Managing #machinelearning 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 the poor performance of an ML system and can result in high maintenance costs and less effective utilization. In this webinar,our #devops expert Vinay Kumar will talk about some problems in tracking model experiments, deployment, how they are overcoming these problems them will the latest tools.

1. Tracking Machine learning
Experiments
Presented By: Vinay Kumar
DevOps Consultant
MachineX Intelligence

2. Our Agenda
01 MachineX Introduction
02 What is a machine learning model management?
03 How to solve the problem of model management using MLflow?
04 Tracking model experiments with mlflow
05 Demo

3. Who We Are?

4. About Knoldus MachineX
MachineX is a group of data wizards.
We are a team of Data Scientist and engineers with a product
mindset who deliver competitive business advantage.

5. What We Do?

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KSAI
Machine learning
library in scala

9. Tracking experiments: Traditional
ways

10. Using excel
sheets
Using Git or
any other VCS
Including
tracking
experiments
and
metrics

11. Challenges in Managing ML
Lifecycle

12. ML lifecycle is manual, inconsistent
and disconnected.

13. Machine Learning Model
Management

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15. Existing Solutions

17. Problem with tracking?

19. So Why Mlflow?

21. Designed to
be
useful for 1 or
10000 person
organization
Scales to big
data with
Apache Spark
Works with
any
ML library,
language &
existing cod
Platform
independent
Managing
the
development
lifecycle for
ML
Including
tracking
experiments
and
metrics

22. Demo

23. Thank You !
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