. Omoju Miller details the many roles Jupyter notebooks play in the building of ML products. Read more.
11:50am12:30pm Wednesday, March 27, 2019
Location: 2022
Average rating: *****
(5.00, 1 rating)
Recently, Scott Mcclellan's team—which analyzes over six petabytes of data using Hadoop technology—created a high-performance data lake using object storage for consumption by big data workloads. Scott shares his experience deploying object storage for AI workloads. Read more.
11:50am12:30pm Wednesday, March 27, 2019
Location: 2005
Mehul Shah (Amazon Web Services )
Average rating: *****
(5.00, 2 ratings)
Mehul Shah offers an overview of serverless computing and details AWS Glue's severless analytics features for data science, data discovery, data cleaning and transformation, and data lake management. Read more.
11:50am12:30pm Wednesday, March 27, 2019
Location: 2007
SYED LATHEEF (Verizon)
Average rating: ****.
(4.00, 1 rating)
Verizon wanted to use its BI on Big Data platform to enable real-time artificial intelligence and machine learning to identify friction points, detect anomalies on the fly, and fix issues instantly. Latheef Syed explains how Verizon utilizes Kyvos as a next-generation analytical platform that delivers real-time AI, ML, and BI. Read more.
2:40pm3:20pm Wednesday, March 27, 2019
Location: 2005
Harinder Singh (AB inBev)
Average rating: ****.
(4.50, 4 ratings)
Harinder Singh explains how, over the course of two years, the world’s largest brewer completely modernized its data architecture and moved it to the cloud. By accelerating data analytics and freeing up the time of its data scientists, AB inBev has been able to better anticipate demand and production, streamline logistics, and develop new beverages that have become best-sellers. Read more.
2:40pm3:20pm Wednesday, March 27, 2019
Location: 2014
Secondary topics:  Jupyter
M Pacer (Netflix)
Average rating: ****.
(4.57, 7 ratings)
M Pacer discusses two meanings of "Talking with Jupyter": talking to others with Jupyter notebooks and talking to Jupyter in the language of its standards, formats, and protocols. M describes tools, workflows, and patterns that make both kinds of talking with Jupyter easier while unlocking new ways of interacting with the Jupyter ecosystem. Read more.
2:40pm3:20pm Wednesday, March 27, 2019
Location: 2007
Ashwin Ramachandran (Syncsort)
Average rating: ***..
(3.50, 2 ratings)
"Legacy" data sources like mainframes and data warehouses still power mission-critical applications, holding the historical and transactional insight essential for advanced analytics and real-time applications. Ashwin Ramachandran shares strategies, tools, and techniques for successfully deriving value from these sources using today's modern architectures while future-proofing for what lies ahead. Read more.
2:40pm3:20pm Wednesday, March 27, 2019
Location: 2022
Jagane Sundar (WANdisco)
Average rating: ****.
(4.50, 2 ratings)
Jagane Sundar shares a system for replicating data across geographically distributed data centers and discusses the benefits of consistently replicating data that is used by TensorFlow for training. Read more.
2:40pm3:20pm Wednesday, March 27, 2019
Location: 2003
Average rating: ****.
(4.00, 1 rating)
Stephen Dantu shares insurance broker Marsh’s pioneering journey into the public cloud and explains why this move was necessary to unleash new opportunities and future-proof the company. Read more.
4:20pm5:00pm Wednesday, March 27, 2019
Location: 2003
Average rating: ***..
(3.00, 1 rating)
MySQL is great but has limits. When you need key-value pair storage with geospatial and JSON support, easy and fast ingestion from various streams, aggregate queries against 100+ million rows in under one second, and more, there's only one solution. Franck Leveneur explains how on-demand dog walking service Wag! uses MemSQL to take its real-time data access and reporting to the next level. Read more.
4:20pm5:00pm Wednesday, March 27, 2019
Location: 2005
Yang Li (Kyligence)
Average rating: ****.
(4.00, 1 rating)
Augmenting data management and analytics platforms with artificial intelligence and machine learning is game changing for analysts, engineers, and other users. It enables companies to optimize their storage, speed, and spending. Yang Li details the Kyligence platform, which is evolving to the next level with augmented capabilities such as intelligent modeling, smart pushdowns, and more. Read more.
