Strata Data Conference - San Jose 2018

Table of contents

1. Keynotes
   1. Merging human and machine learning for everyday solutions - Li Fan (Pinterest)
   2. To a hammer, everything is a nail: Choosing the right tool for your business problems (sponsored by Microsoft) - Tobias Ternstrom (Microsoft)
   3. Privacy in the age of machine learning - Ben Lorica (O'Reilly Media)
   4. Crisis Text Line data usage and insights - Nancy Lublin and Bob Filbin (Crisis Text Line)
   5. Defining responsible data practices: A community-driven approach - Natalie Evans Harris (BrightHive)
   6. Operationalizing machine learning (sponsored by IBM) - Dinesh Nirmal (IBM)
   7. Data Science in the Cloud - Alex Smola (Amazon)
   8. Sprouted clams and stanky bean: When machine learning makes mistakes - Janelle Shane (aiweirdness.com)
   9. The case for a deliberate data strategy in today’s attention-deficit economy (sponsored by MapR) - Anoop Dawar (MapR Technologies)
   10. Differentiating via data science - Eric Colson (Stitch Fix)
   11. Automating decisions with data in the cloud - Amr Awadallah (Cloudera)
   12. What separates the clouds? (sponsored by Google Cloud) - William Vambenepe (Google)
   13. Inclusivity for the greater good - Ajey Gore (GO-JEK)
   14. Lessons in Google Search data - Seth Stephens-Davidowitz (Everybody Lies | NY Times)
2. Data science and machine learning
   1. Approaching the pricing problem at Lyft - Ashivni Shekhawat (Lyft)
   2. Graph analysis of 200,000 tweets from Russian Twitter trolls - Ryan Boyd (Neo4j)
   3. Small pieces, loosely joined: A skater's code - Rodney Mullen (Almost Skateboards)
   4. Lessons learned deploying machine learning and deep learning models in production at major tech companies - Harish Doddi (Datatron Technologies), Jerry Xu (Datatron Technologies)
   5. Spark NLP in action: Improving patient flow forecasting at Kaiser Permanente - David Talby (Pacific AI), Santosh Kulkarni (Kaiser Permanente)
   6. Deep credit risk ranking with LSTM - Kyle Grove (Teradata)
   7. Who are we? The largest-scale study of professional data scientists - Miryung Kim (UCLA), Muhammad Gulzar (UCLA)
   8. The current state of TensorFlow and where it's headed in 2018 - Rajat Monga (Google)
   9. Using deep learning to solve challenging problems - Jeff Dean (Google)
   10. Detecting time series anomalies at Uber scale with recurrent neural networks - Andrea Pasqua (Uber), Anny Chen (Uber)
   11. Data science at Slack - Josh Wills (Slack)
   12. Breaking up the block: Using heterogenous population modeling to drive growth - Daniel Lurie (Pinterest)
   13. Machine learning applications for the industrial internet - Joseph Richards (GE Digital)
   14. Explaining machine learning models - Evan Kriminger (ZestFinance)
   15. sparklyr, implyr, and more: dplyr interfaces to large-scale data - Ian Cook (Cloudera)
   16. Word embeddings under the hood: How neural networks learn from language - Patrick Harrison (S Global)
   17. Cataloging the visible universe through Bayesian inference at petascale in Julia - Keno Fischer (Julia Computing)
   18. Building career advisory tools for the tech sector using machine learning - Simon Hughes (Dice.com), Yuri Bykov (Dice.com)
   19. Enough data engineering for a data scientist; or, How I learned to stop worrying and love the data scientists - Stephen O'Sullivan (Data Whisperers)
   20. Fast and effective natural language understanding - Mike Conover (SkipFlag)
   21. Transforming a machine learning prototype to a deployable solution leveraging Spark in healthcare - Rachita Chandra (IBM Watson Health)
