Video description

Table of Contents

Keynotes

Merging human and machine learning for everyday solutions - Li Fan (Pinterest)

To a hammer, everything is a nail: Choosing the right tool for your business problems (sponsored by Microsoft) - Tobias Ternstrom (Microsoft)

Privacy in the age of machine learning - Ben Lorica (O’Reilly Media)

Crisis Text Line data usage and insights - Nancy Lublin and Bob Filbin (Crisis Text Line)

Defining responsible data practices: A community-driven approach - Natalie Evans Harris (BrightHive)

Operationalizing machine learning (sponsored by IBM) - Dinesh Nirmal (IBM)

Data Science in the Cloud - Alex Smola (Amazon)

Sprouted clams and stanky bean: When machine learning makes mistakes - Janelle Shane (aiweirdness.com)

The case for a deliberate data strategy in today’s attention-deficit economy (sponsored by MapR) - Anoop Dawar (MapR Technologies)

Differentiating via data science - Eric Colson (Stitch Fix)

Automating decisions with data in the cloud - Amr Awadallah (Cloudera)

What separates the clouds? (sponsored by Google Cloud) - William Vambenepe (Google)

Inclusivity for the greater good - Ajey Gore (GO-JEK)

Lessons in Google Search data - Seth Stephens-Davidowitz (Everybody Lies | NY Times)

Data science and machine learning

Approaching the pricing problem at Lyft - Ashivni Shekhawat (Lyft)

Graph analysis of 200,000 tweets from Russian Twitter trolls - Ryan Boyd (Neo4j)

Small pieces, loosely joined: A skater’s code - Rodney Mullen (Almost Skateboards)

Lessons learned deploying machine learning and deep learning models in production at major tech companies - Harish Doddi (Datatron Technologies), Jerry Xu (Datatron Technologies)

Spark NLP in action: Improving patient flow forecasting at Kaiser Permanente - David Talby (Pacific AI), Santosh Kulkarni (Kaiser Permanente)

Deep credit risk ranking with LSTM - Kyle Grove (Teradata)

Who are we? The largest-scale study of professional data scientists - Miryung Kim (UCLA), Muhammad Gulzar (UCLA)

The current state of TensorFlow and where it’s headed in 2018 - Rajat Monga (Google)

Using deep learning to solve challenging problems - Jeff Dean (Google)

Detecting time series anomalies at Uber scale with recurrent neural networks - Andrea Pasqua (Uber), Anny Chen (Uber)

Data science at Slack - Josh Wills (Slack)

Breaking up the block: Using heterogenous population modeling to drive growth - Daniel Lurie (Pinterest)

Machine learning applications for the industrial internet - Joseph Richards (GE Digital)

Explaining machine learning models - Evan Kriminger (ZestFinance)

sparklyr, implyr, and more: dplyr interfaces to large-scale data - Ian Cook (Cloudera)

Word embeddings under the hood: How neural networks learn from language - Patrick Harrison (S Global)

Cataloging the visible universe through Bayesian inference at petascale in Julia - Keno Fischer (Julia Computing)

Building career advisory tools for the tech sector using machine learning - Simon Hughes (Dice.com), Yuri Bykov (Dice.com)

Enough data engineering for a data scientist; or, How I learned to stop worrying and love the data scientists - Stephen O’Sullivan (Data Whisperers)

Fast and effective natural language understanding - Mike Conover (SkipFlag)

Transforming a machine learning prototype to a deployable solution leveraging Spark in healthcare - Rachita Chandra (IBM Watson Health)

Interpretable machine learning products - Mike Lee Williams (Cloudera Fast Forward Labs)

Being smarter than dinosaurs: How NASA uses deep learning for planetary defense - Siddha Ganju (Deep Vision)

Data Case Studies

Supply chain evolution from horseless buggies to driverless cars - Valentin Bercovici (Pencil Data Inc.)

