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Strata Data Conference 2019 - San Francisco, California

Video Description

Thousands of the data scientists, analysts, engineers, developers, and executives converged at the Strata Data Conference San Francisco in March 2019 to absorb the insights and wisdom of the data world's best minds. The conference featured more than 300 speakers, 10 keynotes, 10 tutorials, and 150+ technical sessions. This video compilation captures the best from the conference, offering more than 100 hours of material to review at your own pace. Highlights include:

  • The Strata Business Summit - speakers, executive briefings, and tech sessions laser focused on a central theme: How do the world’s leading companies build their successful data strategies? Learn about recommendation engines, AI-based personalization solutions, data governance, ML based customer insight harvesting, and more from data wizards like Zachery Anderson (Electronic Arts), Eric Bradlow (The Wharton School), David Talby (Pacific AI), Paco Nathan (derwen.ai), Jonathan Francis (Starbucks), and JoLynn Lavin (General Mills).
  • The Strata Data Ethics Summit – Is your AI really making good decisions or have you built a deceptive black box that reinforces ugly stereotypes? Alistair Croll (Strata Chair), Tim O'Reilly (O'Reilly Media), and Susan Etlinger's (Altimeter Group) eight hour deep dive into the thorny issues of data and algorithms with help from Jana Eggers (Nara Logics), Rumman Chowdhury (Accenture), Kathy Baxter (Salesforce), Carole Piovesan (McCarthy Tétrault), and more.
  • Hours of tutorials from the world's top data engineers, such as Francesca Lazzeri (Microsoft) and Holden Karau (Google) on training and deploying models with Kubeflow across different cloud vendors; Dean Wampler (Lightbend) on performing machine learning using Kafka-based streaming pipelines; and Jason Dai (Intel) on the Analytics Zoo, an analytics/AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline.
  • Sessions devoted to Data Science, Machine Learning & AI, including Sharad Goel (Stanford University) on the challenges of "fair machine learning", which aims to ensure that decisions guided by algorithms are equitable; Kelley Rivoire (Stripe) on scaling machine learning using the Railyard API; Vinod Vaikuntanathan (MIT) on performing machine learning on encrypted data; and Jeremy Howard (platform.ai) on recent advances in deep learning that allow non-engineers to train neural networks from scratch without needing code or pre-existing labels.
  • Sessions focused on Data Engineering & Architecture, including Karthik Ramasamy (Streamlio) on reducing stream processing complexity using Apache Pulsar Functions; Rachel Warren's (Salesforce Einstein) review of Spark tuning; and Tobias Knaup (Mesosphere) on the critical learning that must take place before and after you've trained and modeled your deep learning models.
  • Multiple sessions on Streaming and IoT, Business Analytics, UX and Visualization, plus keynotes from AI/cryptography expert Shafi Goldwasser (UC Berkeley) and from "Likewar: The Weaponization of Social Media" co-author Peter Singer.
  • The Future of the Firm, A six-session mini-conference within Strata that highlights how leading edge tech companies adapt to the workforce, business, and economic trends shaping the future of business. Led by Josh Bersin (Founder, Bersin by Deloitte) and executives from Capital One, Bloomberg Beta, Genentech, Publicis Sapient, and more.

Table of Contents

  1. Keynotes
    1. The journey to the data-driven enterprise from the Edge to AI - Amy O'Connor (Cloudera) 00:14:24
    2. Scoring your business in the AI Matrix (sponsored by Dataiku) - Jed Dougherty (Dataiku) 00:05:26
    3. Sustaining machine learning in the enterprise - Ben Lorica (O'Reilly Media) 00:12:51
    4. Cyberconflict: A new era of war, sabotage, and fear - David Sanger (The New York Times | Author, The Perfect Weapon: War, Sabotage, and Fear in the Cyber Age) 00:28:04
    5. Streamlining your Data Assets: A Strategy for the Journey to AI (sponsored by IBM) - Dinesh Nirmal (IBM) 00:05:02
    6. AI and cryptography: Challenges and opportunities - Shafi Goldwasser (UC Berkeley | MIT | Weizmann Institute of Science | Duality) 00:22:19
    7. Hacking the vote: The neuropolitical universe - Elizabeth Svoboda (What Makes a Hero?) 00:08:16
    8. Data warehousing is not a use case (sponsored by Google Cloud) - Jordan Tigani (Google ) 00:09:18
    9. Chatting with machines: Strange things 60 billion bot logs say about human nature - Lauren Kunze (Pandorabots) 00:13:26
    10. The Enterprise Data Cloud - Mike Olson (Cloudera) 00:09:45
    11. Forecasting uncertainty at Airbnb - Theresa Johnson (Airbnb) 00:10:22
    12. The cyberthreat-scape: Key trends in cybersecurity - Peter Singer (New America) 00:20:05
    13. Strata Data Awards: Winners Announced 00:02:38
  2. Data Science, Machine Learning & AI
    1. Ludwig, a code-free deep learning toolbox - Piero Molino (Uber AI) 00:36:08
    2. Natural language understanding in task-oriented conversational AI - Sonal Gupta (Facebook) 00:41:13
    3. Applying deep learning at Google for recommendations - Ron Bodkin (Google) 00:48:29
    4. The measure and mismeasure of fairness in machine learning - Sharad Goel (Stanford University) 00:51:39
    5. New models for generating training data for AI - Roger Chen (Computable) 00:39:11
    6. From an archived data field to GO-JEK’s world-class product feature for customer experience - Divya Choudhary (GO-JEK) 00:36:15
    7. Talking to the machines: Monitoring production machine learning systems - Ting-Fang Yen (DataVisor) 00:27:30
    8. Online evaluation of machine learning models - Ted Dunning (MapR) 00:40:31
    9. Artificial intelligence on human behavior: New insights into customer segmentation - Melinda Han Williams (Dstillery) 00:34:41
    10. Machine learning prediction of blood alcohol content: A digital signature of behavior - Kirstin Aschbacher (UCSF Cardiology) 00:47:16
    11. NLP from scratch: Solving the cold start problem for natural language processing - Michael Johnson (Lockheed Martin), Norris Heintzelman (Lockheed Martin) 00:43:17
    12. Building high-performance text classifiers on a limited labeling budget - Mario Inchiosa (Microsoft), Robert Horton (Microsoft), Ali Zaidi (Microsoft) 00:44:23
    13. Masquerading Malicious DNS Traffic - David Rodriguez (Cisco Systems) 00:37:59
    14. On a Deep Journey Towards Five Nines - Aashish Sheshadri (PayPal Inc) 00:40:39
    15. The magic behind your Lyft ride prices - a case study of Machine Learning and Streaming - Rakesh Kumar (Lyft Inc), Thomas Weise (Lyft) 00:44:32
    16. Efficient Multi-armed Bandit with Thompson Sampling for applications with Delayed feedback - Shradha Agrawal (Adobe Systems Inc) 00:42:18
    17. The future of machine learning is decentralized - Alex Ingerman (Google) 00:40:01
    18. Federated learning - Mike Lee Williams (Cloudera Fast Forward Labs) 00:52:12
    19. Anomaly detection using deep learning to measure quality of Large Datasets​ - Sridhar Alla (Comcast), Syed Nasar (Cloudera) 00:40:59
    20. Our New Publishing Platform Will Make You A Better Writer: Using AI To Assist The Newsroom - Boris Yakubchik (Forbes) 00:38:29
    21. Applied Machine Learning In Finance - Chakri Cherukuri (Bloomberg LP) 00:34:11
    22. Nutrition Data Science - Noah Gift (UC Davis ), Michelle Davenport (Quantitative Nutrition) 00:38:42
    23. Deploying Data Science for National Economic Statistics - Jeff Chen (US Bureau of Economic Analysis) 00:39:55
    24. Cloud-Native Machine Learning: Emerging Trends and the Road Ahead - Tristan Zajonc (Cloudera), Tim Chen (Cloudera) 00:40:16
    25. Testing ad content with survey experiments. - Patrick Miller (Civis Analytics) 00:29:49
    26. Using Deep Learning to automatically rank millions of hotel images - Christopher Lennan (idealo.de) 00:35:22
    27. Personalizing the guest-booking experience at Airbnb - Kapil Gupta (Airbnb) 00:40:32
    28. Real Time Analytics on Deep Learning: when Tensorflow meets Presto at Uber - Zhenxiao Luo (Uber)
    29. Interpretable and Resilient AI for Financial Services - Jari Koister (FICO)
  3. Sponsored
    1. Managing globally distributed data for deep learning using TensorFlow on YARN (sponsored by WANdisco) - Jagane Sundar (WANdisco)
    2. High-performance data lakes for AI workloads using object storage (sponsored by MinIO) - Scott Mcclellan (PRGX)
    3. Augmented OLAP for big data from on-premises to multicloud (sponsored by Kyligence) - Yang Li (Kyligence)
    4. How to compete in the AI arms race (sponsored by Oracle Cloud Infrastructure) - Ian Swanson (Oracle)
    5. The death of coding: How AI redefines our relationship with computers (sponsored by IBM) - Sam Lightstone (IBM)
    6. From data to discovery: The power of choice and control (sponsored by SAS) - Sarah Gates (SAS)
    7. Intelligent design patterns for cloud-based analytics and BI (sponsored by Arcadia Data) - Priyank Patel (Arcadia Data)
    8. Transforming AI, ML, and BI on big data at Verizon (sponsored by Kyvos Insights) - Syed Latheef (Verizon)
    9. The new frontier: Marsh’s data voyage into the public cloud (sponsored by Impetus) - Stephen Dantu (Marsh)
    10. Uncovering the next generation of data architecture for insights at the speed of thought (sponsored by Actian) - Raghu Chakravarthi (Actian)
    11. Walmart's journey from business intelligence to artificial intelligence (sponsored by Walmart Labs) - Prakhar Mehrotra (Walmart Labs)
    12. Rethinking big data analytics with Google Cloud (sponsored by Google Cloud) - Jordan Tigani (Google)
    13. Break through the limits of your current database (sponsored by MemSQL) - Franck Leveneur (WAG Walking)
    14. Solving the enterprise data dilemma (sponsored by erwin, Inc.) - Adam Famularo (erwin, Inc.)
    15. Strategies for leveraging legacy data for real time, cloud, and cluster (sponsored by Syncsort) - Ashwin Ramachandran (Syncsort)
    16. Go serverless with Elasticsearch: Eliminate scaling and performance bottlenecks for faster data search (sponsored by Vizion.ai) - Geoff Tudor (Vizion.ai)
  4. Data Engineering & Architecture
    1. Cloud programming simplified: A Berkeley view on serverless computing - Eric Jonas (UC Berkeley)
    2. MLflow: An open platform to simplify the machine learning lifecycle - Corey Zumar (Databricks)
    3. Automation of root cause analysis for big data stack applications - Alkis Simitsis (Micro Focus), Shivnath Babu (Unravel Data Systems | Duke University)
    4. How Intuit reduced time to reliable insights for data pipelines - Sandeep U (Intuit)
    5. ROCKSET: The design and implementation of a data system for low-latency queries for search and analytics - Igor Canadi (Rockset), Dhruba Borthakur (Rockset)
    6. How Netflix measures app performance on 250 million unique devices across 190 countries - Vivek Pasari (Netflix)
