The Fifth Elephant 2018

The Fifth Elephant 2018

The seventh edition of India's best data conference

About the conference and topics for submitting talks:

The Fifth Elephant is rated as India’s best data conference. It is a conference for practitioners, by practitioners. In 2018, The Fifth Elephant will complete its seventh edition.

The Fifth Elephant is an evolving community of stakeholders invested in data in India. Our goal is to strengthen and grow this community by presenting talks, panels and Off The Record (OTR) sessions that present real insights about:

1. Data engineering and architecture: tools, frameworks, infrastructure, architecture, case studies and scaling.
2. Data science and machine learning: fundamentals, algorithms, streaming, tools, domain specific and data specific examples, case studies.
3. The journey and challenges in building data driven products: design, data insights, visualisation, culture, security, governance and case studies.
4. Talks around an emerging domain: such as IoT, finance, e-commerce, payments or data in government.

Target audience:

You should attend and speak at The Fifth Elephant if your work involves:

  1. Engineering and architecting data pipelines.
  2. Building ML models, pipelines and architectures.
  3. ML engineering.
  4. Analyzing data to build features for existing products.
  5. Using data to predict outcomes.
  6. Using data to create / model visualizations.
  7. Building products with data – either as product managers or as decision scientists.
  8. Researching concepts and deciding on algorithms for analyzing datasets.
  9. Mining data with greater speed and efficiency.
  10. Developer evangelists from organizations which want developers to use their APIs and technologies for machine learning, full stack engineering, and data science.

Perks for submitting proposals:

Submitting a proposal, especially with our process, is hard work. We appreciate your effort.
We offer one conference ticket at discounted price to each proposer, and a t-shirt.
We only accept one speaker per talk. This is non-negotiable. Workshops may have more than one instructor. In case of proposals where more than one person has been mentioned as collaborator, we offer the discounted ticket and t-shirt only to the person with who the editorial team corresponded directly during the evaluation process.

Format:

The Fifth Elephant is a two-day conference with two tracks on each day. Track details will be announced with a draft schedule in February 2018.

We are accepting sessions with the following formats:

  1. Full talks of 40 minutes.
  2. Crisp talks of 20 minutes.
  3. Off the Record (OTR) sessions on focussed topics / questions. An OTR is 60-90 minutes long and typically has up to four facilitators and one moderator.
  4. Workshops and tutorials of 3-6 hours duration on Machine Learning concepts and tools, full stack data engineering, and data science concepts and tools.
  5. Pre-events. Birds Of Feather (BOF) sessions, talks, and workshops for open houses and pre-events in Bangalore and other cities between October 2017 and June 2018.** Reach out to info@hasgeek.com should you be interested in speaking and/or hosting a community event between now and the conference in July 2018.

Selection criteria:

The first filter for a proposal is whether the technology or solution you are referring to is open source or not. The following criteria apply for closed source talks:

  1. If the technology or solution is proprietary, and you want to speak about your proprietary solution to make a pitch to the audience, you should pick up a sponsored session. This involves paying for the speaking slot. Write to fifthelephant.editorial@hasgeek.com
  2. If the technology or solution is in the process of being open sourced, we will consider the talk only if the solution is open sourced at least three months before the conference.
  3. If your solution is closed source, you should consider proposing a talk explaining why you built it in the first place; what options did you consider (business-wise and technology-wise) before making the decision to develop the solution; or, what is your specific use case that left you without existing options and necessitated creating the in-house solution.

The criteria for selecting proposals, in the order of importance, are:

  1. Key insight or takeaway: what can you share with participants that will help them in their work and in thinking about the ML, big data and data science problem space?
  2. Structure of the talk and flow of content: a detailed outline – either as mindmap or draft slides or textual description – will help us understand the focus of the talk, and the clarity of your thought process.
  3. Ability to communicate succinctly, and how you engage with the audience. You must submit link to a two-minute preview video explaining what your talk is about, and what is the key takeaway for the audience.

No one submits the perfect proposal in the first instance. We therefore encourage you to:

  1. Submit your proposal early so that we have more time to iterate if the proposal has potential.
  2. Talk to us on our community Slack channel: https://friends.hasgeek.com if you want to discuss an idea for your proposal, and need help / advice on how to structure it. Head over to the link to request an invite and join #fifthel.

Our editorial team helps potential speakers in honing their speaking skills, fine tuning and rehearsing content at least twice - before the main conference - and sharpening the focus of talks.

How to submit a proposal (and increase your chances of getting selected):

The following guidelines will help you in submitting a proposal:

  1. Focus on why, not how. Explain to participants why you made a business or engineering decision, or why you chose a particular approach to solving your problem.
  2. The journey is more important than the solution you may want to explain. We are interested in the journey, not the outcome alone. Share as much detail as possible about how you solved the problem. Glossing over details does not help participants grasp real insights.
  3. Focus on what participants from other domains can learn/abstract from your journey / solution. Refer to these talks from The Fifth Elephant 2017, which participants liked most: http://hsgk.in/2uvYKI9 and http://hsgk.in/2ufhbWb
  4. We do not accept how-to talks unless they demonstrate latest technology. If you are demonstrating new tech, show enough to motivate participants to explore the technology later. Refer to talks such as this: http://hsgk.in/2vDpag4 and http://hsgk.in/2varOqt to structure your proposal.
  5. Similarly, we don’t accept talks on topics that have already been covered in the previous editions. If you are unsure about whether your proposal falls in this category, drop an email to: fifthelephant.editorial@hasgeek.com
  6. Content that can be read off the internet does not interest us. Our participants are keen to listen to use cases and experience stories that will help them in their practice.

To summarize, we do not accept talks that gloss over details or try to deliver high-level knowledge without covering depth. Talks have to be backed with real insights and experiences for the content to be useful to participants.

Passes and honorarium for speakers:

We pay an honorarium of Rs. 3,000 to each speaker and workshop instructor at the end of their talk/workshop. Confirmed speakers and instructors also get a pass to the conference and networking dinner. We do not provide free passes for speakers’ colleagues and spouses.

Travel grants for outstation speakers:

Travel grants are available for international and domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans. If you require a grant, request it when you submit your proposal in the field where you add your location. The Fifth Elephant is funded through ticket purchases and sponsorships; travel grant budgets vary.

Last date for submitting proposals is: 31 March 2018.

You must submit the following details along with your proposal, or within 10 days of submission:

  1. Draft slides, mind map or a textual description detailing the structure and content of your talk.
  2. Link to a self-recorded, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond the solution you have built, or your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant.
  3. If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and a document showing the full workshop plan.

Contact details:

For more information about the conference, sponsorships, or any other information contact support@hasgeek.com or call 7676332020.

Hosted by

The Fifth Elephant - known as one the best #datascience and #machinelearning conference in Asia - is transitioning into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Rajdeep Dua

@rajdeepd

Building Scalable Machine Learning pipelines with Apache Prediction IO

Submitted Mar 30, 2018

The talk will help developers and data scientists understand how to build ML Pipelines using PredictionIO.
In this talk we will cover how Apache PredictionIO (an open source Machine Learning Server built on top of a state-of-the-art open source stack) helps reduce time from writing a Proof of Concept for a ML model to production ready Model serving micro service with persistent model. We will also show case how Apache PredictionIO helps mix and match multiple models to come up with hybrid Predictions from multiple algorithms.

Outline

  1. Define Machine Learning

  2. Relationship between Data Mining and Other Fields and tools
    * Available Tools
    * Processing Framework
    * Apache Spark, Apache Hadoop
    * Algorithm Libraries

    • MLlib, Mahout
    • Data storage
    • HBase, Cassandra, RDBMs
  3. A Classic Recommender Example: What is Missing?