Automation and analytics enablement in life insurance - Divya Ramachandran (Captricity)

Building a flu predictor model for improved patient care - Jennie Shin (Kaiser Permanente)

From the presidential campaign trail to the enterprise: Building effective data-driven teams - Katie Malone (Civis Analytics)

Your enterprise AI is only as good as your data. - Joe Dumoulin (Next IT)

Smart agriculture: Blending IoT sensor data with visual analytics - Mike Prorock (mesur.io)

Automating business insights through artificial intelligence - Wayde Fleener (General Mills)

Using ML to improve UX and literacy for young poets - Ann Nguyen (Whole Whale)

Working with the data of sports - Thomas Miller (Northwestern University)

Sponsored

The changing role of the CDO: Three keys for success (sponsored by MapR) - Jim Scott (MapR Technologies)

The four elements of modern analytics (sponsored by MicroStrategy) - Vijay Kotu (Oath)

Speed up mission-critical analytics in the cloud (sponsored by Kyligence) - Billy Liu (Kyligence)

Analytics in real time, the (Grey’s) anatomy of event streaming (sponsored by MemSQL) - Adam Ahringer (Disney-ABC TV Digital Media)

The Snowflake data warehouse: How Sharethrough analyzes petabytes of event data in a SQL database (sponsored by Snowflake) - Dave Abercrombie (Sharethrough)

Building the bridge from big data to machine learning and artificial intelligence (sponsored by Google Cloud) - Ryan Lippert (Google Cloud)

Harnessing the cloud to enable connected systems and self-service and accelerate business growth (sponsored by Talend) - Jeff Smits (RingCentral)

Building machine learning systems for scale: Amazon insights and best practices (sponsored by Amazon Web Services) - Guy Ernest (Amazon Web Services)

Focus on your business: Case studies on building data solutions that meet your needs (sponsored by Microsoft) - Tobias Ternstrom (Microsoft)

Data at scale and speed: Real-world use cases (sponsored by MapR) - Ted Dunning (MapR Technologies)

When tests cry wolf (sponsored by Pure Storage) - Ivan Jibaja (Pure Storage)

Managing the intelligent data pipeline and the connected enterprise (sponsored by Hitachi Vantara) - Chuck Yarbrough (Hitachi Vantara)

Journey to digital (sponsored by IBM) - Seth Dobrin, PhD (IBM)

Architecting an edge-to-cloud data pipeline to unify multiple data sources and processing engines (sponsored by NetApp) - Santosh Rao (NetApp)

Digital transformation demands faster, more productive data science (sponsored by DataScience.com) - Ian Swanson (DataScience.com)

Get a farm-to-table view of your data: Track data lineage from source to analytics (sponsored by Syncsort) - Tendu Yogurtcu (Syncsort)

Accelerating analytics and AI from the edge to the cloud (sponsored by Intel) - Kevin Huiskes (Intel), Radhika Rangarajan (Intel)

On-device deep learning: Trends, technologies, and challenges (sponsored by TalkingData) - Andreas Pfadler (TalkingData)

Data engineering and architecture

The state of Postgres - Umur Cubukcu (Citus Data)

Building​ ​a​ ​flexible​ ​ML​ ​pipeline​ ​at​ ​a​ ​B2B​ ​AI​ ​start​up - Dorna Bandari (Jetlore)

Accelerating development velocity of production ML systems with Docker - Kinnary Jangla (Pinterest)

What’s new in Hadoop 3.0 - Daniel Templeton (Cloudera), Andrew Wang (Cloudera)

DataOps: An Agile methodology for data-driven organizations - Ellen Friedman (MapR Technologies)

Better machine learning logistics with the rendezvous architecture - Ted Dunning (MapR Technologies)

Taming deep learning - Evan Sparks (Determined AI)

Metrics-driven tuning of Apache Spark at scale - Edwina Lu (LinkedIn), Ye Zhou (LinkedIn), Min Shen (LinkedIn)

NoSQL no more: SQL on Druid with Apache Calcite - Gian Merlino (Imply)

Operationalize deep learning: How to deploy and consume your LSTM networks for predictive maintenance scenarios - Francesca Lazzeri (Microsoft), Fidan Boylu Uz (Microsoft)

TimescaleDB: Reengineering PostgreSQL as a time series database - Michael Freedman (TimescaleDB | Princeton)

The secret sauce behind LinkedIn’s self-managing Kafka clusters - Jiangjie Qin (LinkedIn)

Building a contacts graph from activity data - Alexis Roos (Salesforce), Noah Burbank (Salesforce)

Crafting data products for the augmented writing experience - Chris Harland (Textio)

Classifying job execution using deep learning - Ash Munshi (Pepperdata)

20 Netflix-style principles and practices to get the most out of your data platform - Kurt Brown (Netflix)

Strata Business Summit

Workplace culture in the age of algorithmic management: The information networks Uber drivers built - Alex Rosenblat (Data Society Research Institute )

How to avoid pitfalls when reasoning with data - Derek Ruths (CAI)

Executive Briefing: Artificial intelligence—The next digital frontier? - Michael Chui (McKinsey Global Institute)

Executive Briefing: Machine learning—Why you need it, why it’s hard, and what to do about it - Mike Olson (Cloudera)

Executive Briefing: Legal best practices for making data work - Alysa Z. Hutnik (Kelley Drye Warren LLP), Crystal Skelton (Kelley Drye Warren LLP)

Reinventing healthcare: Early detection of Alzheimer’s disease with deep learning - Ayin Vala (Foundation for Precision Medicine)

Executive Briefing: BI on big data - Mark Madsen (Third Nature), Shant Hovsepian (Arcadia Data)

Executive Briefing: The conversational AI revolution - Yishay Carmiel (IntelligentWire | Spoken Labs)

Bladder cancer diagnosis using deep learning - Mauro Damo (Dell EMC), Wei Lin (Dell EMC)

Streaming systems and real-time applications

Using machine learning to simplify Kafka operations - Shivnath Babu (Duke University | Unravel Data Systems), Dhruv Goel (Microsoft)

Deploying and monitoring interactive machine learning applications with Clipper - Dan Crankshaw (UC Berkeley RISELab)

Approximation data structures in streaming data processing - Debasish Ghosh (Lightbend )

How to build leakproof stream processing pipelines with Apache Kafka and Apache Spark​ - Jordan Hambleton (Cloudera), Guru Medasani (Domino Data Lab)

Stream storage with Apache BookKeeper - Sijie Guo (Streamlio)

Streaming SQL to unify batch and stream processing: Theory and practice with Apache Flink at Uber - Fabian Hueske (data Artisans), Shuyi Chen (Uber)

Foundations of streaming SQL; or, How I learned to love stream and table theory - Tyler Akidau (Google)

Kafka streaming applications with Akka Streams and Kafka Streams - Dean Wampler (Lightbend)

Effectively once, exactly once, and more in Heron - Karthik Ramasamy (Streamlio), Sanjeev Kulkarni (Streamlio)

Machine-learned model quality monitoring in fast data and streaming applications - Emre Velipasaoglu (Lightbend)

Playing well together: Big data beyond the JVM with Spark and friends - Holden Karau (Google), Rachel Warren (Salesforce Einstein)

The real-time journey from raw streaming data to AI-based analytics - Roy Ben-Alta (Amazon Web Services), Ira Cohen (Anodot)

Unified and elastic batch and stream processing with Pravega and Apache Flink - Fabian Hueske (data Artisans), Flavio Junqueira (Dell EMC)

HDFS on Kubernetes: Tech deep dive on locality and security - Kimoon Kim (Pepperdata), Ilan Filonenko (Bloomberg LP)

Effectively once in Apache Pulsar, the next-generation messaging system - Matteo Merli (Streamlio)

Media and Ad Tech

The data hero’s journey - Amanda Gerdes (Blizzard Entertainment)

The golden age of data and analytics - David Boyle (MasterClass)

Improving the customer experience via clickthru analytics - Sridhar Alla (Comcast)

Big data applicability to the gaming industry - Rizwan Patel (Caesars Entertainment)

Cohort analysis at scale - Blake Irvine (Netflix)

Show me the money: Understanding causality for ad attribution - April Chen (Civis Analytics)

Marketing at future speed - Kevin Lyons (Nielsen Marketing Cloud)

Data science in practice: Examining events in social media - Jennifer Webb (SuprFanz)

What is the relationship between social influence and the NBA? - Noah Gift (UC Davis)

Big data and data science in the cloud

Cloud, multicloud, and the data refinery - Tom Fisher (MapR Technologies)

How does a big data professional get started with AI? - Wee Hyong Tok (Microsoft), Danielle Dean (Microsoft)

Powering robotics clouds with Alluxio - Bin Fan (Alluxio), Shaoshan Liu (PerceptIn)

Machine learning versus machine learning in production - Manu Mukerji (8x8)

Streaming big data in the cloud: What to consider and why - William Chambers (Databricks), Michael Armbrust (Databricks)

Accelerating deep learning on Apache Spark using BigDL with coarse-grained scheduling - Shivaram Venkataraman (Microsoft Research), Sergey Ermolin (Intel)

Big data, big problems: Predicting climate change - Ari Gesher (Kairos Aerospace)

