Strata Data Conference - New York, NY 2018

Table of contents

1. Keynotes
   1. The future of data warehousing - Anupam Singh (Cloudera), Brian Coyne (PNC)
   2. Managing risk in machine learning - Ben Lorica (O'Reilly Media)
   3. The answer to life, the universe, and everything: But can you get that into production? (sponsored by MapR) - Ted Dunning (MapR)
   4. Von Neumann to Deep Learning: Data Revolutionizing the Future - Jeffrey Wecker (Goldman Sachs)
   5. AI, ML, and the IoT will destroy the data center and the cloud (just not in the way you think) (sponsored by Cisco) - DD Dasgupta (Cisco)
   6. The Missing Piece - Cassie Kozyrkov (Google)
   7. Leveraging the best of the past to power a better future (sponsored by MemSQL) - Drew Paroski (MemSQL)
   8. The power of Ethereum - Joseph Lubin (Consensus Systems)
   9. Sound design and the future of experience - Amber Case (MIT Media Lab)
   10. Wait. . .pizza is a vegetable? Decoding regulations using machine learning (sponsored by IBM) - Dinesh Nirmal (IBM)
   11. Practical ML today and tomorrow - Hilary Mason (Cloudera Fast Forward Labs)
   12. Derive value from analytics and AI at scale (sponsored by Intel) - (Ziya Ma) (Intel)
   13. Quantifying forgiveness - Julia Angwin (ProPublica)
   14. Smarter cities through Geotab with BigQuery ML and geospatial analytics (sponsored by Google Cloud) - Chad W. Jennings (Google)
   15. Brain-based human-machine interfaces: New developments, legal and ethical issues, and potential uses - Amanda Pustilnik (University of Maryland School of Law | Center for Law, Brain Behavior, Mass. General Hospital)
   16. The data imperative - Ben Sharma (Zaloni)
   17. Black box: How AI will amplify the best and worst of humanity - Jacob Ward (CNN | Al Jazeera | PBS)
2. Sponsored
   1. Feet on the ground, head in the clouds (sponsored by AtScale) - Mark Stange-Tregear (Ebates)
   2. The importance of experimental iteration: A data-centric approach to an AI project (sponsored by Globant) - Antonio Fragoso (Globant)
   3. Augmented data engineering: Leveraging machine learning in data profiling and discovery (sponsored by Io-Tahoe) - Arun Murugan (GE Digital), Jeff Miller (GE)
   4. On the road to digital transformation, AI is a team sport (sponsored by Oracle + DataScience.com) - Ian Swanson (Oracle)
   5. Interactive business intelligence and OLAP on big data lakes using a Spark-native fast data mart (sponsored by Oracle + DataScience.com) - Srikanth Desikan (Oracle)
   6. Quick, reliable, and cost-effective ways to operationalize big data apps (sponsored by Unravel) - Shivnath Babu (Unravel Data Systems, Duke University), Madhusudan Tumma (TIAA)
   7. A developer's guide to building AI applications (sponsored by Microsoft) - Wee Hyong Tok (Microsoft)
   8. From data lakes to the data fabric: Our vision for digital strategy (sponsored by Cambridge Semantics) - Ben Szekely (Cambridge Semantics)
   9. Best practices for migrating big data workloads to Amazon Web Services (sponsored by Amazon Web Services) - Bruno Faria (Amazon Web Services)
   10. Commercial software in an increasingly open source ecosystem (sponsored by SAS) - Paul Kent (SAS)
   11. How Bell Canada increased the scale of BI exponentially with OLAP on big data (sponsored by Kyvos Insights) - Mark Huang (Bell Canada)
   12. Guidebook to unwind the enterprise "data hairball" and get ready for AI (sponsored by IBM) - Tim Davis (IBM)