    7. Adaptive ETL to optimize query performance at Lyft - James Taylor (Lyft)
    8. Accelerating analytical antelopes: Integrating Apache Kudu's RPC into Apache Impala - Lars Volker (Cloudera), Michael Ho (Cloudera)
    9. When SQL users run wild: Resource management features and techniques to tame Apache Impala - Tim Armstrong (Cloudera)
    10. Cloud-native data pipelines with Apache Kafka - Gwen Shapira (Confluent)
    11. Serverless workflows for orchestration hybrid cluster-based and serverless processing - Rustem Feyzkhanov (Instrumental)
    12. ML and AI at scale at PayPal - Subhadra Tatavarti (PayPal), Chen Kovacs (PayPal)
    13. Taking graph applications to production - Denise Gosnell (DataStax)
    14. Bullet: Querying streaming data in transit with sketches - Akshai Sarma (Oath), Nathan Speidel (Yahoo)
    15. Clusters in Kubernetes on a cluster: Building a multitenant environment for the field - Paul Curtis (MapR Technologies)
    16. Managing Uber's Data Workflows at Scale - Alex Kira (Uber)
    17. Presto: Tuning Performance of SQL-on-Anything Analytics - Kamil Bajda-Pawlikowski (Starburst), Martin Traverso (Facebook)
    18. Real-time monitoring of Twitter network infrastructure with Heron - Julien Delange (Twitter), Neng Lu (Twitter)
    19. Persistent Storage for Machine Learning in KubeFlow - Skyler Thomas (MapR), Terry He (MapR Technologies)
    20. Live-Aggregators: A Scalable, Cost Effective and Reliable Way of Aggregating Billions of Messages in Realtime - Osman Sarood (Mist Systems), Chunky Gupta (Mist Systems)
    21. From flat files to deconstructed database: The evolution and future of the big data ecosystem - Julien Le Dem (WeWork)
    22. Building Rakuten Analytics: A Story of Evolutions - Juan Paulo Gutierrez (Rakuten)
    23. Transforming behavioural analytics at Atlassian - Rohan Dhupelia (Atlassian), Jimmy Li (Atlassian)
    24. Taming large-state to join datasets for Personalization - Sonali Sharma (Netflix), Shriya Arora (Netflix)
    25. Spark Adaptive Execution Unleash the Power of Spark SQL - Haifeng Chen (Intel)
    26. New Directions in Record Linkage - Yves Thibaudeau (U.S. Census Bureau)
    27. Netflix - The journey towards a self-service data platform - Kurt Brown (Netflix)
    28. How to Protect Big Data in a Containerized Environment - Thomas Phelan (BlueData)
    29. Data Science in Deutsche Telekom - Predicting global travel patterns and network demand - Václav Surovec (Deutsche Telekom IT), Gabor Kotalik (Deutsche Telekom AG)
    30. Optimizing Computing Clusters Resource Utilization with In-Memory Distributed File System - Shouwei Chen (Rutgers University), Yue Li (MemVerge)
    31. Put Kafka in jail with Strimzi - Sean Glover (Lightbend)
    32. Disrupting Data Discovery - Mark Grover (Lyft), Tao Feng (Lyft)
    33. Scaling Apache Spark on Kubernetes at Lyft - Li Gao (Lyft Inc.), Bill Graham (Lyft Inc.)
    34. Faster ML over Joins of Tables - Arun Kumar (University of California, San Diego)
    35. Scanner: Efficient Video Analysis at Scale - Alex Poms (Stanford University), Will Crichton (Stanford University)
    36. Automating DevOps for Machine Learning - Diego Oppenheimer (Algorithmia)
    37. Database migrations don't have to be painful, but the road will be bumpy - Adrian Lungu (Adobe), Serban Teodorescu (Adobe)
    38. Cost Effective Presto on AWS with Spot Nodes - Shubham Tagra (Qubole)
    39. Cruise Control: Effortless Management of Kafka Clusters - Adem Efe Gencer (LinkedIn)
    40. Enabling Insights and Analytics with Data Streaming Architectures and Pipelines using Kafka and Hadoop - Mohammad Quraishi (Cigna)
    41. Real Time Analytics at Uber: bring SQL into everything - Zhenxiao Luo (Uber)
