The Fifth Elephant 2015
A conference on data, machine learning, and distributed and parallel computing
Machine Learning, Distributed and Parallel Computing, and High-performance Computing are the themes for this year’s edition of Fifth Elephant.
The deadline for submitting a proposal is 15th June 2015
We are looking for talks and workshops from academics and practitioners who are in the business of making sense of data, big and small.
Track 1: Discovering Insights and Driving Decisions #
This track is about general, novel, fundamental, and advanced techniques for making sense of data and driving decisions from data. This could encompass applications of the following ML paradigms:
- Statistical Visualizations
- Unsupervised Learning
- Supervised Learning
- Semi-Supervised Learning
- Active Learning
- Reinforcement Learning
- Monte-carlo techniques and probabilistic programming
- Deep Learning
Across various data modalities including multi-variate, text, speech, time series, images, video, transactions, etc.
Track 2: Speed at Scale #
This track is about tools and processes for collecting, indexing, and processing vast amounts of data. The theme includes:
- Distributed and Parallel Computing
- Real Time Analytics and Stream Processing
- MapReduce and Graph Computing frameworks
- Kafka, Spark, Hadoop, MPI
- Stories of parallelizing sequential programs
- Cost/Security/Disaster Management of Data
Commitment to Open Source #
HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source license. If your software is commercially licensed or available under a combination of commercial and restrictive open source licenses (such as the various forms of the GPL), please consider picking up a sponsorship. We recognize that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.
Workshops #
If you are interested in conducting a hands-on session on any of the topics falling under the themes of the two tracks described above, please submit a proposal under the workshops section. We also need you to tell us about your past experience in teaching and/or conducting workshops.
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
RN
Russell Nash
@russnash
Deploying Batch and Streaming Architectures on AWS
Submitted Jun 18, 2015
Section:
Sponsored
Technical level:
Intermediate
To learn about the key Big Data and Analytics services on AWS and how they can be used for both batch and streaming workloads.
Outline #
One of the biggest challenges organizations face when designing Big Data platforms is analyzing historical, batch and streaming data using the same architecture.
This session will illustrate how to use AWS Big Data services such as Amazon Elastic MapReduce, Amazon Kinesis, Amazon Redshift and others to build a scalable, fault-tolerant and multi-layered processing system which includes the ability to analyze streaming data by comparing it against historical data in near real-time.
Speaker bio #
Russell Nash is a Solutions Architect with Amazon focused on data analytics.
He works with customers to derive maximum value and performance from using the Amazon data analytics services.
Russell has over 20 years experience in the IT industry and the majority of that time was spent working with database and parallel technologies designed for large scale analysis.
He is passionate about applying these technologies to business problems in order to return value and insight to organizations.
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Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
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