   22. Interpretable machine learning products - Mike Lee Williams (Cloudera Fast Forward Labs)
   23. Being smarter than dinosaurs: How NASA uses deep learning for planetary defense - Siddha Ganju (Deep Vision)
3. Data Case Studies
   1. Supply chain evolution from horseless buggies to driverless cars - Valentin Bercovici (Pencil Data Inc.)
   2. Automation and analytics enablement in life insurance - Divya Ramachandran (Captricity)
   3. Building a flu predictor model for improved patient care - Jennie Shin (Kaiser Permanente)
   4. From the presidential campaign trail to the enterprise: Building effective data-driven teams - Katie Malone (Civis Analytics)
   5. Your enterprise AI is only as good as your data. - Joe Dumoulin (Next IT)
   6. Smart agriculture: Blending IoT sensor data with visual analytics - Mike Prorock (mesur.io)
   7. Automating business insights through artificial intelligence - Wayde Fleener (General Mills)
   8. Using ML to improve UX and literacy for young poets - Ann Nguyen (Whole Whale)
   9. Working with the data of sports - Thomas Miller (Northwestern University)
4. Sponsored
   1. The changing role of the CDO: Three keys for success (sponsored by MapR) - Jim Scott (MapR Technologies)
   2. The four elements of modern analytics (sponsored by MicroStrategy) - Vijay Kotu (Oath)
   3. Speed up mission-critical analytics in the cloud (sponsored by Kyligence) - Billy Liu (Kyligence)
   4. Analytics in real time, the (Grey's) anatomy of event streaming (sponsored by MemSQL) - Adam Ahringer (Disney-ABC TV Digital Media)
   5. The Snowflake data warehouse: How Sharethrough analyzes petabytes of event data in a SQL database (sponsored by Snowflake) - Dave Abercrombie (Sharethrough)
   6. Building the bridge from big data to machine learning and artificial intelligence (sponsored by Google Cloud) - Ryan Lippert (Google Cloud)
   7. Harnessing the cloud to enable connected systems and self-service and accelerate business growth (sponsored by Talend) - Jeff Smits (RingCentral)
   8. Building machine learning systems for scale: Amazon insights and best practices (sponsored by Amazon Web Services) - Guy Ernest (Amazon Web Services)
   9. Focus on your business: Case studies on building data solutions that meet your needs (sponsored by Microsoft) - Tobias Ternstrom (Microsoft)
   10. Data at scale and speed: Real-world use cases (sponsored by MapR) - Ted Dunning (MapR Technologies)
   11. When tests cry wolf (sponsored by Pure Storage) - Ivan Jibaja (Pure Storage)
   12. Managing the intelligent data pipeline and the connected enterprise (sponsored by Hitachi Vantara) - Chuck Yarbrough (Hitachi Vantara)
   13. Journey to digital (sponsored by IBM) - Seth Dobrin, PhD (IBM)
   14. Architecting an edge-to-cloud data pipeline to unify multiple data sources and processing engines (sponsored by NetApp) - Santosh Rao (NetApp)
   15. Digital transformation demands faster, more productive data science (sponsored by DataScience.com) - Ian Swanson (DataScience.com)
   16. Get a farm-to-table view of your data: Track data lineage from source to analytics (sponsored by Syncsort) - Tendu Yogurtcu (Syncsort)
   17. Accelerating analytics and AI from the edge to the cloud (sponsored by Intel) - Kevin Huiskes (Intel), Radhika Rangarajan (Intel)
   18. On-device deep learning: Trends, technologies, and challenges (sponsored by TalkingData) - Andreas Pfadler (TalkingData)
5. Data engineering and architecture
   1. The state of Postgres - Umur Cubukcu (Citus Data)
   2. Building​ ​a​ ​flexible​ ​ML​ ​pipeline​ ​at​ ​a​ ​B2B​ ​AI​ ​start​up - Dorna Bandari (Jetlore)
   3. Accelerating development velocity of production ML systems with Docker - Kinnary Jangla (Pinterest)
   4. What's new in Hadoop 3.0 - Daniel Templeton (Cloudera), Andrew Wang (Cloudera)
   5. DataOps: An Agile methodology for data-driven organizations - Ellen Friedman (MapR Technologies)
   6. Better machine learning logistics with the rendezvous architecture - Ted Dunning (MapR Technologies)
   7. Taming deep learning - Evan Sparks (Determined AI)
   8. Metrics-driven tuning of Apache Spark at scale - Edwina Lu (LinkedIn), Ye Zhou (LinkedIn), Min Shen (LinkedIn)