   13. Bringing together machine and human intelligence (sponsored by SAP) - Richard Mooney (SAP)
   14. Accelerate big data analytics and AI with NetApp hybrid cloud architecture (sponsored by NetApp) - Karthikeyan Nagalingam (NetApp)
   15. From two weeks in Python to two hours in Pentaho: Building modern big data pipelines for machine learning (sponsored by Hitachi Vantara) - David Huh and Kevin Haas (Hitachi Vantara)
   16. Refactor your data warehouse with mobile analytics products (sponsored by Kyligence) - Zhi Zhu (China Construction Bank ), Luke Han (Kyligence)
   17. A tale of two BI standards: Data warehouses and data lakes (sponsored by Arcadia Data) - Randy Lea (Arcadia Data)
   18. Hadoop-compatible filesystems: The limits of "compatible" (sponsored by WANdisco) - Paul Scott-Murphy (WANdisco)
   19. How the blurring of memory and storage is revolutionizing the data era (sponsored by Intel) - Arakere Ramesh (Intel), Bharath Yadla (Aerospike)
   20. Kubernetes plays Cupid for data scientists and IT (sponsored by MapR) - Skyler Thomas (MapR)
   21. How to avoid drowning in logs: Streaming 80 billion events and batch processing 40 TB/hour (sponsored by Pure Storage) - Ivan Jibaja (Pure Storage)
   22. Speed, scale, smarts: GPU-powered analytics for the extreme data economy (sponsored by Kinetica) - Michael Mahoney (Kinetica)
   23. Assumptions, constraints, and risks: How the wrong assumptions can jeopardize any model (sponsored by IBM) - Jennifer Shin (8 Path Solutions | NYU Stern | IBM)
   24. Building the Bridge from Big Data to ML, featuring Geotab (sponsored by Google Cloud) - Bob Bradley (Geotab), Chad W. Jennings (Google)
   25. Data operations problems created by deep learning and how to fix them (sponsored by MapR) - Jim Scott (MapR Technologies)
   26. Redis for velocity and volume: Fast data ingest and probabilistic data structures (sponsored by Redi Labs) - Kyle Davis (Redis Labs)
   27. Enabling predictive maintenance using automated IoT data pipelines (sponsored by BMC) - Basil Faruqui (BMC Software)
   28. Getting the most out of advanced analytics with people (sponsored by Alteryx) - Patrick Nussbaumer (Alteryx)
   29. Conda, Docker, and Kubernetes: The cloud-native future of data science (sponsored by Anaconda) - Mathew Lodge (Anaconda)
   30. Simplifying AI infrastructure: Lessons in scaling a deep learning enterprise (sponsored by NVIDIA) - Darrin Johnson (NVIDIA)
   31. Deep learning: Assessing analytics project feasibility and requirements (sponsored by NVIDIA) - Ward Eldred (NVIDIA)
   32. Kubernetes on GPUs (sponsored by NVIDIA) - Michael Balint (NVIDIA)
3. Data science and machine learning
   1. Programming by input-output examples - Sumit Gulwani (Microsoft)
   2. Breaking the rules: End-stage renal disease prediction - Olga Cuznetova (Optum), Manna Chang (Optum)
   3. A roadmap for open data science and AI for business: Panel discussion with State Street - Bethann Noble (Cloudera), Daniel Huss (State Street), Abhishek Kodi (State Street)
   4. Perverse incentives in metrics: Inequality in the like economy - Bonnie Barrilleaux (LinkedIn)
   5. Semantic recommendations - Shioulin Sam (Cloudera Fast Forward Labs)
   6. Deploying machine learning models in the enterprise - Diego Oppenheimer (Algorithmia)
   7. 50 reasons to learn the shell for doing data science - Jeroen Janssens (Data Science Workshops B.V.)
