Theme and format
The Fifth Elephant 2017 is a four-track conference on:
- Data engineering – building pipelines and platforms; exposure to latest open source tools for data mining and real-time analytics.
- Application of Machine Learning (ML) in diverse domains such as IOT, payments, e-commerce, education, ecology, government, agriculture, computational biology, social network analysis and emerging markets.
- Hands-on tutorials on data mining tools, and ML platforms and techniques.
- Off-the-record (OTR) sessions on privacy issues concerning data; building data pipelines; failure stories in ML; interesting problems to solve with data science; and other relevant topics.
The Fifth Elephant is a conference for practitioners, by practitioners.
Talk submissions are now closed.
You must submit the following details along with your proposal, or within 10 days of submission:
- Draft slides, mind map or a textual description detailing the structure and content of your talk.
- Link to a self-record, 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 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.
- 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 documents showing the full workshop plan.
About the conference
This year is the sixth edition of The Fifth Elephant. The conference is a renowned gathering of data scientists, programmers, analysts, researchers, and technologists working in the areas of data mining, analytics, machine learning and deep learning from different domains.
We invite proposals for the following sessions, with a clear focus on the big picture and insights that participants can apply in their work:
- Full-length, 40-minute talks.
- Crisp, 15-minute talks.
- Sponsored sessions, of 15 minutes and 40 minutes duration (limited slots available; subject to editorial scrutiny and approval).
- Hands-on tutorials and workshop sessions of 3-hour and 6-hour duration where participants follow instructors on their laptops.
- Off-the-record (OTR) sessions of 60-90 minutes duration.
- Proposals will be filtered and shortlisted by an Editorial Panel.
- Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
- Proposers are also encouraged to vote and comment on other proposals submitted here.
We will notify you if we move your proposal to the next round or reject it. A speaker is NOT confirmed for a slot unless we explicitly mention so in an email or over any other medium of communication.
Selected speakers must participate in one or two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.
There is only one speaker per session. Entry is free for selected speakers.
Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.
Commitment to Open Source
We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.
- Deadline for submitting proposals: June 10
- First draft of the coference schedule: June 20
- Tutorial and workshop announcements: June 20
- Final conference schedule: July 5
- Conference dates: 27-28 July
For more information about speaking proposals, tickets and sponsorships, contact firstname.lastname@example.org or call +91-7676332020.
Machine Learning from Practice to Production
With AI research and machine learning systems growing at great speed, companies require significant effort to keep up or risk losing their relevance in this brave new world. The new tide also brings with it numerous tools to tackle previously intractable problems. However, there does seem to exist a gulf between appreciating these developments and subsequently deploying them. Despite the global push to democratize machine learning, the steps prescribed don’t align with the fuzzier problems that need solving.
As a startup focused on organizing the world’s ecommerce data, Semantics3 has faced its fair share of challenges. To tackle numerous problems covering categorization, feature extraction, cross-domain product matching and price tracking, we have had to incorporate multiple modern techniques into our workflows.
Going over our experiences, I would like to share the broader questions (not whether you need a CNN, GAN or TROL) that need to be considered. Datasets, frameworks and deployment practices - are just some of the topics I wish to touch upon. The talk is almost a recollection of our journey, when moving machine learning from practice to production in an ecommerce-centric environment.
Introduction [2-3 mins]
A short introduction to the topics that are to be covered. Additional context about the machine learning problems in the domain of ecommerce data will be presented. This section sets the stage for discussing the machine learning “pipeline” to be built for solving the various problems.
Let there be data [10 mins]
Having high-fidelity datasets is crucial when starting on any machine learning problem. Balanced classes, representative coverage of ground truth, and adversarial examples, all need to be considered before jumping in with the modelling.
- Starting from Day 0
- Garbage In, Garbage Out
- Organizing datasets
- Preprocessing playbooks
Your first model [15 mins]
Once the datasets have been prepared, the actual “fun” can begin - experimentation is the name of the game. This section will be present an overview of the landscape together with processes that help weigh available options.
- Problem solving by pattern recognition
- Simplifying and starting simple
- Model calibration
- Launch then iterate
Integration [10 mins]
The 99.8% accurate model is not of much use, unless it can be integrated into systems meant to solve the actual problem. By constantly keeping these integration goals within sight, the aim is to convey the point that early and good enough is often better than late and perfect.
- Plumbing pipelines
- Versioning & Testing
- Production Skew
Conclusion [5 mins]
While pushing the state-of-the-art in machine learning might sound interesting, it is still important to maintain focus on its applicability within desired domains. Together with an overview of the points covered, a few concluding remarks will be presented.
Aimed at machine learning enthusiasts who wish to get started with applying machine learning techniques across various domains.
I am a member of the data science team at Semantics3 - building data-powered software for ecommerce-focused companies. Over the years, I have had the chance to dabble in various fields covering data processing, pipeline setup, database management and data science. When not picking locks, or scuba diving, I usually blog about my technical adventures at our team’s engineering blog.
- This talk is based on an earlier blogpost (https://engineering.semantics3.com/moving-machine-learning-from-practice-to-production-9c462eeef9fa) and follows discussion started from the front page of HN: https://news.ycombinator.com/item?id=12954825
- My talk on “Adventures in Postgres management”, Rootconf 2017 - https://rootconf.talkfunnel.com/2017/1-adventures-in-postgres-management
- My other technical articles - https://engineering.semantics3.com/@ramananb
- LinkedIn - https://www.linkedin.com/in/ramananbalakrishnan
- Related article by Martin Zinkevich, “Rules of Machine Learning, Best Practices for Machine Learning” - http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
- Related article by Sculley et al., “What’s your ML Test Score? A rubric for ML production systems” - https://www.eecs.tufts.edu/~dsculley/papers/ml_test_score.pdf