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Esha Poddar

start

Personal Projects

Future Goals

Thesis

Work Projects

About Me

Index

Skills

  • Versatile Technologist: Proficient in Python, SQL, Terraform, Docker, Kubernetes.
  • Cloud & DevOps: Experienced with Google Cloud, AWS, Jenkins, Airflow, Terraform.
  • Data Visualization Expert: Google Data Studio, Tableau, Power BI.
  • Version Control: Git & Git Workflows.

Data Professional | Tech Enthusiast LinkedIn: Esha Poddar Github: eshapoddar Email: eshapoddar@gmail.com | Phone: +44 07768282853

Esha Poddar

About Me

01

Work Projects

  • Engineered a Continuous Integration/ Continuous Deployment (CICD) ETL pipeline.
  • Designed specifically for a retail chain, leveraging the power of BigQuery.
  • Streamlines data extraction, transformation, and loading processes for retail analytics and insights.
  • Developed an Infrastructure as Code (IaC) platform catering to researchers' needs.
  • Enables seamless setup of cloud environments tailored to specific research topics.
  • Automates infrastructure setup, including VM provisioning and Jupyter Notebook configuration based on chosen datasets.

CICD ETL Pipeline for Retail Chain:

Infrastructure as Code for Research Environments:

Product/Services

IMPLEMENTATION OF MLOPS USING NO-CODE AI PLATFORMS

02

Masters Thesis

+ info

MLOps, an amalgamation of Machine Learning (ML) and Operations (Ops), streamlines the development and deployment of ML models at scale. No-Code AI Platforms: These platforms empower users to create and deploy ML models without coding expertise. They offer intuitive interfaces and drag-and-drop functionalities, aiming to democratize AI by engaging diverse backgrounds in ML model development.

Recent AI advancements have transformed industries, yet the complexity of ML model development restricted accessibility to a specialized group. Traditional ML deployment demanded technical expertise, limiting its reach to data scientists and experts.

Democratizing AI: Integrating No-Code Platforms with MLOps

Preface of the thesis

Situation

Comparison

Task: Integrating no-code AI platforms into the realm of MLOps Goal: To significantly enhance the usability and accessibility of MLOps for individuals without extensive technical expertise. Significance: By achieving this objective, we pave the way for democratizing AI and widening its accessibility beyond the confines of technical experts.

Enhancing Accessibility in MLOps

Objective

Manual configuration of these cloud resources can pose challenges for users unfamiliar with GCP

Infrastructure Setup for ML Pipelines:

1. A Google Cloud Platform project2. Enabling the required APIs3. Service Account4. A Cloud Storage bucket5. A Vertex AI dataset

Seamless end-to-end MLOps workflows without extensive technical specialization.

  • Integration of Terraform and Google Cloud’s Vertex AI suite.
  • Initiating AutoML jobs for iterative tasks
  • Programmatic deployment of generated models to endpoints.

Liberation from manual infrastructure management for practitioners.

  • Leveraging Terraform to establish foundational infrastructure for ML pipelines.
  • User-initiated configurations to create and configure Google Cloud resources.

No-Code Orchestration of ML Pipelines:

Automated Infrastructure Provisioning using IaC:

Infra-as-Code & Automation

Implementation

6 participants recruited, representing ML enthusiasts without cloud or ML expertise.Between-subjects methodology employed for three machine learning models using Terraform and Vertex AI AutoML.

Test ease of usability

Models deployed to endpoints enable real-time or batch predictions.Vertex AI allows synchronous or asynchronous requests for predictions.

Endpoint Deployment & Testing

Predictions

  • Precision
  • Recall
  • AuPRC
  • Log loss
  • Confidence Threshold
  • Confusion Matrix.
These metrics provide insights into model performance and behavior.

User Study

Metrics available in Vertex AI

Model Evaluation

Model Evaluation and User Study

Majority lacked prior cloud or ML experience.Participants reported satisfaction in setting up and using the MLOps pipeline.Ratings clustered around "Satisfied" and "Very Satisfied".

Results showcase substantial enhancements in usability and user satisfaction validated through a user study. Statistical analysis and user feedback affirm the success of the integration.

Achieved Outcomes: Usability & Satisfaction

Results

  • Strong statistical significance with a t-value of 22.010.
  • Very low p-value (< .001) rejects the null hypothesis.
  • Participants' perceptions significantly differ from neutral usability.

One Sample T-Test:

  • Median & mean SUS scores: 83.750, indicating usability.
  • Moderate standard deviation suggests consistent responses.
  • Normal distribution confirmed by Shapiro-Wilk test.

Descriptive Statistics:

Statistical Analysis of SUS Score:

Long Term

Medium Term

Short Term

  • Address ethical considerations in AI deployment.
  • Add a web faced interface for users to limit further cloud interaction
  • Personalize integrations for specific use-cases.
  • Explore streaming data pipeline integrations.
  • Integrate IaC with Cloud build
  • Scale the system and optimize performance.

Limitations exist, but future work can expand on scalability, transparency, real-time models, and customization, enhancing accessibility and democratization of AI.

Future Work

I'm eager to expand horizontally, diving into different facets of cloud and data engineering.My goal is to master various tech stacks and frameworks, creating a colorful palette to craft innovative and adaptive solutions.I aim to evolve into a mentor and guide, fostering an environment where everyone thrives on sharing knowledge and creative ideas.My ambition is to architect not just robust solutions but also sustainable ones, ensuring scalability and environmental mindfulness.I dream of leveraging technology as a force for social good, channeling my skills toward solutions that tackle real-world challenges.

Goals and Aspirations

Q & A

Quantitative (SUS ratings) and qualitative (thematic analysis) data collected. Quantitative data used to gauge overall system usability. Thematic analysis used to categorize qualitative responses for insights and improvements.

Data Collection & Analysis: