The Fifth Elephant 2019

Gathering of 1000+ practitioners from the data ecosystem

Tickets

BoF: ML Model Management

Submitted by Ravi Ranjan (@raviranjan03) via Abhishek Balaji (@booleanbalaji) on Thursday, 18 July 2019

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

View proposal in schedule

Abstract

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.

  • Krishna Durai
  • Ravishankar Babu

Comments

  • Krishna Durai (@krishnadurai) 3 months ago
    • Data lineage is important for training models: Training and Validation data need to be isolated and tracable on models trained
    • Performance of models to be compared for model understanding and promotion
    • Reproducability of operations: training, serving and validation become important
    • Compliance and Audit requirements for sensitive industries like Healthcare and Finance
    • Tooling and transparent components in architecture for better integrated workflows in model management

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