   8. Why data scientists should love Linux containers - William Benton (Red Hat)
   9. Diversification in recommender systems: Using topical variety to increase user satisfaction - Ahsan Ashraf (Pinterest)
   10. When Tiramisu meets online fashion retail - Patty Ryan (Microsoft), CY Yam (Microsoft), Elena Terenzi (Microsoft)
   11. Scalable machine learning for data cleaning - Ihab Ilyas (University of Waterloo | Tamr)
   12. Solving the cold start problem: Data and model aggregation using differential privacy - Chang Liu (Georgian Partners )
   13. Machine learning for time series: What works and what doesn't - Mikio Braun (Zalando SE)
   14. BlazeIt: An exploratory video analytics engine - Daniel Kang (Stanford University)
   15. Predicting residential occupancy and hot water usage from high-frequency, multivector utilities data - Cristobal Lowery (Baringa), Marc Warner (ASI)
   16. Achieving personalization with LSTMs - Ankit Jain (Uber)
   17. Harnessing and customizing state-of-the-art recommendation solutions with OpenRec - Longqi Yang (Cornell Tech, Cornell University)
   18. Democratizing deep learning with transfer learning - Lars Hulstaert (Microsoft)
4. Emerging technologies case studies
   1. What's the Hadoop-la about Kubernetes? - Anant Chintamaneni (BlueData), Nanda Vijaydev (BlueData)
   2. Progress for big data in Kubernetes - Ted Dunning (MapR)
   3. High-performance messaging with Apache Pulsar - Karthik Ramasamy (Streamlio), Matteo Merli (Streamlio)
   4. Driving predictive analytics for the IoT and connected vehicles - Steve Otto (Navistar)
   5. Sewers can talk: Understanding the language of sewers - Greg Quist (SmartCover Systems)
5. Data engineering and architecture
   1. The move to a modern data platform in the cloud: Pitfalls to avoid and best practices to follow - Amandeep Khurana (Okera)
   2. Data governance: A big job that's getting bigger - Andrew J Brust (ZDNet | Blue Badge Insights)
   3. Machine learning for nonstationary streaming data using Structured Streaming and StreamDM - Heitor Murilo Gomes (Télécom ParisTech), Albert Bifet (Télécom ParisTech)
   4. Marmaray: A generic, scalable, and pluggable Hadoop data ingestion and dispersal framework - Danny Chen (Uber Technologies), Omkar Joshi (Uber Technologies), Eric Sayle (Uber Technologies)
   5. Setting up a lightweight distributed caching layer using Apache Arrow - Jacques Nadeau (Dremio)
   6. How Komatsu is improving mining efficiencies using the IoT and machine learning - Shawn Terry (Komatsu)
   7. From flat files to deconstructed database: The evolution and future of the big data ecosystem - Julien Le Dem (WeWork)
   8. Using machine learning to drive intelligence at the edge - Dave Shuman (Cloudera), Bryan Dean (Red Hat)
   9. Performant time series data management and analytics with Postgres - Michael Freedman (TimescaleDB)
   10. Case study: A Spark-based distributed simulation optimization architecture for portfolio optimization in retail banking - Kaushik Deka (Novantas), Ted Gibson (Novantas)
   11. The future of ETL isn’t what it used to be. - Gwen Shapira (Confluent)
   12. Tracking data lineage at Stitch Fix - Neelesh Srinivas Salian (Stitch Fix)
   13. Real-time analytics and BI with data lakes and data warehouses using Kudu, HBase, Spark, and Kafka: Lessons learned - Mauricio Aristizabal (Impact)
   14. Clouds and containers: Case studies for big data - Paul Curtis (MapR Technologies)
   15. Lessons learned building a scalable and extendable data pipeline for Call of Duty - Yaroslav Tkachenko (Activision)
   16. A comparative analysis of the fundamentals of AWS and Azure - Jason Wang (Cloudera), Suraj Acharya (Cloudera), Tony Wu (Cloudera)
   17. Apache Kafka and the four challenges of production machine learning systems - Jay Kreps (Confluent)
   18. TuneIn: How to get your jobs tuned while you are sleeping - Manoj Kumar (LinkedIn), Pralabh Kumar (LinkedIn), Arpan Agrawal (LinkedIn)
   19. Building a high-performance model serving engine from scratch using Kubernetes, GPUs, Docker, Istio, and TensorFlow - Chris Fregly (PipelineAI)
   20. Big data at speed - Ted Malaska (Capital One), Mark Grover (Lyft)
   21. IoT edge processing with Apache NiFi, Apache MiniFi, and multiple deep learning libraries - Timothy Spann (DZone)