Radically modular data ingestion APIs in Apache Beam - Eugene Kirpichov (Google)

Machine learning to tackle industrial data fusion - Alexandra Gunderson (Arundo Analytics)

Vectorized query processing using Apache Arrow - Siddharth Teotia (Dremio)

Moving the needle of the pin: Streaming hundreds of terabytes of pins from MySQL to S3/Hadoop continuously - Henry Cai (Pinterest), Yi Yin (Pinterest)

Best practices for productionizing Apache Spark MLlib models - Joseph Bradley (Databricks)

Code Property Graph: A modern, queryable data storage for source code - Vlad A Ionescu (ShiftLeft), Fabian Yamaguchi (ShiftLeft)

Improving user-merchant propensity modeling using neural collaborative filtering and wide and deep models on Spark BigDL at scale - Sergey Ermolin (Intel), Suqiang Song (Mastercard)

Pipeline testing with Great Expectations - Abe Gong (Superconductive Health), James Campbell (USG)

Analytics in the cloud: Building a modern cloud-based big data warehouse - Greg Rahn (Cloudera)

Distributed deep learning with containers on heterogeneous GPU clusters - dong meng (MapR)

Hive as a service - Szehon Ho (Criteo), Pawel Szostek (Criteo)

Magellan: Scalable and fast geospatial analytics - Ram Sriharsha (Databricks)

Continuous delivery for NLP on Kubernetes: Lessons learned - Michelle Casbon (Google Cloud Platform Developer Relations)

Data reflections: Making data fast and easy to use without making copies - Tomer Shiran (Dremio), Jacques Nadeau (Dremio)

Building ML and AI pipelines with Spark and TensorFlow - Chris Fregly (PipelineAI)

Lyft’s analytics pipeline: From Redshift to Apache Hive and Presto - Shenghu Yang (Lyft)

Not your parents’ machine learning: How to ship an XGBoost churn prediction app in under four weeks - Goodman Gu (Atlassian)

Cuttlefish: Lightweight primitives for online tuning - Tomer Kaftan (University of Washington)

Data-driven business management

Managing data science at scale - Matthew Granade (Domino Data Lab)

Executive Briefing: Building effective heterogeneous data communities—Driving organizational outcomes with broad-based data science - Frances Haugen (Pinterest), Patrick Phelps (Pinterest)

Executive Briefing: Managing successful data projects—Technology selection and team building - Ted Malaska (Blizzard Entertainment), Jonathan Seidman (Cloudera)

If you can’t measure it, you can’t improve it: How reporting and experimentation fuel product innovation at LinkedIn - Kapil Surlaker (LinkedIn), Ya Xu (LinkedIn)

The limits of inference: What data scientists can learn from the reproducibility crisis in science - Clare Gollnick (Terbium Labs)

Understanding metadata - Michael Schrenk (Self-Employed)

Executive Briefing: Why machine-learned models crash and burn in production and what to do about it - David Talby (Pacific AI)

Human in the loop: A design pattern for managing teams working with machine learning - Paco Nathan (O’Reilly Media)

Architecting an open source enterprise data lake - Sagar Kewalramani (Meijer)

Humans versus the machines: Using human-based computation to improve machine learning - Veronica Mapes (Pinterest), Garner Chung (Pinterest)

Lessons on driving data science and analytics transformation - Chris Chapo (Gap Inc.)

Trapped by the present: Estimating long-term impact from A/B experiments - Brian Karfunkel (Pinterest)

Detecting retail fraud with data wrangling and machine learning - Matt Derda (Trifacta), Harrison Lynch (Consensus Corporation)

Executive Briefing: What does an exec need to know about architecture and why - Jesse Anderson (Big Data Institute)

Big data insights equal big money: Stories from the trenches at GoDaddy - Felix Gorodishter (GoDaddy)

Data-driven fuel management at Ryanair - Marcin Pilarczyk (Ryanair)

Law, ethics, and governance

Progressive data governance for emerging technologies - Anne Buff (SAS Institute)

The rise of big data governance: Insight on this emerging trend from active open source initiatives - John Mertic (The Linux Foundation), Maryna Strelchuk (ING)

The science of patchy data - Jennifer Prendki (Atlassian)

Achieving GDPR compliance and data privacy using blockchain technology - Ajay Mothukuri (Sapient), Dr. Vijay Srinivas Agneeswaran (SapientRazorfish)

Human in the loop: Bayesian rules enabling explainable AI - Pramit Choudhary (DataScience.com)