    42. Data processing at the speed of 100 Gbps using Apache Crail - Patrick Stuedi (IBM Research)
  5. Strata Data Ethics Summit
    1. Getting real about ethical technology - Susan Etlinger (Altimeter Group)
    2. The human side of data and technology - Bradley Voytek (UC San Diego)
    3. AI's terrible twos: When AI does what we taught it - Jana Eggers (Nara Logics)
    4. Say what? The ethical challenges of designing for humanlike interaction - Jonathan Foster (Microsoft)
    5. Is your AI making good decisions? - Brian Rieger (Labelbox)
    6. Panel: Causes - Bradley Voytek (UC San Diego), Jana Eggers (Nara Logics), Jonathan Foster (Microsoft), Brian Rieger (Labelbox)
    7. On the Accountability of Black Boxes: How we can control what we can’t exactly measure. - Yiannis Kanellopoulos (Code4Thought)
    8. The future of data ethics - Alistair Croll (Solve For Interesting), Susan Etlinger (Altimeter Group), Tim O'Reilly (O'Reilly Media)
  6. Future of the Firm
    1. The future of the firm: Starting now - Josh Bersin (Bersin by Deloitte)
    2. A human-centered approach to AI and machine learning - Cathryn Posey (Capital One)
    3. Automating yourself out of a job? The problem with knowledge work - James Cham (Bloomberg Beta)
    4. The brave new world of computational propaganda - Renee DiResta (New Knowledge)
    5. The conscience of a company - Tim O'Reilly (O'Reilly Media), Janet Haven (Data & Society), Catherine Bracy (TechEquity Collaborative)
  7. Law and Ethics
    1. Owning ethics: Doing ethics inside a tech company - Jake Metcalf (Ethical Resolve), Emanuel Moss (Data & Society)
    2. Community and regional data sharing policy frameworks: Frontier stories - Mei Fung (Customer Think)
  8. Strata Business Summit
    1. Executive Briefing: From the edge to AI—Taking control of your data for fun and profit - Mike Olson (Cloudera)
    2. Recommendation engines and mobile gaming - Bysshe Easton (KIXEYE), Thomas Dobbs (KIXEYE)
    3. Scaling visualization for big data and analytics in the cloud - Jaipaul Agonus (FINRA), Daniel Monteiro (FINRA)
    4. Shortcuts that short-circuit talent pipelines: Data-driven optimization of hiring - Maryam Jahanshahi (TapRecruit)
    5. The ethics of analytics - Bill Franks (International Institute For Analytics)
    6. Yay, we are going to deploy an AI/ML powered app. But wait! Where do I deploy? - Swatee Singh (American Express)
    7. Understanding the data universe with a data catalog - John Haddad (Informatica)
    8. What the reproducibility problem means for your business - Stuart Buck (Laura and John Arnold Foundation)
    9. Apache Superset: An open source data visualization platform - Maxime Beauchemin (Lyft)
    10. An alternative approach to adding data science to an organization: Use Jupyter and start with the domain experts - Dave Stuart (Department of Defense )
  9. Business Analytics and Visualization
    1. How Walgreens transformed supply chain management with Kyvos, Tableau, and big data - Neerav Jain (Walgreens), Anne Cruz (Walgreens)
    2. Understanding the world food economy with satellite images and AI - Alex Kudriashova (Astro Digital)
    3. When Self-Service BI meets Geospatial Analysis, - kyungtaak Noh (SK Telecom)
    4. The Paradise Papers and West Africa Leaks: Behind the scenes with the ICIJ - Pierre Romera (International Consortium of Investigative Journalists (ICIJ))
  10. Case studies
    1. Voice of the Customer; a Case Study in how Machine Learning can Automate Consumer Insights - JoLynn Lavin (General Mills, Inc)
    2. Informing the Art of Business with Data and Science - Craig Rowley (Columbia Sportswear)
    3. Sharing Cancer Genomic Data from Clinical Sequencing Using Blockchain - Benjamin Glicksberg (UCSF)