   9. NoSQL no more: SQL on Druid with Apache Calcite - Gian Merlino (Imply)
   10. Operationalize deep learning: How to deploy and consume your LSTM networks for predictive maintenance scenarios - Francesca Lazzeri (Microsoft), Fidan Boylu Uz (Microsoft)
   11. TimescaleDB: Reengineering PostgreSQL as a time series database - Michael Freedman (TimescaleDB | Princeton)
   12. The secret sauce behind LinkedIn's self-managing Kafka clusters - Jiangjie Qin (LinkedIn)
   13. Building a contacts graph from activity data - Alexis Roos (Salesforce), Noah Burbank (Salesforce)
   14. Crafting data products for the augmented writing experience - Chris Harland (Textio)
   15. Classifying job execution using deep learning - Ash Munshi (Pepperdata)
   16. 20 Netflix-style principles and practices to get the most out of your data platform - Kurt Brown (Netflix)
6. Strata Business Summit
   1. Workplace culture in the age of algorithmic management: The information networks Uber drivers built - Alex Rosenblat (Data Society Research Institute )
   2. How to avoid pitfalls when reasoning with data - Derek Ruths (CAI)
   3. Executive Briefing: Artificial intelligence—The next digital frontier? - Michael Chui (McKinsey Global Institute)
   4. Executive Briefing: Machine learning—Why you need it, why it's hard, and what to do about it - Mike Olson (Cloudera)
   5. Executive Briefing: Legal best practices for making data work - Alysa Z. Hutnik (Kelley Drye Warren LLP), Crystal Skelton (Kelley Drye Warren LLP)
   6. Reinventing healthcare: Early detection of Alzheimer’s disease with deep learning - Ayin Vala (Foundation for Precision Medicine)
   7. Executive Briefing: BI on big data - Mark Madsen (Third Nature), Shant Hovsepian (Arcadia Data)
   8. Executive Briefing: The conversational AI revolution - Yishay Carmiel (IntelligentWire | Spoken Labs)
   9. Bladder cancer diagnosis using deep learning - Mauro Damo (Dell EMC), Wei Lin (Dell EMC)
7. Streaming systems and real-time applications
   1. Using machine learning to simplify Kafka operations - Shivnath Babu (Duke University | Unravel Data Systems), Dhruv Goel (Microsoft)
   2. Deploying and monitoring interactive machine learning applications with Clipper - Dan Crankshaw (UC Berkeley RISELab)
   3. Approximation data structures in streaming data processing - Debasish Ghosh (Lightbend )
   4. How to build leakproof stream processing pipelines with Apache Kafka and Apache Spark​ - Jordan Hambleton (Cloudera), Guru Medasani (Domino Data Lab)
   5. Stream storage with Apache BookKeeper - Sijie Guo (Streamlio)
   6. Streaming SQL to unify batch and stream processing: Theory and practice with Apache Flink at Uber - Fabian Hueske (data Artisans), Shuyi Chen (Uber)
   7. Foundations of streaming SQL; or, How I learned to love stream and table theory - Tyler Akidau (Google)
   8. Kafka streaming applications with Akka Streams and Kafka Streams - Dean Wampler (Lightbend)
   9. Effectively once, exactly once, and more in Heron - Karthik Ramasamy (Streamlio), Sanjeev Kulkarni (Streamlio)
   10. Machine-learned model quality monitoring in fast data and streaming applications - Emre Velipasaoglu (Lightbend)
   11. Playing well together: Big data beyond the JVM with Spark and friends - Holden Karau (Google), Rachel Warren (Salesforce Einstein)
   12. The real-time journey from raw streaming data to AI-based analytics - Roy Ben-Alta (Amazon Web Services), Ira Cohen (Anodot)
   13. Unified and elastic batch and stream processing with Pravega and Apache Flink - Fabian Hueske (data Artisans), Flavio Junqueira (Dell EMC)
   14. HDFS on Kubernetes: Tech deep dive on locality and security - Kimoon Kim (Pepperdata), Ilan Filonenko (Bloomberg LP)
   15. Effectively once in Apache Pulsar, the next-generation messaging system - Matteo Merli (Streamlio)
8. Media and Ad Tech
   1. The data hero’s journey - Amanda Gerdes (Blizzard Entertainment)
   2. The golden age of data and analytics - David Boyle (MasterClass)
   3. Improving the customer experience via clickthru analytics - Sridhar Alla (Comcast)
   4. Big data applicability to the gaming industry - Rizwan Patel (Caesars Entertainment)
   5. Cohort analysis at scale - Blake Irvine (Netflix)