   22. MLflow: An open platform to simplify the machine learning lifecycle - Mani Parkhe (Databricks), Andrew Chen (Databricks)
6. Text and Language processing and analysis
   1. Document vectors in the wild: Building a content recommendation system for Reuters.com - James Dreiss (Reuters)
   2. Anxiety at scale: How Investopedia used readership data to track market volatility - Masha Westerlund (Investopedia)
   3. From emotion analysis and topic extraction to narrative modeling - Andreea Kremm (Netex Group), Mohammed Ibraaz Syed (UCLA)
   4. Automating business processes with large-scale knowledge graphs - Mike Tung (Diffbot)
   5. Applying petabyte-scale analytics and machine learning to billions of news reading sessions - Andrew Montalenti (Parse.ly )
7. Data Platforms
   1. DIY versus designer approaches to deploying data center infrastructure for machine learning and analytics - Cory Minton (Dell EMC), Colm Moynihan (Cloudera)
   2. Bighead: Airbnb's end-to-end machine learning platform - Atul Kale (Airbnb), Xiaohan Zeng (Airbnb)
   3. Zipline: Airbnb's data management platform for machine learning - Varant Zanoyan (Airbnb)
   4. How to cost-effectively and reliably build infrastructure for machine learning - Osman Sarood (Mist Systems)
   5. TonY: Native support of TensorFlow on Hadoop - Jonathan Hung (LinkedIn), Keqiu Hu (LinkedIn), Zhe Zhang (LinkedIn)
   6. Data at Netflix: See what’s next - Michelle Ufford (Netflix)
   7. Deep learning on YARN: Running distributed TensorFlow, MXNet, Caffe, and XGBoost on Hadoop clusters - Wangda Tan (Hortonworks inc)
   8. A high-performance system for deep learning inference and visual inspection - Moty Fania (Intel), Sergei Kom (Intel)
   9. A/B testing at Uber: How we built a BYOM (bring your own metrics) platform - Milene Darnis (Uber)
   10. Using Alluxio as a fault-tolerant pluggable optimization component of JD.com's compute frameworks - tao huang (JD.com), mang zhang (JD.com), 冰 白 (JD.com)
   11. Aetna's advanced analytics platform, Data Fabric - Occhio Orsini (Aetna)
   12. Scaling data infrastructure in the fashion world; or, “What is this? Business intelligence for ants?” - Francesco Mucio (Zalando SE)
8. Big data and data science in the cloud
   1. Circuit breakers to safeguard for garbage in, garbage out - Sandeep Uttamchandani (Intuit)
   2. Job recommendations leveraging Deep Learning using Analytics Zoo on Apache Spark and BigDL - Guoqiong Song (Intel), Wenjing Zhan (Talroo), Jacob Eisinger (Talroo )
   3. Cassandra versus cloud databases - Jonathan Ellis (DataStax)
   4. Optimizing Apache Impala for a cloud-based data warehouse - Greg Rahn (Cloudera)
   5. Deep learning on audio in Azure to detect sounds in real time - Swetha Machanavajhala (Microsoft), Xiaoyong Zhu (Microsoft)
   6. Building turnkey recommendations for 5% of internet video - Nir Yungster (JW Player), Kamil Sindi (JW Player)
   7. Best practices for developing an enterprise data hub to collect and analyze 1 TB of data a day from a multiple services with Apache Kafka and Google Cloud Platform - Kenji Hayashida (Recruit Lifestyle co., ltd.), Toru Sasaki (NTT DATA Corporation)
9. Strata Business Summit
   1. From data governance to AI governance: The CIO's new role - JF Gagne (Element AI)
   2. Agile for data science teams - Jennifer Prendki (Figure Eight)
   3. Executive Briefing: Profit from AI and machine learning—The best practices for people and process - Tony Baer (Ovum), Florian Douetteau (DATAIKU)
   4. Realizing the true value in your data: Data-drivenness assessment - Lawrence Cowan (Cicero Group)
   5. An ethical foundation for the AI-driven future - Harry Glaser (Periscope Data)
   6. Executive Briefing: Managing successful data projects—Technology selection and team building - Ted Malaska (Blizzard Entertainment), Jonathan Seidman (Cloudera)
   7. The lure of "the one metric that matters" - Adil Aijaz (Split Software)
   8. Executive Briefing: Enhance your data lake with comprehensive data governance to improve adoption and meet compliance needs - Sanjeev Mohan (Gartner)
   9. Rationalizing risk in AI and ML - Kimberly Nevala (SAS Institute)
   10. The care and feeding of data scientists: Concrete tips for retaining your data science team - Michelangelo D'Agostino (ShopRunner)
   11. Data and privacy at scale at Wikipedia - Nuria Ruiz (Wikimedia)
