Jul 2019
22 Mon
23 Tue
24 Wed
25 Thu 09:15 AM – 05:45 PM IST
26 Fri 09:20 AM – 05:30 PM IST
27 Sat
28 Sun
Make a submission
Accepting submissions till 15 Jun 2019, 01:00 PM
##The eighth edition of The Fifth Elephant will be held in Bangalore on 25 and 26 July. A thousand data scientists, ML engineers, data engineers and analysts will gather at the NIMHANS Convention Centre in Bangalore to discuss:
##Highlights:
1. Meet Peter Wang, co-founder of Anaconda Inc, and learn about why data privacy is the first step towards robust data management; the journey of building Anaconda; and Anaconda in enterprise.
2. Talk to the Fulfillment and Supply Group (FSG) team from Flipkart, and learn about their work with platform engineering where ground truths are the source of data.
3. Attend tutorials on Deep Learning with RedisAI; TransmorgifyAI, Salesforce’s open source AutoML.
4. Discuss interesting problems to solve with data science in agriculture, SaaS perspective on multi-tenancy in Machine Learning (with the Freshworks team), bias in intent classification and recommendations.
5. Meet data science, data engineering and product teams from sponsoring companies to understand how they are handling data and leveraging intelligence from data to solve interesting problems.
##Why you should attend?
##Full schedule published here: https://hasgeek.com/fifthelephant/2019/schedule
##Contact details:
For more information about The Fifth Elephant, sponsorships, or any other information call +91-7676332020 or email info@hasgeek.com
#Sponsors:
Sponsorship Deck.
Email sales@hasgeek.com for bulk ticket purchases, and sponsoring 2019 edition of JSFoo:VueDay.
#Platinum Sponsor
#Community Sponsors
#Exhibition Sponsors
#Bronze Sponsor
#Community Sponsors
Hosted by
Ravi Ranjan
@raviranjan03
Submitted Jul 18, 2019
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.
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:
Computational challenges: machine learning model definition and validation, decisions on model retraining, adversarial settings.
Data management challenges: lack of a declarative abstraction for the whole ML pipeline, querying model metadata, model interpretation.
Engineering challenges: multiple tools and frameworks make integration complex, heterogeneous skill level of users, backwards compatibility of trained Models and hard to reproduce the training result.
~From Ravi Ranjan’s proposal: https://hasgeek.com/fifthelephant/2019/proposals/machine-learning-model-management-with-mlflow-abVkXSgaAvMLgxkR2vD4Ho
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.
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