   6. Show me the money: Understanding causality for ad attribution - April Chen (Civis Analytics)
   7. Marketing at future speed - Kevin Lyons (Nielsen Marketing Cloud)
   8. Data science in practice: Examining events in social media - Jennifer Webb (SuprFanz)
   9. What is the relationship between social influence and the NBA? - Noah Gift (UC Davis)
9. Big data and data science in the cloud
   1. Cloud, multicloud, and the data refinery - Tom Fisher (MapR Technologies)
   2. How does a big data professional get started with AI? - Wee Hyong Tok (Microsoft), Danielle Dean (Microsoft)
   3. Powering robotics clouds with Alluxio - Bin Fan (Alluxio), Shaoshan Liu (PerceptIn)
   4. Machine learning versus machine learning in production - Manu Mukerji (8x8)
   5. Streaming big data in the cloud: What to consider and why - William Chambers (Databricks), Michael Armbrust (Databricks)
   6. Accelerating deep learning on Apache Spark using BigDL with coarse-grained scheduling - Shivaram Venkataraman (Microsoft Research), Sergey Ermolin (Intel)
   7. Big data, big problems: Predicting climate change - Ari Gesher (Kairos Aerospace)
   8. Radically modular data ingestion APIs in Apache Beam - Eugene Kirpichov (Google)
   9. Machine learning to tackle industrial data fusion - Alexandra Gunderson (Arundo Analytics)
   10. Vectorized query processing using Apache Arrow - Siddharth Teotia (Dremio)
   11. Moving the needle of the pin: Streaming hundreds of terabytes of pins from MySQL to S3/Hadoop continuously - Henry Cai (Pinterest), Yi Yin (Pinterest)
   12. Best practices for productionizing Apache Spark MLlib models - Joseph Bradley (Databricks)
   13. Code Property Graph: A modern, queryable data storage for source code - Vlad A Ionescu (ShiftLeft), Fabian Yamaguchi (ShiftLeft)
   14. 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)
   15. Pipeline testing with Great Expectations - Abe Gong (Superconductive Health), James Campbell (USG)
   16. Analytics in the cloud: Building a modern cloud-based big data warehouse - Greg Rahn (Cloudera)
   17. Distributed deep learning with containers on heterogeneous GPU clusters - dong meng (MapR)
   18. Hive as a service - Szehon Ho (Criteo), Pawel Szostek (Criteo)
   19. Magellan: Scalable and fast geospatial analytics - Ram Sriharsha (Databricks)
   20. Continuous delivery for NLP on Kubernetes: Lessons learned - Michelle Casbon (Google Cloud Platform Developer Relations)
   21. Data reflections: Making data fast and easy to use without making copies - Tomer Shiran (Dremio), Jacques Nadeau (Dremio)
   22. Building ML and AI pipelines with Spark and TensorFlow - Chris Fregly (PipelineAI)
   23. Lyft's analytics pipeline: From Redshift to Apache Hive and Presto - Shenghu Yang (Lyft)
   24. Not your parents' machine learning: How to ship an XGBoost churn prediction app in under four weeks - Goodman Gu (Atlassian)
   25. Cuttlefish: Lightweight primitives for online tuning - Tomer Kaftan (University of Washington)
10. Data-driven business management
    1. Managing data science at scale - Matthew Granade (Domino Data Lab)
    2. Executive Briefing: Building effective heterogeneous data communities—Driving organizational outcomes with broad-based data science - Frances Haugen (Pinterest), Patrick Phelps (Pinterest)
    3. Executive Briefing: Managing successful data projects—Technology selection and team building - Ted Malaska (Blizzard Entertainment), Jonathan Seidman (Cloudera)
    4. 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)
    5. The limits of inference: What data scientists can learn from the reproducibility crisis in science - Clare Gollnick (Terbium Labs)
    6. Understanding metadata - Michael Schrenk (Self-Employed)
    7. Executive Briefing: Why machine-learned models crash and burn in production and what to do about it - David Talby (Pacific AI)
    8. Human in the loop: A design pattern for managing teams working with machine learning - Paco Nathan (O'Reilly Media)
    9. Architecting an open source enterprise data lake - Sagar Kewalramani (Meijer)
    10. Humans versus the machines: Using human-based computation to improve machine learning - Veronica Mapes (Pinterest), Garner Chung (Pinterest)
