Strata + Hadoop World 2017
2017-03-13..2017-03-16
San Jose, United States
[Strata + Hadoop World 2016 - San Jose, California, Strata + Hadoop World 2016 - New York, New York, Strata + Hadoop World 2016 - Singapore, Strata + Hadoop World 2017]
who it's for
machine learning engineers, data engineers, data scientists, platform engineers, enterprise architects, site reliability engineers
topics
Hadoop and Spark platforms; MLlib and TensorFlow integration; real-time scoring with Kafka and in-memory DB; Ray distributed execution framework; edge machine learning with MapR Data Fabric; genomic sequencing pipelines and SNAP alignment; productionizing machine learning for enterprise; SQL on data lake with Presto; Google Cloud scalability and provisioning limits; veracity scoring for news credibility; robotics perception and social design; Kylo and NiFi ETL frameworks
| title | author | length |
|---|---|---|
| The machine-learning renaissance | Mike Olson | 04m |
Argues that a true machine learning renaissance arrives only when abundant data meets a platform to manage it, and names the two technical enablers as Hadoop… | ||
| Applying data and machine learning to scale education | Daphne Koller | 03m |
Coursera applies large-scale data and ML to make course recommendations for 24 million registered users, tackling cold-start with IP-derived country, referrer… | ||
| Launching Pokémon GO | Phil Keslin, Beau Cronin | 04m |
Niantic launches in Australia/New Zealand then the US ~12 hours later and immediately exceeds capacity, running on Google Cloud with a 5X provisioning cap that… | ||
| Machines and the magic of fast learning | Eric Frenkiel | 06m |
Connects live data pipelines to machine learning and shows concrete wins: a nonprofit Thorne scans 100,000 public ads per day and faces 5,000% growth in… | ||
| Collaboration in AI benefits humanity | Jason Waxman | 07m |
Claims concrete wins: a radiology model raised thyroid nodule detection from 75% (experienced doctors) to 90% accurate and is in production screening thousands… | ||
| Turning the internet upside down: Driving big data right to the edge | Ted Dunning | 09m |
Argues the Internet flips from cloud-centric to device-first and shows a working stack: MapR Data Fabric spanning cloud, regional cores and tiny edge clusters… | ||
| Becoming smarter about credible news | Tom Reilly, Khalid Al-Kofahi | 04m |
Thomson Reuters shows a live system that clusters real-time tweets and articles into user-defined channels, extracts topics and concepts, scores each cluster… | ||
| Making good robots | Andra Keay | 03m |
Argues that everyday robots already combine face recognition, emotion detection, voice/NLP, object recognition, mapping and path planning and can access… | ||
| Big data, AI, the genome, and everything | Vijay Narayanan | 14m |
A real case shows rapid sequencing saving a life after cerebrospinal fluid DNA pinpointed an obscure bacterial infection. DNA sequencing cost falls from ~$10… | ||
| Ray: A Distributed Execution Framework for Emerging AI Applications | Michael Jordan | 03m |
A new execution framework called Ray aims to replace Spark for low-latency, live-data AI workloads by combining the familiar guarantees of Hadoop/Spark with a… | ||
| Driving enterprise open source adoption, from data lake to AI | Ron Bodkin | 06m |
Teradata explains how enterprises combine data warehouses and Hadoop data lakes to run SQL on raw petabyte data with Presto, stream ingest and real‑time… | ||
| Data in disasters: Saving lives and innovating in real time | Desiree Matel-Anderson | 03m |
Field Innovation Team mines over 7 million tweets with Splunk and finds a clear spike in the phrase “please help” on Oct 30–31 after Hurricane Sandy, tying… | ||
| Machine learning is about your data and deployment | Dinesh Normal | 06m |
Argues that production matters more than fancy models: focus on three Ds — data cleaning, model development, and deployment — with deployment causing the… | ||
| Machine learning at Google | Rob Craft | 07m |
Google describes real deployments: search shifts from ~two and a half words of query context toward conversational inputs after 13 years of ML work, cutting… | ||
| Unpacking MapR Edge | Jack Norris | 13m |
Announces MapR Edge as a small-footprint edge node that can run on an Intel NUC and join the full MapR fabric so management, data protection and a global… | ||
| Deep learning helping to solve cancer | Jason Waxman | 11m |
Intel describes concrete projects applying deep learning to cancer: an open Collaborative Cancer Cloud to pool genomes across institutions, a deployed imaging… | ||
| Data science is no longer a back office function | William Merchan | 09m |
Platform provides a managed environment with R Python Scala and dependency/version control, aimed at closing the handoff where engineering rewrites models by… | ||
| How to be fast and have a good memory with Machine Learning | Eric Frenkiel | 09m |
MemSQL presents a main-memory database with built-in TensorFlow integration, Kafka-connected create pipeline primitives that attach streams to tables, and… | ||
| Launching Teradata IntelliCloud | Brian Wood | 08m |
Announces Teradata IntelliCloud, a second‑generation managed cloud that runs Teradata Database, Aster analytics and partner Hadoop stacks on Teradata data… | ||
| What's the difference between BI and machine learning | Rob Craft | 13m |
Business intelligence summarizes why things happened; machine learning builds statistical models to predict and act on outcomes. Uses concrete tools like… | ||
| Operationalizing machine learning for the enterprise | Dinesh Nirmal | 13m |
Covers how enterprises move models from notebooks into COBOL and legacy apps using IBM tools such as Watson deep learning APIs and TensorFlow with R and Python… | ||
| The Servicification of the Economy | Robert Cohen | 14m |
Argues that digital conversion of products into services drives hidden productivity gains and changed job mixes, claiming 5–7% extra GDP growth from 2020–2025… | ||
| nothing matches | ||