   12. Executive Briefing: Why machine-learned models crash and burn in production and what to do about it - David Talby (Pacific AI)
   13. Executive Briefing: From Business to AI—The missing pieces in becoming "AI ready" - Mikio Braun (Zalando SE)
   14. Enacting Data Subject Access Rights for GDPR with data services and data management - Jean-Michel Franco (Talend)
   15. Executive Briefing: Analytics for executives—Building an approachable language to drive data science in your organization - Brandy Freitas (Pitney Bowes)
   16. Building it beautiful: Analyzing the effectiveness of platform products and marketing at scale - Joshua Laurito (Squarespace)
   17. Executive Briefing: Best practices for human in the loop—The business case for active learning - Paco Nathan (derwen.ai)
   18. Analytics maturity: Industry trends and financial impacts - Bill Franks (International Institute For Analytics)
   19. Real-time machine intelligence in IndyCar and Tour de France - Yasuyuki Kataoka (NTT Innovation Institute, Inc.)
   20. A day in the life of a data scientist: How do we train our teams to get started with AI? - Francesca Lazzeri (Microsoft), Jaya Mathew (Microsoft)
10. Data-driven business management
    1. Visualize AI to spot new trading opportunities - Paul Lashmet (Arcadia Data)
    2. The business case for messy data - James Psota (Panjiva )
    3. Modernizing operational architecture with big data: Creating and implementing a modern data strategy - Jennifer Lim (Cerner)
    4. How to be aggressively tone-deaf using data; or, We should all be "for-benefits." - Ann Nguyen (Whole Whale)
    5. "Moneyballing" recruiting: A data-driven approach to battling bottlenecks and biases in hiring - Maryam Jahanshahi (TapRecruit)
    6. Real-time automated claim processing: The surprising utility of NLP methods on non-text data - Amro Alkhatib (National Health Insurance Company-Daman)
    7. From chaos to insight: Automatically derive value from your user-generated content - Stephanie Fischer (datanizing GmbH)
    8. Cataloging the data lake for distributed analytics innovation at Munich Re - Andreas Kohlmaier (Munich Re)
    9. Why the internet of things doesn’t exist but will still reshape your business - Ajay Kulkarni (TimescaleDB)
    10. Data science in an Agile environment: Methods and organization for success - Sam Helmich (Deere Company)
    11. Too big data to fail: How banks use big data to prevent the next financial crisis - Patrick Angeles (Cloudera)
    12. Decision-centricity: Operationalizing analytics and data science in health systems - Mike Berger (Mount Sinai Health System)
    13. The revenue forecasting platform at Airbnb - Theresa Johnson (Airbnb)
    14. Self-reliant, secure, end-to-end data, activity, and revenue analytics: A roadmap for the airline industry - Katharina Warzel (EveryMundo)
    15. Improving patient screening by applying predictive analytics to electronic medical records. - Ian Brooks (Hortonworks)
    16. From strategy to implementation: Putting data to work at USA for UNHCR - Friederike Schuur (Cloudera), Rita Ko (USA for UNHCR)
    17. Executive Briefing: Most data-driven cultures aren’t - Cassie Kozyrkov (Google)
11. Law, ethics, governance
    1. Beyond explainability: Regulating machine learning in practice - Andrew Burt (Immuta)
    2. If you thought politics was dirty, you should see the analytics behind it. - John Thuma (Arcadia Data)
    3. Explainable artificial intelligence (XAI): Why, when, and how? - Mridul Mishra (Fidelity Investments)
    4. Balancing stakeholder interests in personal data governance technology - LaVonne Reimer (Lumenous)
    5. Mapping India - Anand S (Gramener)
    6. Democratizing artificial intelligence: Lessons from the real world - Swatee Singh (American Express)
12. Platform security and cybersecurity
    1. Protecting sensitive data in huge datasets: Cloud tools you can use - Felipe Hoffa (Google), Damien Desfontaines (Google / ETH Zürich)
    2. Privacy by design: Building in data privacy and protection versus bolting it on later - Les McMonagle (BlueTalon)
13. Visualization and user experience
    1. The Vega project: Building an ecosystem of tools for interactive visualization - Jeffrey Heer (Trifacta | University of Washington)
