The Fifth Elephant round the year submissions for 2019
Submit a talk on data, data science, analytics, business intelligence, data engineering and ML engineering
Make a submission
Accepting submissions till 31 Dec 2020, 11:59 PM
Submit a talk on data, data science, analytics, business intelligence, data engineering and ML engineering
Accepting submissions till 31 Dec 2020, 11:59 PM
If you missed the deadline for submitting your talk for The Fifth Elephant 2019 -- to be held in Bangalore on 25 and 26 July -- you can propose a talk here.
We are accepting talks on:
##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.
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.
##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:
The criteria for selecting proposals, in the order of importance, are:
No one submits the perfect proposal in the first instance. We therefore encourage you to:
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:
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.
You must submit the following details along with your proposal, or within 10 days of submission:
Hosted by
Ravi Ranjan
@raviranjan03
Submitted Jul 15, 2019
Background
Data is the new oil and its size is growing exponentially day by day. Most of the companies are leveraging data science capabilities extensively to affect business decisions, perform audits on ML patterns, decode faults in business logic, and more. They run large number of machine learning model to produce results.
Problem Statement
Managing ML models in production is non-trivial. The training, maintenance, deployment, monitoring, organization and documentation of machine learning (ML) models – in short model management – is a critical task in virtually all production ML use cases. Wrong model management decisions can lead to poor performance of a ML system and can result in high maintenance cost and less effective utilization. Below are the key concern for model management:
Existing Solution
There are custom ML platform to address the above concerns such as FBLearner by Facebook and Michelangelo by Uber but they have their own limitations like:
Why MLflow?
Databricks team found above concerns as their motivation to develop MLflow as an open source and cloud agnostic machine learning model management platform. Benefits of MLflow from machine learning model management:
Key focus area for Machine Learning Model Management with MLflow:
Basic understating of machine learning and its workflow
Ravi Ranjan is working as Senior Data Scientist at Publicis Sapient. He is part of Centre of Excellence and responsible for building machine learning model at scale. He has worked on multiple engagements with clients mainly from Automobile, Banking, Retail and Insurance industry across geographies. In current role, he is working on Hyper-personalized recommendation system for Automobile industry focused on Machine Learning, Deep learning, Realtime data processing on large scale data using MLflow and Kubeflow.
He holds Bachelor degree in Computer Science with proficiency course in Reinforcement Learning from IISc, Bangalore.
https://drive.google.com/open?id=19fVbkGPGZrc973JVYOZvMCxaDjG78nIA
Accepting submissions till 31 Dec 2020, 11:59 PM
Hosted by
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