Strata Data Conference 2018 - New York, NY
2018-09-11..2018-09-13
New York, United States
[Strata Data Conference 2017 - New York, New York, Strata Data Conference 2018, Strata Data Conference 2018 - New York, NY, Strata Data Conference 2019 - London, United Kingdom, Strata Data Conference 2019 - New York, NY]
| title | author | length |
|---|---|---|
| The Missing Piece | Cassie Kozyrkov | 02m |
Main claim: machine learning projects fail less from bad models and more from missing decision frameworks and domain wisdom, based on lessons learned at… | ||
| The Future of Data Warehousing | Anupam Singh, Brian Coyne | 05m |
Shows a reference architecture that pairs real-time streaming ingestion with columnar analytical storage and explains which pieces are responsible for scale… | ||
| Power a Better Future | Drew Paroski | 12m |
Claims a return to SQL with a distributed, in-memory, horizontally scalable database and shows a production migration where mem sequel replaces Elasticsearch… | ||
| The Answer to Life, the Universe, and Everything | Ted Dunning | 08m |
Claims that only ~15% of big-data projects reach production (Gartner) versus many teams hitting near 100% readiness by building repeatable platforms. Shows… | ||
| Black Box: How AI will Amplify the Best and Worst of Humanity | Jacob Ward | 04m |
A hospital deploys a machine learning model from Epic that scores cardiac patients with probabilities derived from past records — examples like 87% or 79% are… | ||
| Sound Design and the Future of Experience | Amber Case | 03m |
Argues that improving sound requires hardware and composition not just UX buzzwords, recommending sawtooth wave over cheap square waves and higher bandwidth… | ||
| Practical ML Today and Tomorrow | Hilary Mason | 04m |
Machine learning and artificial intelligence are exciting technologies, but real value comes from marrying those capabilities with the right business problems… | ||
| Brain-based Human-machine Interfaces | Amanda Pustilnik | 02m |
Factories in China already place EEG sensors in helmets to flag boredom, anger or frustration and similar devices aim at pain or lie detection and even… | ||
| Quantifying Forgiveness | Julia Angwin | 04m |
Investigation uses 30 million quotes and official zip‑code payout data from four states to test whether premiums track true accident cost. Finding: premiums… | ||
| The Data Imperative | Ben Sharma | 07m |
Argues that data loss is existential — cites 90% of Brazil's National Museum destroyed and frames corporate decline with S&P 500 lifespan 22 years — and… | ||
| Smarter Cities through Geotab | Chad W. Jennings | 07m |
Geotab ingests dense telematics (a US point cloud from one day of location data, plus velocity and acceleration from UPS trucks) into BigQuery and trains… | ||
| Strata Attendee Testimonial | 03m | |
Calls Strata the CES of data and lists concrete takeaways: last year had a streaming focus, this year shifts to applications and machine learning, speakers are… | ||
| Automation of decisions to augment efficiency in the enterprise | Amr Awadallah | 10m |
Claims concrete wins: Cloudera customers use ML to cut 400,000 lawyer hours via JP Morgan COIN that reviews contracts in ~10 minutes, and Commonwealth Bank… | ||
| Decoding Regulations Using Machine Learning | Dinesh Nirmal | 05m |
Regulatory text piles up — 81,000 pages since 2015 — and comes as messy PDFs, tables and flowcharts; IBM Emilia is pre-trained on federal, state and local regs… | ||
| Using machine learning to infuse data operations | Madhu Kochar | 08m |
IBM describes a pipeline that uses metadata‑based machine learning to automate data discovery and classification, deployable as part of an analytics stack with… | ||
| ML and AI systems will make mistakes | Kimberly Nevala | 09m |
Message: AI systems—even top models with great data—will make mistakes, so organizations must quantify error, set tolerance and guardrails, and design human… | ||
| Breaking down the buzzwords to get value from AI, ML, and deep learning | Ian Swanson | 04m |
Company-built platform started nearly five years ago gets acquired by Oracle in May 2018 and is now positioned to put models into production for enterprise… | ||
| Combining algorithms and quality data to build better AI platforms | Ma Ziya | 13m |
Argues that better data and tighter AI–analytics integration beat marginal algorithm gains, and shows a working stack: BigDL running on Spark and Hadoop with… | ||
| Technical convergence in enterprise database software | Rick Negrin | 05m |
Company claims that MemSQL 6.5 consolidates transactional, analytical and search workloads into one in‑memory SQL database, adds pipelines for faster ingest… | ||
| Open source is allowing companies access powerful software | Doug Cutting | 07m |
Subscribe to O'Reilly on YouTube: http://goo.gl/n3QSYi
Follow O'Reilly on:
Twitter: http://twitter.com/oreillymedia
Facebook… | ||
| AI and ML are changing traditional business | Dhritiman Dasgupta | 05m |
Argues that data centers and cloud persist but their role shifts toward where compute and learning must live — often at the edge because cameras and sensors… | ||
| The power of "The Age of With" | Rohit Balasubramanian | 08m |
Argues that the Age of With means humans plus technology cooperate, not replace; shows concrete toolset: cloud‑centric platforms, modernized architectures… | ||
| Automating cognitive tasks to increase business value | Ashish Verma | 09m |
Claims that data footprints double every 8–12 months and that automating data engineering can handle 30–40% automated data engineering of pipeline work… | ||
| nothing matches | ||