BoF: ML Model Management

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

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Accepting submissions till 15 Jun 2019, 01:00 PM

NIMHANS Convention Centre, Bengaluru

Tickets

Pinned update

The Fifth Elephant Winter edition starts at 9:30 am; live stream for members This update is for participants only

##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:

Model management, including data cleaning, instrumentation and productionizing data science.
Bad data and case studies of failure in building data products.
Identifying and handling fraud + data security at scale
Applications of data science in agriculture, media and marketing, supply chain, geo-location, SaaS and e-commerce.
Feature engineering and ML platforms.
What it takes to create data-driven cultures in organizations of different scales.

##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?

Network with peers and practitioners from the data ecosystem
Share approaches to solving expensive problems such as cleanliness of training data, model management and versioning data
Demo your ideas in the demo session
Join Birds of Feather (BOF) sessions to have productive discussions on focussed topics. Or, start your own Birds of Feather (BOF) session.

##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.

JSFoo:VueDay 2019 sponsors:

#Platinum Sponsor

#Community Sponsors

#Exhibition Sponsors

#Bronze Sponsor

#Community Sponsors

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The Fifth Elephant

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

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BoF: ML Model Management

Submitted Jul 18, 2019

Session type: BOF session of 1 hour Session type: Birds of a Feather session of 1 hour

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

Outline

Managing ML models in production is non-trivial. What are the challenges and concerns of machine learning management lifecycle?
What is machine learning model management?

Requirements

Basic understating of machine learning and its workflow

Speaker bio

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.