    2. Augmented reality: Going beyond plots in 3D - Bob Levy (Virtual Cove, Inc.)
    3. Stories beat statistics: How to master the art and science of data storytelling - Brent Dykes (Domo)
    4. Data visualization in mixed reality with Python - Anna Nicanorova (Annalect)
    5. UX strategies for underperforming analytics services and data products - Brian O'Neill (Designing for Analytics)
14. Streaming systems real-time applications
    1. Processing fast data with Apache Spark: A tale of two APIs - Gerard Maas (Lightbend)
    2. Building Fabric Answers using Apache Heron - Karthik Ramasamy (Streamlio), Andrew Jorgensen (Google)
    3. Why and how to leverage the power and simplicity of SQL on Apache Flink - Fabian Hueske (Apache Flink project)
    4. A deep dive into Kafka controller - Jun Rao (Confluent)
    5. Streaming big data in the cloud: What to consider and why - William Chambers (Databricks)
    6. AppNexus's stream-based control system for automated buying of digital ads - Brian Wu (AppNexus)
    7. Architectural principles for building trusted, real-time, distributed IoT systems - Dan Harple (Context Labs)
    8. Hudi: Unifying storage and serving for batch and near-real-time analytics - Nishith Agarwal (Uber), Balaji Varadarajan (Uber), Vinoth Chandar (Uber)
    9. Near-real-time anomaly detection at Lyft - Thomas Weise (Lyft), Mark Grover (Lyft)
    10. Executive Briefing: What you need to know about fast data - Dean Wampler (Lightbend)
    11. Kafka at PayPal: Enabling 400 billion messages a day - Kevin Lu (PayPal), Maulin Vasavada (PayPal), Na Yang (PayPal)
15. Financial Services
    1. Using the blockchain in the enterprise - Jim Scott (MapR Technologies)
    2. Accelerating financial data science workflows with GPUs - Joshua Patterson (NVIDIA), Onur Yilmaz (NVIDIA)
    3. Using big data to unlock the delivery of personalized, multilingual real-time chat services for global financial service organizations - Tim Walpole (BJSS)
    4. The balancing act: Building business-relevant data solutions for the front line - Jane Tran (Unqork)
    5. Stochastic field theory for time series - Revant Nayar (FMI Technologies LLC )
16. Tutorials
    1. Deep learning-based search and recommendation systems using TensorFlow - Dr. Vijay Srinivas Agneeswaran (SapientRazorfish), Abhishek Kumar (SapientRazorfish) - Part 1
    2. Deep learning-based search and recommendation systems using TensorFlow - Dr. Vijay Srinivas Agneeswaran (SapientRazorfish), Abhishek Kumar (SapientRazorfish) - Part 2
    3. Deep learning-based search and recommendation systems using TensorFlow - Dr. Vijay Srinivas Agneeswaran (SapientRazorfish), Abhishek Kumar (SapientRazorfish) - Part 3
    4. Deep learning-based search and recommendation systems using TensorFlow - Dr. Vijay Srinivas Agneeswaran (SapientRazorfish), Abhishek Kumar (SapientRazorfish) - Part 4
    5. Model serving and management at scale using open source tools - Dan Crankshaw (UC Berkeley RISELab) - Part 1
    6. Model serving and management at scale using open source tools - Dan Crankshaw (UC Berkeley RISELab) - Part 2
    7. Architecting a data platform for enterprise use - Mark Madsen (Think Big Analytics), Todd Walter (Teradata) - Part 1
    8. Architecting a data platform for enterprise use - Mark Madsen (Think Big Analytics), Todd Walter (Teradata) - Part 2
    9. Architecting a data platform for enterprise use - Mark Madsen (Think Big Analytics), Todd Walter (Teradata) - Part 3
    10. Architecting a data platform for enterprise use - Mark Madsen (Think Big Analytics), Todd Walter (Teradata) - Part 4
