Machine learning models are only as good as the quality of data and the size of datasets used to train the models. Data has shown that data scientists spend around 80% of their time on preparing and managing data for analysis and 57% of the data scientists regard cleaning and organizing data as the least enjoyable part of their work. This further validates the idea of MLOps and the need for collaboration between data scientists and data engineers.

1. Model Experiments Tracking and
Registration using MLflow on
Databricks
Dash Desai
Director Of Platform And Technical Evangelism, StreamSets
dash@streamsets.com | @iamontheinet | https://www.linkedin.com/in/dash-desai/

2. Agenda
Overview
The perfect recipe for building machine learning
model experiments comes from automating tasks
for data acquisition, preparation and being able to
track model experiments.
Hands-On Demo
Hands-on demo of automating these crucial tasks
using StreamSets and MLflow on Databricks.
Find Out More
Join me for “StreamSets Live: Demos with
Dash.”
https://bit.ly/DemosWDash

3. Data Acquisition And Preparation
▪ 80% of the data scientist's time is spent acquiring and preparing the
data
▪ Source: Infoworld https://www.infoworld.com/article/3228245/the-80-20-data-science-dilemma.html
▪ Access to data is controlled by constrained teams
▪ The Dice 2020 Tech Job Report suggests data engineer was the fastest growing job in technology with a 50% year-over-year growth in
the number of open positions.
▪ Source: Smith Hanley https://www.smithhanley.com/2020/06/11/data-engineers-more-in-demand-than-data-scientists/
▪ Ability to experiment on large datasets
▪ “In machine learning, is more data always better than better algorithms?” - Banko and Brills
▪ Source: https://courses.cs.cornell.edu/cs674/2004sp/materials/banko-brill-acl2001.pdf

4. Model Experiments, Tracking, And Registration
▪ Precursor to model development
▪ Model experiments in a rapid, iterative manner
▪ Lack of industry standards
▪ Manual versioning and tracking models, inputs, hyperparameters
▪ Long model deployment/release cycles
▪ Hinders adoption to dynamic changes, gain competitive advantage
▪ Compliance with changing governance and regulations

5. Automation - StreamSets
Data Acquisition, Preparation, Model Experimentation

6. ▪ An open source platform for
end-to-end machine learning
lifecycle
▪ Modern data integration platform for
building smart data pipelines
▪ Easy to build; Self-serve
▪ Cloud and platform agnostic
▪ 100s of connectors
▪ Easy to scale and port
▪ Extensible and resilient
▪ Built-in orchestration and automation
▪ Unified data analytics platform
▪ Fully managed Apache Spark
and MLflow
Automation - MLflow | Databricks | StreamSets
Data Acquisition, Preparation, Model Experimentation

7. Automation - StreamSets Transformer
Data Acquisition, Preparation, Model Experimentation

8. Automation - StreamSets Transformer
Data Acquisition, Preparation, Model Experimentation

9. Automation - StreamSets Transformer
Data Acquisition, Preparation, Model Experimentation

10. Automation - StreamSets Transformer
Data Acquisition, Preparation, Model Experimentation

11. Automation - StreamSets Transformer
Data Acquisition, Preparation, Model Experimentation

12. Automation - StreamSets Transformer
Data Acquisition, Preparation, Model Experimentation

13. Hands-On Demo
● Review data acquisition and preparation pipelines
● Run model experiments pipelines
● How to build pipelines

14. Feedback
Your feedback is important to us.
Don’t forget to rate
and review the sessions.

15. Thank You!
Dash Desai
Director Of Platform And Technical Evangelism
dash@streamsets.com | @iamontheinet | https://www.linkedin.com/in/dash-desai/