    11. Lessons on driving data science and analytics transformation - Chris Chapo (Gap Inc.)
    12. Trapped by the present: Estimating long-term impact from A/B experiments - Brian Karfunkel (Pinterest)
    13. Detecting retail fraud with data wrangling and machine learning - Matt Derda (Trifacta), Harrison Lynch (Consensus Corporation)
    14. Executive Briefing: What does an exec need to know about architecture and why - Jesse Anderson (Big Data Institute)
    15. Big data insights equal big money: Stories from the trenches at GoDaddy - Felix Gorodishter (GoDaddy)
    16. Data-driven fuel management at Ryanair - Marcin Pilarczyk (Ryanair)
11. Law, ethics, and governance
    1. Progressive data governance for emerging technologies - Anne Buff (SAS Institute)
    2. The rise of big data governance: Insight on this emerging trend from active open source initiatives - John Mertic (The Linux Foundation), Maryna Strelchuk (ING)
    3. The science of patchy data - Jennifer Prendki (Atlassian)
    4. Achieving GDPR compliance and data privacy using blockchain technology - Ajay Mothukuri (Sapient), Dr. Vijay Srinivas Agneeswaran (SapientRazorfish)
    5. Human in the loop: Bayesian rules enabling explainable AI - Pramit Choudhary (DataScience.com)
    6. Data and ethics : Brainstorming Session - Natalie Evans Harris (BrightHive)
12. Visualization and user experience
    1. Spark on Kubernetes: A case study from JD.com - Zhen Fan (JD.com), Wei Ting Chen (Intel)
    2. Why nobody cares about your anomaly detection - Baron Schwartz (VividCortex)
    3. Semi-automated analytic pipeline creation and validation using active learning - Sean Ma (Trifacta)
    4. Personalization at scale: Mastering the challenges of personalization to create compelling user experiences - Rahim Daya (Pinterest)
13. Platform security cybersecurity
    1. Distributed clinical models: Inference without sharing patient data - Balasubramanian Narasimhan (Stanford University), John-Mark Agosta (Microsoft), Philip Lavori (Stanford University)
14. Ask Me Anything
    1. Ask Me Anything: Streaming architectures and applications (Kafka, Spark, Akka, and microservices) - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend)
    2. Ask Me Anything: Big data and machine learning techniques to drive and grow business - Burcu Baran (LinkedIn), Wei Di (LinkedIn)
    3. Ask Me Anything: Deep learning-based search and recommendation systems using TensorFlow - Dr. Vijay Srinivas Agneeswaran (SapientRazorfish), Abhishek Kumar (SapientRazorfish)
    4. Ask Me Anything: Managing data science in the enterprise - Nick Elprin (Domino Data Lab)
15. Tutorials
    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 1
    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 2
    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 3
    4. 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
    5. 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
    6. 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
    7. 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
    8. 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
    9. Spark camp: Apache Spark 2.0 for analytics and text mining with Spark ML - Joseph Kambourakis (databricks) - Part 1
    10. Spark camp: Apache Spark 2.0 for analytics and text mining with Spark ML - Joseph Kambourakis (databricks) - Part 2
    11. Spark camp: Apache Spark 2.0 for analytics and text mining with Spark ML - Joseph Kambourakis (databricks) - Part 3
    12. Spark camp: Apache Spark 2.0 for analytics and text mining with Spark ML - Joseph Kambourakis (databricks) - Part 4
    13. Learning PyTorch by building a recommender system - Mo Patel (Independent), Neejole Patel (Virginia Tech) - Part 1
    14. Learning PyTorch by building a recommender system - Mo Patel (Independent), Neejole Patel (Virginia Tech) - Part 2
    15. Learning PyTorch by building a recommender system - Mo Patel (Independent), Neejole Patel (Virginia Tech) - Part 3
    16. Learning PyTorch by building a recommender system - Mo Patel (Independent), Neejole Patel (Virginia Tech) - Part 4