    11. Running multidisciplinary big data workloads in the cloud - Sudhanshu Arora (Cloudera), Stefan Salandy (Cloudera), Suraj Acharya (Cloudera), Brandon Freeman (Cloudera), Jason Wang (Cloudera), Shravan Pabba (Cloudera) - Part 1
    12. Running multidisciplinary big data workloads in the cloud - Sudhanshu Arora (Cloudera), Stefan Salandy (Cloudera), Suraj Acharya (Cloudera), Brandon Freeman (Cloudera), Jason Wang (Cloudera), Shravan Pabba (Cloudera) - Part 2
    13. Designing modern streaming data applications - Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio) - Part 1
    14. Designing modern streaming data applications - Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio) - Part 2
    15. Designing modern streaming data applications - Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio) - Part 3
    16. Designing modern streaming data applications - Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio) - Part 4
    17. How to be fair: A tutorial for beginners - Aileen Nielsen (Skillman Consulting) - Part 1
    18. How to be fair: A tutorial for beginners - Aileen Nielsen (Skillman Consulting) - Part 2
    19. How to be fair: A tutorial for beginners - Aileen Nielsen (Skillman Consulting) - Part 3
    20. How to be fair: A tutorial for beginners - Aileen Nielsen (Skillman Consulting) - Part 4
    21. Apache Metron: Open source cybersecurity at scale - Carolyn Duby (Hortonworks)
    22. Making interactive browser-based visualizations easy in Python - James Bednar (Anaconda) - Part 1
    23. Making interactive browser-based visualizations easy in Python - James Bednar (Anaconda) - Part 2
    24. Making interactive browser-based visualizations easy in Python - James Bednar (Anaconda) - Part 3
    25. Data science with Unix power tools - Jeroen Janssens (Data Science Workshops B.V.) - Part 1
    26. Data science with Unix power tools - Jeroen Janssens (Data Science Workshops B.V.) - Part 2
    27. Data science with Unix power tools - Jeroen Janssens (Data Science Workshops B.V.) - Part 3
    28. Data science with Unix power tools - Jeroen Janssens (Data Science Workshops B.V.) - Part 4
    29. Learning machine learning using astronomy datasets - Viviana Acquaviva (CUNY New York City College of Technology) - Part 1
    30. Learning machine learning using astronomy datasets - Viviana Acquaviva (CUNY New York City College of Technology) - Part 2
    31. Learning machine learning using astronomy datasets - Viviana Acquaviva (CUNY New York City College of Technology) - Part 3
    32. From theory to data product: Applying data science methods to effect business change - Janet Forbes (T4G), Danielle Leighton (T4G), Lindsay Brin (T4G) - Part 1
    33. From theory to data product: Applying data science methods to effect business change - Janet Forbes (T4G), Danielle Leighton (T4G), Lindsay Brin (T4G) - Part 2
    34. From theory to data product: Applying data science methods to effect business change - Janet Forbes (T4G), Danielle Leighton (T4G), Lindsay Brin (T4G) - Part 3
    35. Recurrent neural networks for time series analysis - Bruno Gonçalves (New York University) - Part 1
    36. Recurrent neural networks for time series analysis - Bruno Gonçalves (New York University) - Part 2
    37. Recurrent neural networks for time series analysis - Bruno Gonçalves (New York University) - Part 3
    38. Recurrent neural networks for time series analysis - Bruno Gonçalves (New York University) - Part 4