Data and ethics : Brainstorming Session - Natalie Evans Harris (BrightHive)

Visualization and user experience

Spark on Kubernetes: A case study from JD.com - Zhen Fan (JD.com), Wei Ting Chen (Intel)

Why nobody cares about your anomaly detection - Baron Schwartz (VividCortex)

Semi-automated analytic pipeline creation and validation using active learning - Sean Ma (Trifacta)

Personalization at scale: Mastering the challenges of personalization to create compelling user experiences - Rahim Daya (Pinterest)

Platform security cybersecurity

Distributed clinical models: Inference without sharing patient data - Balasubramanian Narasimhan (Stanford University), John-Mark Agosta (Microsoft), Philip Lavori (Stanford University)

Ask Me Anything

Ask Me Anything: Streaming architectures and applications (Kafka, Spark, Akka, and microservices) - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend)

Ask Me Anything: Big data and machine learning techniques to drive and grow business - Burcu Baran (LinkedIn), Wei Di (LinkedIn)

Ask Me Anything: Deep learning-based search and recommendation systems using TensorFlow - Dr. Vijay Srinivas Agneeswaran (SapientRazorfish), Abhishek Kumar (SapientRazorfish)

Ask Me Anything: Managing data science in the enterprise - Nick Elprin (Domino Data Lab)

Tutorials

Big data analytics and machine learning techniques to drive and grow business - Burcu Baran (LinkedIn), Wei Di (LinkedIn), Michael Li (LinkedIn), Chi-Yi Kuan (LinkedIn) - Part 1

Big data analytics and machine learning techniques to drive and grow business - Burcu Baran (LinkedIn), Wei Di (LinkedIn), Michael Li (LinkedIn), Chi-Yi Kuan (LinkedIn) - Part 2

Big data analytics and machine learning techniques to drive and grow business - Burcu Baran (LinkedIn), Wei Di (LinkedIn), Michael Li (LinkedIn), Chi-Yi Kuan (LinkedIn) - Part 3

Big data analytics and machine learning techniques to drive and grow business - Burcu Baran (LinkedIn), Wei Di (LinkedIn), Michael Li (LinkedIn), Chi-Yi Kuan (LinkedIn) - Part 4

Getting ready for GDPR: Securing and governing hybrid, cloud, and on-premises big data deployments - Mark Donsky (Cloudera), Andre Araujo (Cloudera), Syed Rafice (Cloudera), Mubashir Kazia (Cloudera) - Part 1

Getting ready for GDPR: Securing and governing hybrid, cloud, and on-premises big data deployments - Mark Donsky (Cloudera), Andre Araujo (Cloudera), Syed Rafice (Cloudera), Mubashir Kazia (Cloudera) - Part 2

Getting ready for GDPR: Securing and governing hybrid, cloud, and on-premises big data deployments - Mark Donsky (Cloudera), Andre Araujo (Cloudera), Syed Rafice (Cloudera), Mubashir Kazia (Cloudera) - Part 3

Getting ready for GDPR: Securing and governing hybrid, cloud, and on-premises big data deployments - Mark Donsky (Cloudera), Andre Araujo (Cloudera), Syed Rafice (Cloudera), Mubashir Kazia (Cloudera) - Part 4

Spark camp: Apache Spark 2.0 for analytics and text mining with Spark ML - Joseph Kambourakis (databricks) - Part 1

Spark camp: Apache Spark 2.0 for analytics and text mining with Spark ML - Joseph Kambourakis (databricks) - Part 2

Spark camp: Apache Spark 2.0 for analytics and text mining with Spark ML - Joseph Kambourakis (databricks) - Part 3

Spark camp: Apache Spark 2.0 for analytics and text mining with Spark ML - Joseph Kambourakis (databricks) - Part 4

Learning PyTorch by building a recommender system - Mo Patel (Independent), Neejole Patel (Virginia Tech) - Part 1

Learning PyTorch by building a recommender system - Mo Patel (Independent), Neejole Patel (Virginia Tech) - Part 2

Learning PyTorch by building a recommender system - Mo Patel (Independent), Neejole Patel (Virginia Tech) - Part 3

Learning PyTorch by building a recommender system - Mo Patel (Independent), Neejole Patel (Virginia Tech) - Part 4

