In Assignment 1, the integration of the Industry Prediction ML model was successfully achieved in the TALENTX backend, ensuring alignment with business needs and quality standards, while validation scenarios and sampling criteria were established for thorough verification. Assignment 2 involved creating comprehensive user stories, facilitating the Engineering team's integration of the qualified model into the backend with consideration for scalability, scheduling, and monitoring, laying the foundation for future customer-facing applications. Assignment 3 explored the potential of Industry insights to provide enhanced value to TALENTX, envisioning possibilities such as benchmarking, skill gap analysis, compensation trends, and more, bolstering the company's data-driven talent decision-making capabilities.

1. Assignment 1
PRASANNA HEGDE
Integration of Industry
Prediction ML Model

2. Introduction
Structure
• ML Model Integration Architecture
• ML Model Technical Specification
• Success Criteria and Validation
Introduction
• The Industry Prediction ML Model is a valuable tool that
can help businesses make informed decisions about talent
acquisition and development.
• The integration of the model into the TALENTX backend
will make it easier for businesses to use the model and
reap the benefits of its insights.

3. Integration Strategy
Input Data
The ML model requires specific
company attributes as input to
make predictions. Key attributes
include job title, job description,
company location, and historical
performance metrics.
Model Architecture
The ML model for industry prediction is built upon a state-
of-the-art deep learning architecture, leveraging a multi-
layered neural network. This sophisticated architecture
enables the model to effectively learn intricate patterns and
relationships within the input data, resulting in accurate
industry value predictions.
API Integration
To facilitate seamless integration
with the TALENTX backend, the
model's functionalities will be
exposed through RESTful APIs.
Data Preprocessing
Data Cleaning: Prior to feeding data into the model, a meticulous data cleaning pipeline
will be implemented. This pipeline will handle missing values, remove duplicates, and
address any inconsistencies in the company attributes.
Data Encoding: As the ML model processes numerical data, categorical features like
job titles and company locations will undergo encoding using techniques such as one-
hot encoding or label encoding.
Model Hosting
To enable real-time predictions, the trained
ML model will be deployed on resilient cloud-
based servers. Cloud hosting provides the
necessary scalability and flexibility, ensuring
seamless handling of varying prediction loads
as demand fluctuates.
Output
The ML model generates industry
predictions for individual
companies based on their
attributes and historical data,
providing a forecasted industry
value as the output.

4. Technical Specifications
The ML model for industry prediction is built upon a state-
of-the-art deep learning architecture, leveraging a multi-
layered neural network.
Model Architecture
The ML model is implemented to handle one company at a
time for prediction. This means that given the specific
attributes of a company, the model will process this data
and return the predicted industry value for that particular
company.
Single Company Prediction
The ML model is trained using the labeled dataset, and the
multi-layered neural network learns patterns and
relationships between input features and the industry
category.
Model Training
The ML models implemented predict key performance
indicators (KPIs) in the labor market landscape,
particularly focusing on the industry association of
companies.
The predictions encompass various KPIs, including:
• Industry Demand Prediction
• Skill Gap Analysis
• Salary and Compensation Insights
• Geographical Talent Trends
• Workforce Diversity Analysis
• Job Market Competition
• Skill Supply Forecasting
• Job Market Sentiment Analysis
KPIs for Prediction
The deep learning architecture may have various
hyperparameters that control its behavior, such as the
number of layers, learning rate, batch size, etc. Proper
tuning of these hyperparameters is essential to achieve
optimal performance.
Hyperparameter Tuning
To ensure the model's quality, it is evaluated on a separate
validation dataset to measure its performance metrics,
such as accuracy, precision, recall, or F1 score. This
evaluation helps determine how well the model generalizes
to unseen data.
Model Evaluation
As the talent landscape evolves and new data becomes
available, retraining the ML model helps it adapt to
changing trends and patterns.
Model Retraining

5. Success Criteria and Validation
Success Criteria
• Accuracy: Model success criteria involve
accurate predictions with a low error rate,
measured using the Mean Absolute Error
(MAE), which calculates the average difference
between the model's predictions and the actual
values.
• Robustness: The model's robustness to data
changes and avoidance of overfitting will be
assessed through holdout validation, where the
model is tested on a separate dataset not used
during training.
• Interpretability: Interpretability is achieved by
adopting a transparent machine learning
approach, making the model's parameters
easily understandable and explainable,
ensuring users can comprehend its predictions.
Validation Scenarios
• Holdout validation: A portion of the dataset is set
aside as a holdout dataset, not used during model
training, to test its generalization on unseen data.
• K-fold cross-validation: K-fold cross-validation
reduces variance by dividing the dataset into K
subsets and training the model K times, using
different subsets for validation in each iteration.
• Stratified sampling: To ensure representativeness,
data is sampled proportionally across industries to
validate the model's performance.