    4. Leveraging fashion data to make shopping recommendations - Rhonda Textor (True Fit)
  11. Executive Briefing and best practices
    1. Executive Briefing: Why machine-learned models crash and burn in production and what to do about it - David Talby (Pacific AI)
    2. Executive Briefing: What it takes to use machine learning in fast data pipelines - Dean Wampler (Lightbend)
    3. From Data Driven to Data Competitive - June Andrews (GE)
    4. How to extract stories from your data and tell them visually? It Can Be Done. We Will Show You How. - Ambal Balakrishnan (IBM)
    5. Organic Intelligence: Telling a story about the Human Experience with Math - Robin Way (Corios)
  12. Streaming and IoT
    1. Critical Turbine Maintenance: Monitoring & Diagnosing Planes and Power Plants in Real Time - June Andrews (GE), John Rutherford (GE)
    2. Flink SQL in Action - Fabian Hueske (Ververica)
    3. Serverless for data and AI - Avner Braverman (Binaris)
    4. Apache Druid auto scale-out/in for streaming data ingestion on Kubernetes - Jinchul Kim (SK Telecom)
  13. Culture and organization
    1. Data Science University: Transforming a Fortune 5 workforce - Marc Paradis (UnitedHealth Group)
    2. Scaling data infrastructure in the fashion world; or, “What is this? Business intelligence for ants?” - Francesco Mucio (Zalando)
    3. Creating a data engineering culture at USAA - Jesse Anderson (Big Data Institute), Thomas Goolsby (USAA)
  14. Visualization and UX
    1. Bringing data to life: Combining machine learning and art to tell a data story - Nancy Rausch (SAS Institute)
  15. Jupyter
    1. From Jupyter to production: Accelerating solutions to business problems in production - Manu Mukerji (8x8)
    2. Talking with Jupyter - M Pacer (Netflix)
    3. Where does Jupyter fit into building end-to-end ML products? - Omoju Miller (GitHub)
    4. Scaling Jupyter with Jupyter Enterprise Gateway - Alan Chin (IBM), Luciano Resende (IBM)
    5. Jupyter Book: Online interactive books with the Jupyter Notebook- Chris Holdgraf (Berkeley Institute for Data Science)
  16. Tutorials
    1. Hands-on Machine Learning with Kafka-based Streaming Pipelines - Boris Lublinsky (Lightbend), Dean Wampler (Lightbend) - Part 1
    2. Hands-on Machine Learning with Kafka-based Streaming Pipelines - Boris Lublinsky (Lightbend), Dean Wampler (Lightbend) - Part 2
    3. Hands-on Machine Learning with Kafka-based Streaming Pipelines - Boris Lublinsky (Lightbend), Dean Wampler (Lightbend) - Part 3
    4. Hands-on Machine Learning with Kafka-based Streaming Pipelines - Boris Lublinsky (Lightbend), Dean Wampler (Lightbend) - Part 4
    5. Introduction to Flink via Flink SQL - Fabian Hueske (Ververica) - Part 1
    6. Introduction to Flink via Flink SQL - Fabian Hueske (Ververica) - Part 2
    7. Introduction to Flink via Flink SQL - Fabian Hueske (Ververica) - Part 3
    8. Managing data science in the enterprise - Joshua Poduska (Domino Data Lab), Kimberly Shenk (NakedPoppy), Mac Steele (Domino Data Lab) - Part 1
    9. Managing data science in the enterprise - Joshua Poduska (Domino Data Lab), Kimberly Shenk (NakedPoppy), Mac Steele (Domino Data Lab) - Part 2
    10. Managing data science in the enterprise - Joshua Poduska (Domino Data Lab), Kimberly Shenk (NakedPoppy), Mac Steele (Domino Data Lab) - Part 3
    11. Streamlining a Machine Learning Project Team - Sourav Dey (Manifold), Alex Ng (Manifold) - Part 1
    12. Streamlining a Machine Learning Project Team - Sourav Dey (Manifold), Alex Ng (Manifold) - Part 2
    13. Streamlining a Machine Learning Project Team - Sourav Dey (Manifold), Alex Ng (Manifold) - Part 3
    14. Running multidisciplinary big data workloads in the cloud - Jason Wang (Cloudera), Tony Wu (Cloudera), Vinithra Varadharajan (Cloudera) - Part 1