    17. Managing data science in the enterprise - Nick Elprin (Domino Data Lab) - Part 1
    18. Managing data science in the enterprise - Nick Elprin (Domino Data Lab) - Part 2
    19. Managing data science in the enterprise - Nick Elprin (Domino Data Lab) - Part 3
    20. Managing data science in the enterprise - Nick Elprin (Domino Data Lab) - Part 4
    21. How to use Impala's query plan and profile to fix performance issues - Juan Yu (Cloudera) - Part 1
    22. How to use Impala's query plan and profile to fix performance issues - Juan Yu (Cloudera) - Part 2
    23. How to use Impala's query plan and profile to fix performance issues - Juan Yu (Cloudera) - Part 3
    24. How to use Impala's query plan and profile to fix performance issues - Juan Yu (Cloudera) - Part 4
    25. Modern real-time streaming architectures - Karthik Ramasamy (Streamlio), Sanjeev Kulkarni (Streamlio), Sijie Guo (Streamlio), Arun Kejariwal (MZ) - Part 1
    26. Modern real-time streaming architectures - Karthik Ramasamy (Streamlio), Sanjeev Kulkarni (Streamlio), Sijie Guo (Streamlio), Arun Kejariwal (MZ) - Part 2
    27. Modern real-time streaming architectures - Karthik Ramasamy (Streamlio), Sanjeev Kulkarni (Streamlio), Sijie Guo (Streamlio), Arun Kejariwal (MZ) - Part 3
    28. Modern real-time streaming architectures - Karthik Ramasamy (Streamlio), Sanjeev Kulkarni (Streamlio), Sijie Guo (Streamlio), Arun Kejariwal (MZ) - Part 4
    29. 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
    30. 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
    31. 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
    32. 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
    33. Streaming applications as microservices using Kafka, Akka Streams, and Kafka Streams - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend) - Part 1
    34. Streaming applications as microservices using Kafka, Akka Streams, and Kafka Streams - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend) - Part 2
    35. Streaming applications as microservices using Kafka, Akka Streams, and Kafka Streams - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend) - Part 3
    36. Streaming applications as microservices using Kafka, Akka Streams, and Kafka Streams - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend) - Part 4
    37. Deep learning-based search and recommendation systems using TensorFlow - Abhishek Kumar (SapientRazorfish), Dr. Vijay Srinivas Agneeswaran (SapientRazorfish) - Part 1
    38. Deep learning-based search and recommendation systems using TensorFlow - Abhishek Kumar (SapientRazorfish), Dr. Vijay Srinivas Agneeswaran (SapientRazorfish) - Part 2
    39. Deep learning-based search and recommendation systems using TensorFlow - Abhishek Kumar (SapientRazorfish), Dr. Vijay Srinivas Agneeswaran (SapientRazorfish) - Part 3
    40. Deep learning-based search and recommendation systems using TensorFlow - Abhishek Kumar (SapientRazorfish), Dr. Vijay Srinivas Agneeswaran (SapientRazorfish) - Part 4
    41. Natural language understanding at scale with spaCy and Spark NLP - David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed) - Part 1
    42. Natural language understanding at scale with spaCy and Spark NLP - David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed) - Part 2
    43. Natural language understanding at scale with spaCy and Spark NLP - David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed) - Part 3
    44. Natural language understanding at scale with spaCy and Spark NLP - David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed) - Part 4
    45. Time series data: Architecture and use cases - Ted Malaska (Blizzard Entertainment) - Part 1
    46. Time series data: Architecture and use cases - Ted Malaska (Blizzard Entertainment) - Part 2
    47. Time series data: Architecture and use cases - Ted Malaska (Blizzard Entertainment) - Part 3
    48. Time series data: Architecture and use cases - Ted Malaska (Blizzard Entertainment) - Part 4