6. Next Steps
The following are the key action items and next steps to proceed with the integration of the Industry Prediction ML Model:
1. Finalize the integration strategy: This includes defining the data sources, data preparation steps, and model hosting and deployment details.
2. Validate the model: This involves using a holdout dataset and sampling criteria to measure the model's accuracy, robustness, and interpretability.
3. Improve the model: This involves addressing any areas where the model is not performing well.
4. Deploy the model: This involves making the model available to users through APIs.
5. Monitor the model: This involves ongoing monitoring to ensure the model maintains accuracy over time.

7. ASSIGNMENT 2
PRASANNA HEGDE
User Stories for Engineering Team

8. Introduction
The purpose of this assignment is to develop user stories for the Engineering team to
integrate the ML model into the TALENTX backend. The user stories will specify the
business and technical specifications that the team needs to build the integration and
deploy the model to production.
To ensure that user stories consider scheduling, scalability, and monitoring
aspects, we need to specify the frequency with which the model should be
updated, the number of users who will be using the model, and the metrics
that will be used to monitor the model's performance.
Purpose
Ensuring that User Stories Meet the Needs of
the Stakeholders

9. User Stories
01
02
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Data Ingestion and Integration
• As a data engineer, I want to establish seamless data
ingestion pipelines to fetch real-time job data from various
sources.
• The pipeline should support data from multiple formats,
such as CSV and JSON, and efficiently integrate it into our
big data ecosystem.
ML Model API Integration
• As a backend developer, I want to develop robust APIs for
smooth communication between the ML model and the
TALENTX platform.
• The API should handle high volumes of concurrent requests
and ensure low-latency responses for real-time predictions.
• The API endpoints should be secure and scalable to
accommodate future enhancements.
Scalability and Load Testing
• As a systems architect, I want to ensure the ML model's
scalability and performance during peak usage periods.
• The system should undergo load testing to handle an
increased user load and maintain response times within
acceptable thresholds.
04
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Real-time Monitoring and Alerting
• As a DevOps engineer, I want to implement real-
time monitoring and alerting mechanisms to track
system health and performance.
• The monitoring system should provide actionable
insights and alerts in case of any anomalies or
performance degradation.
Model Deployment and Versioning
• As a data engineer, I want to deploy the qualified
ML model into the production environment
seamlessly.
• The deployment process should support model
versioning for easy rollback and updates.

10. Acceptance Criteria
ML Model API Integration
• The API must handle a minimum of 1,000 concurrent requests.
• The API must have a response time of 100 milliseconds or less.
• The API must be secured with authentication mechanisms.
• The API must be scalable to handle increased user loads.
Scalability and Load Testing
• The system must be able to handle a load of 10,000 concurrent users.
• The system must maintain response times within acceptable thresholds.
• The system must be load tested to verify its scalability.
• The system must be monitored to track its performance.
Data Ingestion and Integration
• The data ingestion pipeline must fetch real-time job data from various
sources.
• The data must be successfully ingested and transformed into a unified
structure.
• The pipeline must be scheduled to update every 30 minutes.
• The pipeline must be scalable to handle large volumes of data.
Real-time Monitoring and Alerting
• The monitoring system must provide real-time performance
metrics.
• The monitoring system must alert the team in case of anomalies.
• The monitoring system must be scalable to handle increased
traffic.
• The monitoring system must be able to track performance over
time.
Model Deployment and Versioning
• The ML model deployment process must support versioning.
• Model updates must be deployed seamlessly.
• Model updates must be scheduled to avoid disrupting the
platform.
• Model performance must be tracked over time.

11. Deployment Strategy
Deployment Strategy
The ML model will be deployed in a production environment using
a CI/CD pipeline and will be scheduled to be updated on a regular
basis.
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Monitoring and Health Checks
The system will be monitored in real-time to identify
potential bottlenecks or anomalies, and regular health
checks will verify the system's components and ensure
smooth operations.
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Deployment Process
The deployment process will involve a series of well-
defined steps to ensure a smooth transition from
development to the production environment.
Alerting and Incident Response
Alerts will be set up to notify the Engineering team in case
of any system irregularities or performance degradation,
and an incident response plan will be in place to swiftly
address and resolve any issues that may arise.
Scalability and Load Balancing
The ML model will be deployed on a scalable cloud
infrastructure with load-balancing capabilities to
accommodate increasing user demand.
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Rollback and Versioning
The deployment strategy will include the ability to roll back to
a previous version of the ML model if unforeseen issues
occur, and model versioning will enable tracking of changes
and improvements over time.

