1. The document discusses Pangea, an analytics workbench that provides an end-to-end platform for building and operationalizing analytics. Pangea reduces model deployment time from months to days through its distributed architecture and intuitive drag-and-drop interface. 2. Key aspects of Pangea include zero-coding model building, single click deployment, distributed analytics at scale, and modular flexibility. It aims to drastically reduce time-to-insight through automation of the analytics lifecycle. 3. The document emphasizes best practices like the importance of data ingestion, preparation, and ontology development without emphasis on outcomes. It also stresses that analytical models must be easily deployable, regularly monitored, and have

1. 1
Analytics Life Cycle: Pangea is Panacea!

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3. Analytics Lifecycle – Enterprise View
Data Ingestion
Algorithm Selection
Model Building and Tuning
Model Monitoring
Ingest data from diverse data sources
- It is a common ask
Lesser coding and more effort on model execution
& tuning accounts for productivity
ML models need regular monitoring and
Updation on need basis unlike traditional programs
Select appropriate algorithms suitable for the
Business requirement(s) – Automated recommendation
Is definite ask
Ease of deployment and (near) real time
Scoring are crucial for enterprise acceptance
& Success
Data Preparation
Ontologies for schema preparation
& Transformation should be defined
.
Data
Ingestion
Business
Problem
Description
Data
Preparation
Algorithm
Selection
Model
Building &
Tuning
Model
Deployment
Model
Monitoring
Business
Insights &
VisualizationsBusiness Problem Description
Extract problem definition from business
Owners – Tricky often times but important
Model Deployment
Business Insights
Business insight wrappers are crucial for the
Successful adoption of analytics/ML

4. ML/DL Model Lifecycle
.
Drift
Analysis Automatic
Output
Variance
Analysis
Manual/
Supervised
Analysis
Identify
impacted
parametersRevise
Model
Parameters
Update model
Deployed
model
Monitor
Inputs
Input Analysis
Output Analysis
Drift/Newness
Error/Variance/FP/FN
Numerical Data – Distribution Analysis
Categorical Data – Obsolete/New categories
Text data – Obsolete/New Keywords
Estimate data shift @ regular intervals
Check for new/deleted categories/words
Error/variance for time/state models
FP/FN for feedback based models
Boolean/Categorical/Labels (Clusters)

5. Analytics Model Monitoring – Heuristics to Watch
Burst or patch of data causes
abrupt transition
Production data causes the
model outcome to shift/change
incrementally
Yet times data influences
gradual change in the
outcomes over a period of time
Some data sets yield recurring
change states in output
Stray incidents occur when
occasional input results in
unexpected output

6. Types of Analytical Models - Recap
Preventive and proactive
alerts and life time estimates
Unsupervised Model that
groups similar data/objects into
k - clusters
OptimizationClustering
Heuristic and OR models for
optimization
Survival
Time series based forecast
models
Supervised models that label
datasets
Classification ForecastRegression
Linear, non-linear and logistic
regression models

7. Best Practices – Analytics Adoption
Data analysis
for duplicates,
missing values
etc
Model
building,
tuning,
deployment
Model
monitoring at
regular
intervals
Reduced Time to Value
Ontologies
and schema
preparation
* These views may not expressly or implied to the affiliated organization. They are entirely speaker’s opinions based on his experience and understanding
Data ingestion
with diverse
connectors
Business logic
wrappers and
insights +
visualizations

8. Pangea* - Overview
Pangea is a distributed analytics workbench that provides an end to end platform for building and operationalizing Analytics quicker
Delivers end to end analytics with an intuitive
drag and drop of data and models/algorithms
Reduces model deployment time from several
months to days
Data & Code distribution on virtual nodes ensures
scalability
Actionable Insights
customizable solution to fit the client needs
Zero Coding Approach Single Click Deployment
Distributed Analytics at ScaleModular and Flexible
Pangea brings in automation to achieve speed, scale, collaboration and enforces best practices implementation across analytics life cycle to reduce the total cost of ownership
 Drastic time-to-insight reduction
Data Ingestion from
divergent data sources
Modelling and tuning
without coding
Inbuilt & 3rd party UI for
reports and charts
Deployment through
clicks and configuration
* HCL Internal IP/Tool

9. • Data ingestion is key without too much emphasis on ‘outcome’ at that time
• Data preparation goes hand and glove with business problem descriptions
• Ontology and/or schema preparation invisible yet inevitable step in the
enterprise analytics life cycle
• Analytics/ML Modelling without ease of deployment and monitoring are
short-lived
• Analytical models without business wrappers are only serve as PoCs
9
Summary – Pangea Best Practices