    15. Running multidisciplinary big data workloads in the cloud - Jason Wang (Cloudera), Tony Wu (Cloudera), Vinithra Varadharajan (Cloudera) - Part 2
    16. Running multidisciplinary big data workloads in the cloud - Jason Wang (Cloudera), Tony Wu (Cloudera), Vinithra Varadharajan (Cloudera) - Part 3
    17. Foundations for Successful Data Projects - Jonathan Seidman (Cloudera), Ted Malaska (Capital One) - Part 1
    18. Foundations for Successful Data Projects - Jonathan Seidman (Cloudera), Ted Malaska (Capital One) - Part 2
    19. Foundations for Successful Data Projects - Jonathan Seidman (Cloudera), Ted Malaska (Capital One) - Part 3
    20. Foundations for Successful Data Projects - Jonathan Seidman (Cloudera), Ted Malaska (Capital One) - Part 4
    21. Architecting a data platform for enterprise use - Mark Madsen (Think Big Analytics), Todd Walter (Teradata) - Part 1
    22. Architecting a data platform for enterprise use - Mark Madsen (Think Big Analytics), Todd Walter (Teradata) - Part 2
    23. Architecting a data platform for enterprise use - Mark Madsen (Think Big Analytics), Todd Walter (Teradata) - Part 3
    24. Architecting a data platform for enterprise use - Mark Madsen (Think Big Analytics), Todd Walter (Teradata) - Part 4
    25. Natural language understanding at scale with Spark NLP - David Talby (Pacific AI), Alex Thomas (Indeed), Claudiu Branzan (G2 Web Services) - Part 1
    26. Natural language understanding at scale with Spark NLP - David Talby (Pacific AI), Alex Thomas (Indeed), Claudiu Branzan (G2 Web Services) - Part 2
    27. Natural language understanding at scale with Spark NLP - David Talby (Pacific AI), Alex Thomas (Indeed), Claudiu Branzan (G2 Web Services) - Part 3
    28. Natural language understanding at scale with Spark NLP - David Talby (Pacific AI), Alex Thomas (Indeed), Claudiu Branzan (G2 Web Services) - Part 4
    29. Recurrent Neural Networks without a PhD workshop - Martin Gorner (Google) - Part 1
    30. Recurrent Neural Networks without a PhD workshop - Martin Gorner (Google) - Part 2
    31. Recurrent Neural Networks without a PhD workshop - Martin Gorner (Google) - Part 3
    32. Recurrent Neural Networks without a PhD workshop - Martin Gorner (Google) - Part 4
    33. The Hitchhiker's Guide to Deep Learning Based Recommenders in Production - Abhishek Kumar (Publicis.Sapient), Pramod Singh (Sapient Razorfish) - Part 1
    34. The Hitchhiker's Guide to Deep Learning Based Recommenders in Production - Abhishek Kumar (Publicis.Sapient), Pramod Singh (Sapient Razorfish) - Part 2
    35. The Hitchhiker's Guide to Deep Learning Based Recommenders in Production - Abhishek Kumar (Publicis.Sapient), Pramod Singh (Sapient Razorfish) - Part 3
    36. The Hitchhiker's Guide to Deep Learning Based Recommenders in Production - Abhishek Kumar (Publicis.Sapient), Pramod Singh (Sapient Razorfish) - Part 4
    37. Practical Techniques for Interpretable Machine Learning - Patrick Hall (H2O.ai | George Washington University) - Part 1
    38. Practical Techniques for Interpretable Machine Learning - Patrick Hall (H2O.ai | George Washington University) - Part 2
    39. Practical Techniques for Interpretable Machine Learning - Patrick Hall (H2O.ai | George Washington University) - Part 3
    40. Practical Techniques for Interpretable Machine Learning - Patrick Hall (H2O.ai | George Washington University) - Part 4
    41. Learning Presto: SQL-on-Anything - Matt Fuller (Starburst) - Part 1
    42. Learning Presto: SQL-on-Anything - Matt Fuller (Starburst) - Part 2
    43. Learning Presto: SQL-on-Anything - Matt Fuller (Starburst) - Part 3
    44. Learning Presto: SQL-on-Anything - Matt Fuller (Starburst) - Part 4