  4. A Classic Recommender Example : Beyond prototyping
    How to deploy a scalable service that respond to dynamic prediction query?
    How do you persist the predictive model, in a distributed environment?
    How to make HBase, Spark and algorithms talking to each other?
    How should I prepare, or transform, the data for model training?
    How to update the model with new data without downtime?
    Where should I add some business logic?
    How to make the code configurable, re-usable and maintainable?
    How do I build all these with a separate of concerns (SoC)?

  5. Classic Recommender example : Apache Prediction IO
    PredictionIO is a machine learning server for building and deploying predictive engines on production in a fraction of the time. Built on Apache Spark, MLlib and HBase

  6. Event Server : Event Server : Collection Data Collecting Date
    Example Event
    Engine

  7. Functions of an Engine
    A. Train predictive model(s)
    B. Respond to dynamic query

  8. Deploying on Heroku/AWS/ GCE
    Event Server and PIO Engine run as two Applications
    Connected to the same PostgreSQL backend
    Event Server has Single dynos
    Web

PIO Engine has two dynos: Web, Train

  1. Collaborative Filtering and ALS
    Collaborative Filtering :
    Collaborative Filtering(CF) is a subset of algorithms that exploit other users and items along with their ratings(selection, purchase information could be also used)
    Target user history to recommend an item that target user does not have ratings for.
    Assumption behind this approach is that other users preference over the items could be used recommending an item to the user who did not see the item or purchase
    Matrix Factorization
    Both users and items are mapped to a joint latent factor space of dimensionality ‘f’ where user-item interaction is modeled as inner product in this space.
    Item i is associated with vector q
    (where q measures the extent to which the item possesses the latent factors)
    User u is associated with vector p
    (where p measures the extent of interest the user has in the item.)
    The dot product between q and p captures the interaction between user u and item I : i.e. users interest in item.
    Key to model is finding vectors q and p.

  2. Matrix Factorization: Alternative Least Square Method
    ALS works by iteratively solving a series of least squares regression problems. In each iteration, one of the user- or item-factor matrices is treated as fixed, while the other one is updated using the fixed factor and the rating data.
    User Factors : p
    Item Factors : q
    The factor matrix that was solved for is, in turn, treated as fixed, while the other one is updated. This process continues until the model has converged (or for a fixed number of iterations).

  3. Demo ALS

  4. Summary
    Building ML pipeline is about selecting the algorithm , training and tuning the model. Taking it to production is key to realizing the true power on ML and AI Prediction

Requirements

Internet connection, projector, microphone

Speaker bio

Rajdeep Dua has over 18 years of experience in the Cloud and Big
Data space. Currently, he leads Developer Relations team at Salesforce
India. He also works with the Engineering teams at Salesforce building scalable
AI services, which
uses Hadoop and Spark to expose big data processing tools for
developers. He has worked in the advocacy team for Google’s Big Data
tools, BigQuery. He worked on the Greenplum big data platform at
VMware in the developer evangelist team. He worked closely with a team
on porting Spark to run on VMware’s public and private cloud as a
feature set. He has taught Spark and Big Data at some of the most
prestigious tech schools in India.

He has also presented BigQuery and Google App Engine at W3C conference
in Hyderabad (http://wwwconference.org/proceedings/www2011/schedule/w
ww2011_Program.pdf). He led Developer Relations teams at Google,
VMware, and Microsoft. He has spoken at hundreds of other conferences
on the cloud.

His contributions to the open source community are related to Docker,
Kubernetes, Android, OpenStack, and cloudfoundry. He has teaching
experience in big data at IIIT Hyderabad, ISB, IIIT Delhi, and College
of Engineering Pune.

LinkedIn profile can be found at https://www.linkedin.com/in/rajdeepd.

Twitter : @rajdeepdua

Links

Slides

https://drive.google.com/file/d/1nCeFzyOsMggMIg7kbNHaKup_w2XDKCtO/view?usp=sharing

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Hosted by

The Fifth Elephant - known as one the best #datascience and #machinelearning conference in Asia - is transitioning into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more