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.
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.
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
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.
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.
Leveraging Cloud for BigData Analytics - Patterns, Options and Practical Next Steps
This talk will cover in-depth about leveraging public clouds for Big data analytics. It will also describe the next steps to get you started on your cloud based big data analytics initiative.
Of late, Cloud has proven to be a great enabler for Big data analytics. A great example of convergence of two very powerful technologies. All the leading public cloud vendors like Amazon, Microsoft and Google have done (and are continuing to do) extensive research in this area to utilize the key strengths of cloud computing to provide different big data capabilities. Amazon EMR, Azure ML and Google BigQuery are some of the more visible options available.
We will talk about the why, what and how of Big Data on cloud during this session.
The talk will start with highlights of the key benefits of leveraging cloud for big data applications. Usage of elastic nature of cloud to cater to the high throughput and large data volume requirements. Big Data Analytics is slowly migrating towards Cloud Computing platforms – we will look at the drivers that are making this happen.
This talk will then cover existing cloud based big data capabilities available under different clouds and will give sense of direction in which further research is going. It will also help with making vendor choices based on the nature of your big data analytics requirement. You will get answers about how to handle structured, semi-structured, unstructured and streaming data workloads on cloud. We will see how to handle massive data storage, support for Hadoop and spark for data processing and data visualization using the cloud infrastructure. A brief look will be given to the aspect of significant cost savings achieved when using cloud for big data.
I will discuss in detail about a real world example of Big Data application successfully deployed on cloud.
This will be followed by describing the next steps to get you started on your cloud based big data analytics initiative.
Conceptual understanding of Big Data analytics.
Conceptual understanding of Cloud Computing.
Amit Jain has almost 2 decades of experience in the software industry. He has got the opportunity to work on variety of technologies in India and United States; growing his technical career starting with mainframes to some of the cutting edge technologies of today. He is serving Aricent Technologies as Technology Director. He is the key technology driver for Big Data and Cloud computing initiatives at his organization.