12. Conclusion
• In conclusion, the integration of the Industry Prediction ML Model into the TALENTX backend is a significant step towards empowering our platform with
valuable talent insights.
• User stories serve as a vital bridge between business requirements and technical implementation, ensuring a successful integration.
• By addressing scheduling, scalability, and monitoring aspects within the user stories, we guarantee a robust and efficient integration process.
• The deployment strategy emphasizes smooth transitioning, scalability, monitoring, and incident response for a reliable ML model deployment.

13. ASSIGNMENT 3
Prasanna Hegde
Additional Insights Enabled with Industry
Information

14. INTRODUCTION
14
1 Leading Platform Insights
As a leading talent intelligence platform, TALENTX already provides critical talent insights to empower business leaders in making
informed decisions.
2 Untapped Potential
In this assignment, we explore the untapped potential of industry information and its ability to complement the existing job
data, adding significant value to TALENTX's portfolio.
3 Enabling Additional Insights
The key question we seek to address is: With Industry information in place, what other insights can be enabled to further enhance
TALENTX's offerings and provide deeper, more comprehensive talent intelligence?

15. INSIGHTS LIST AND USE CASES
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RemoteWorkTrends
Analyzing the adoption of remote work practices
in different industries to understand their
readiness for remote work and support flexible
work arrangements.
SkillResilience
Assessing the resilience of industry-specific skills
in the face of technological advancements and
economic changes to guide talent development
strategies.
JobGrowthProjections
Predicting the future job growth rates for specific
industries to inform workforce planning and
identify potential areas of expansion.
TalentChurnAnalysis
Studying talent turnover and mobility within
industries to devise retention strategies and
address attrition challenges.
EmployeeEngagement
Measuring employee engagement levels in
different industries to enhance workplace
productivity and employee satisfaction.
WorkforceDiversityImpact
Assessing the impact of diverse workforces on
innovation and company performance within
industries to promote diversity initiatives.
JobMarketSentiment
Analyzing sentiment analysis of job market data
to understand the perception of specific
industries among job seekers and
professionals.
EmergingTechnologies
Identifying emerging technologies and their
adoption rates within industries to anticipate
future skill demands and foster innovation.

16. TARGET AUDIENCE AND IMPLEMENTATION
Target Audience:
Business Leaders
• The insights derived from industry
information are strategically
targeted at business leaders.
• These leaders, including HR
executives, talent acquisition
managers, and C-suite
executives, can leverage these
insights to make well-informed
talent decisions.
Implementation within
TALENTX Platform
• The insights will be seamlessly integrated
into the TALENTX platform's existing
talent intelligence offerings.
• Leveraging the platform's big data
capabilities, machine learning algorithms,
and industry taxonomy, the insights will
be presented in intuitive and actionable
visualizations.
Benefits for Targetted
Business Leaders
• Comprehensive and real-time talent
intelligence.
• Data-driven decision-making across
industries.
• Strategic talent acquisition and
development.
• Industry-specific talent benchmarking.
• Fostering diversity and inclusion
initiatives.
• Rapid adaptation to market changes.

17. IMPACT ON DECISION-MAKING AND ROADMAP
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• Data-Driven Talent Decisions: Business leaders can make well-
informed talent decisions based on accurate and real-time industry-
specific insights.
• Proactive Talent Strategies: Anticipate talent market shifts and
proactively align talent acquisition and development strategies with
industry trends.
Insight-Driven Talent Roadmap
• Phase 1: ML Model Implementation - Develop and deploy the ML model for
industry prediction, leveraging a state-of-the-art deep learning architecture to
provide accurate industry value predictions.
• Phase 2: Integration with TALENTX - Integrate the ML model seamlessly into
the TALENTX platform through RESTful APIs, enabling easy access and
utilization of the industry predictions for talent acquisition and development
strategies.
• Phase 3: Additional Insights - Enhance the ML model to predict key
performance indicators (KPIs) in the labor market landscape, including
industry demand, skill gaps, salary insights, geographical talent trends,
workforce diversity, etc.

18. CONCLUSION
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• Unlocking Additional Insights: The presentation has demonstrated the immense potential of industry information in unlocking valuable additional insights for
TALENTX.
• Targeted at Business Leaders: These insights are strategically tailored for business leaders, providing them with a data-driven edge in talent decision-making.
• Seamless Implementation: The insights will be seamlessly integrated into the TALENTX platform, enriching the talent intelligence offerings.
• Empowering Talent Decisions: Business leaders will be empowered to make well-informed talent decisions with real-time and industry-specific insights.
• A Step Towards the Future: This integration marks a significant step forward in providing cutting-edge talent intelligence to our valued clients.