    39. Architecting a next-generation data platform - Ted Malaska (Blizzard Entertainment), Jonathan Seidman (Cloudera) - Part 1
    40. Architecting a next-generation data platform - Ted Malaska (Blizzard Entertainment), Jonathan Seidman (Cloudera) - Part 2
    41. Architecting a next-generation data platform - Ted Malaska (Blizzard Entertainment), Jonathan Seidman (Cloudera) - Part 3
    42. Architecting a next-generation data platform - Ted Malaska (Blizzard Entertainment), Jonathan Seidman (Cloudera) - Part 4
    43. Hands-on Kafka streaming microservices with Akka Streams and Kafka Streams - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend) - Part 1
    44. Hands-on Kafka streaming microservices with Akka Streams and Kafka Streams - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend) - Part 2
    45. Hands-on Kafka streaming microservices with Akka Streams and Kafka Streams - Dean Wampler (Lightbend), Boris Lublinsky (Lightbend) - Part 3
    46. Managing data science in the enterprise - Joshua Poduska (Domino Data Lab), Patrick Harrison (S Global) - Part 1
    47. Managing data science in the enterprise - Joshua Poduska (Domino Data Lab), Patrick Harrison (S Global) - Part 2
    48. Managing data science in the enterprise - Joshua Poduska (Domino Data Lab), Patrick Harrison (S Global) - Part 3
    49. Managing data science in the enterprise - Joshua Poduska (Domino Data Lab), Patrick Harrison (S Global) - Part 4
    50. Building a large-scale machine learning application using Amazon SageMaker and Spark - David Arpin (Amazon Web Services) - Part 1
    51. Building a large-scale machine learning application using Amazon SageMaker and Spark - David Arpin (Amazon Web Services) - Part 2
    52. Natural language understanding at scale with Spark NLP - David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed) - Part 1
    53. Natural language understanding at scale with Spark NLP - David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed) - Part 2
    54. Natural language understanding at scale with Spark NLP - David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed) - Part 3
    55. Natural language understanding at scale with Spark NLP - David Talby (Pacific AI), Claudiu Branzan (G2 Web Services), Alexander Thomas (Indeed) - Part 4
    56. Leveraging Spark and deep learning frameworks to understand data at scale - Vartika Singh (Cloudera), Alan Silva (Cloudera), Alex Bleakley (Cloudera), Steven Totman (Cloudera), Mirko Kämpf (Cloudera), Syed Nasar (Cloudera) - Part 1
    57. Leveraging Spark and deep learning frameworks to understand data at scale - Vartika Singh (Cloudera), Alan Silva (Cloudera), Alex Bleakley (Cloudera), Steven Totman (Cloudera), Mirko Kämpf (Cloudera), Syed Nasar (Cloudera) - Part 2
    58. Leveraging Spark and deep learning frameworks to understand data at scale - Vartika Singh (Cloudera), Alan Silva (Cloudera), Alex Bleakley (Cloudera), Steven Totman (Cloudera), Mirko Kämpf (Cloudera), Syed Nasar (Cloudera) - Part 3
    59. Leveraging Spark and deep learning frameworks to understand data at scale - Vartika Singh (Cloudera), Alan Silva (Cloudera), Alex Bleakley (Cloudera), Steven Totman (Cloudera), Mirko Kämpf (Cloudera), Syed Nasar (Cloudera) - Part 4
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    61. Deep learning methods for natural language processing - Garrett Hoffman (StockTwits) - Part 2
    62. Deep learning methods for natural language processing - Garrett Hoffman (StockTwits) - Part 3
    63. Deep learning methods for natural language processing - Garrett Hoffman (StockTwits) - Part 4