4:20pm5:00pm Wednesday, March 27, 2019
Location: 2014
Secondary topics:  Jupyter
Chris Holdgraf (Berkeley Institute for Data Science)
Average rating: ****.
(4.75, 4 ratings)
Chris Holdgraf shares recent tools from the Jupyter project in partnership with UC Berkeley that facilitate communication with Jupyter and get us closer to displaying notebook-style content in a more discoverable and reader-friendly form—allowing you to turn collections of notebooks into an online book and connect this content with the cloud in order to make your online content interactive. Read more.
4:20pm5:00pm Wednesday, March 27, 2019
Location: 2022
Joel Hron (ThoughtTrace), Nick Vandivere (ThoughtTrace)
Average rating: ****.
(4.00, 1 rating)
Building a SaaS AI company targeted at enterprise users presents unique challenges, both technical and nontechnical. Joel Hron and Nick Vandivere walk you through ThoughtTrace's journey, highlighting its beginnings as a company and sharing the challenging use cases the company tackled first. Read more.
5:10pm5:50pm Wednesday, March 27, 2019
Location: 2014
Secondary topics:  Jupyter
Average rating: ***..
(3.43, 7 ratings)
Project Jupyter is very popular for data science, data exploration, and visualization. Manu Mukerji and Justin Driemeyer explain how to use it for AI/ML in a production environment. Read more.
5:10pm5:50pm Wednesday, March 27, 2019
Location: 2003
Geoff Tudor (Vizion.ai)
Average rating: *....
(1.00, 2 ratings)
Elasticsearch is powerful. In its current form, it's also nontrivial and rather expensive to deploy. Not very "elastic." Fortunately, innovations like serverless and microservices are eliminating these barriers, lowering upfront costs, and reducing complexity. Geoff Tudor explains how this is unfolding in the market. Read more.
9:00am9:10am Thursday, March 28, 2019
Location: Ballroom
Jordan Tigani (Google )
Average rating: ***..
(3.45, 11 ratings)
Modern data analysis requirements have fundamentally redefined what our expectations should be for data warehouses. Join Google BigQuery cocreator Jordan Tigani as he shares his vision for where he sees cloud-scale data analytics heading as well as what technology leaders should be considering as part of their data warehousing roadmap. Read more.
11:00am11:40am Thursday, March 28, 2019
Location: 2005
Prakhar Mehrotra (Walmart Labs)
Average rating: ****.
(4.14, 7 ratings)
Prakhar Mehrotra shares Walmart’s digital transformation journey and explains how the company is using recent advancements in machine learning to power core retail operations like pricing, assortment, and replenishment. Along the way, Prakhar demonstrates how to leverage human expertise and use it as feedback to improve your algorithms. Read more.
11:50am12:30pm Thursday, March 28, 2019
Location: 2003
Adam Famularo (erwin, Inc.)
Average rating: ****.
(4.00, 1 rating)
Adam Famularo showcases erwin's combination of data management and data governance to produce actionable insights. Erwin customer Nasdaq then shares a real-world use case. You'll learn how to answer tough data questions, how to maintain a metadata landscape, and how to use data management and governance to produce actionable insights. Read more.
11:50am12:30pm Thursday, March 28, 2019
Location: 2005
Priyank Patel (Arcadia Data)
Average rating: ****.
(4.00, 1 rating)
With cloud object storage, you'd expect business intelligence (BI) applications to benefit from the scale of data and real-time analytics. However, traditional BI in the cloud surfaces non-obvious challenges. Priyank Patel reviews service-oriented cloud design (storage, compute, catalog, security, SQL) and shows how native cloud BI provides analytic depth, low cost, and high performance. Read more.
1:50pm2:30pm Thursday, March 28, 2019
Location: 2003
Jordan Tigani (Google )
Average rating: ****.
(4.00, 3 ratings)
Google Cloud Platform combines powerful serverless solutions for enterprise data warehousing, streaming analytics, managed Spark and Hadoop, modern BI, planet-scale data lake, and AI. Jordan Tigani details Google Cloud’s vision and engineering strategy, which can help you move big data analytics solutions to the next level of benefits. Read more.