Managing data science in the enterprise - Nick Elprin (Domino Data Lab) - Part 1

Managing data science in the enterprise - Nick Elprin (Domino Data Lab) - Part 2

Managing data science in the enterprise - Nick Elprin (Domino Data Lab) - Part 3

Managing data science in the enterprise - Nick Elprin (Domino Data Lab) - Part 4

How to use Impala’s query plan and profile to fix performance issues - Juan Yu (Cloudera) - Part 1

How to use Impala’s query plan and profile to fix performance issues - Juan Yu (Cloudera) - Part 2

How to use Impala’s query plan and profile to fix performance issues - Juan Yu (Cloudera) - Part 3

How to use Impala’s query plan and profile to fix performance issues - Juan Yu (Cloudera) - Part 4

Modern real-time streaming architectures - Karthik Ramasamy (Streamlio), Sanjeev Kulkarni (Streamlio), Sijie Guo (Streamlio), Arun Kejariwal (MZ) - Part 1

Modern real-time streaming architectures - Karthik Ramasamy (Streamlio), Sanjeev Kulkarni (Streamlio), Sijie Guo (Streamlio), Arun Kejariwal (MZ) - Part 2

Modern real-time streaming architectures - Karthik Ramasamy (Streamlio), Sanjeev Kulkarni (Streamlio), Sijie Guo (Streamlio), Arun Kejariwal (MZ) - Part 3

Modern real-time streaming architectures - Karthik Ramasamy (Streamlio), Sanjeev Kulkarni (Streamlio), Sijie Guo (Streamlio), Arun Kejariwal (MZ) - Part 4

Using R and Python for scalable data science, machine learning, and AI - Mario Inchiosa, Vanja Paunic, Robert Horton, Debraj GuhaThakurta, Ali Zaidi, Tomas Singliar, and John-Mark Agosta (Microsoft) - Part 1

Using R and Python for scalable data science, machine learning, and AI - Mario Inchiosa, Vanja Paunic, Robert Horton, Debraj GuhaThakurta, Ali Zaidi, Tomas Singliar, John-Mark Agosta (Microsoft) - Part 2

Using R and Python for scalable data science, machine learning, and AI - Mario Inchiosa, Vanja Paunic, Robert Horton, Debraj GuhaThakurta, Ali Zaidi, Tomas Singliar, and John-Mark Agosta (Microsoft) - Part 3

Using R and Python for scalable data science, machine learning, and AI - Mario Inchiosa, Vanja Paunic, Robert Horton, Debraj GuhaThakurta, Ali Zaidi, Tomas Singliar, and John-Mark Agosta (Microsoft) - Part 4

Streaming applications as microservices using Kafka, Akka Streams, and Kafka Streams - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend) - Part 1

Streaming applications as microservices using Kafka, Akka Streams, and Kafka Streams - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend) - Part 2

Streaming applications as microservices using Kafka, Akka Streams, and Kafka Streams - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend) - Part 3

Streaming applications as microservices using Kafka, Akka Streams, and Kafka Streams - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend) - Part 4

Deep learning-based search and recommendation systems using TensorFlow - Abhishek Kumar (SapientRazorfish), Dr. Vijay Srinivas Agneeswaran (SapientRazorfish) - Part 1

Deep learning-based search and recommendation systems using TensorFlow - Abhishek Kumar (SapientRazorfish), Dr. Vijay Srinivas Agneeswaran (SapientRazorfish) - Part 2

Deep learning-based search and recommendation systems using TensorFlow - Abhishek Kumar (SapientRazorfish), Dr. Vijay Srinivas Agneeswaran (SapientRazorfish) - Part 3

Deep learning-based search and recommendation systems using TensorFlow - Abhishek Kumar (SapientRazorfish), Dr. Vijay Srinivas Agneeswaran (SapientRazorfish) - Part 4

Natural language understanding at scale with spaCy and Spark NLP - David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed) - Part 1

Natural language understanding at scale with spaCy and Spark NLP - David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed) - Part 2

Natural language understanding at scale with spaCy and Spark NLP - David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed) - Part 3

Natural language understanding at scale with spaCy and Spark NLP - David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed) - Part 4

Time series data: Architecture and use cases - Ted Malaska (Blizzard Entertainment) - Part 1

Time series data: Architecture and use cases - Ted Malaska (Blizzard Entertainment) - Part 2

Time series data: Architecture and use cases - Ted Malaska (Blizzard Entertainment) - Part 3

Time series data: Architecture and use cases - Ted Malaska (Blizzard Entertainment) - Part 4