Aniket Deshpande

Aniket Deshpande

Data Scientist & Machine Learning Engineer

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AWS Machine Learning Specialty

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Aniket Deshpande

Projects

LLM NLP App — API + UI on Hugging Face Spaces

LLM NLP App

Modular FastAPI + Gradio stack delivering text summarization & rewrite with transformer models. Fully automated deploys to two Spaces via GitHub Actions.

FastAPIGradioTransformersCI/CD
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What I built

  • Separate API and UI services with health checks, env validation, and smoke tests.
  • Automated CI/CD: push → build → deploy to Spaces; blue‑green style swap on success.
  • Fallback routing (local models → OpenRouter/OpenAI) with graceful degradation.

Links

Live API Live UI View Code
Stockio — forecasting & sentiment dashboard

Stockio

Interactive stock dashboard with fast price forecasts, news sentiment, and sanity checks. Supports multi‑ticker comparisons.

StreamlityfinanceProphet/ARIMAPlotly
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What I built

  • Cache‑aware pipelines for fetching quotes & generating short‑horizon forecasts.
  • Forecast sanity guardrails (bounds, trend checks) to curb over‑confident outputs.
  • Clean UX with historical chart, forecast table, and quick filters.

Links

Live Demo View Code
Minimal free-tier RAG QA over SEC filings

RAG‑QA

CPU‑only retrieval‑augmented QA using MiniLM, FAISS, extractive QA. Automates EDGAR ingestion for Risk/MD&A sections and returns citation‑grounded answers.

FAISSSentence‑TransformersEDGARExtractive QA
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What I built

  • End‑to‑end ingest → chunk → embed → retrieve → answer pipeline with citations.
  • No paid APIs, this runs on MiniLM encoder + RoBERTa SQuAD2.
  • CLI with ticker input, doc caching, and top‑k controls.

Links

View Code
Drift-aware fraud detection system

Fraud Detection (Drift‑Aware)

Recall‑optimized fraud classifier with prequential (time‑ordered) eval and drift tracking (PSI). Compares static vs adaptive update policies.

Imbalanced LearningRecall@FPRPSI DriftScikit‑learn
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What I built

  • Baselines (LR, RF) + threshold tuning to maximize Recall@FPR for review teams.
  • Prequential splits to simulate streaming; drift quantified via PSI.
  • Optional updating (periodic / drift‑triggered / sliding window) for stability.

Links

View Code
Speech Emotion Recognition MFCC + CNN

Speech Emotion Recognition (MFCC & CNN)

Audio -> MFCC feature maps -> CNN classifier with real‑time prediction via mic or .wav upload.

LibrosaMFCCPyTorchGradio
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What I built

  • Signal preprocessing & augmentation; class‑weighted training loop.
  • Confusion‑matrix‑driven error analysis to separate similar emotions.
  • Light Gradio UI for quick demo.

Links

View Code

Resume

Click below to view or download my resume, or browse the summary below.

View Resume (PDF)

⚡ Skills

Programming & ML
PythonRC++ NumPyPandasscikit‑learn PyTorchTensorFlowXGBoostLightGBM
LLMs & NLP
TransformersHugging FaceLangChain RAGFAISSPrompt Engineering Text SummarizationSentiment Analysis
MLOps & Cloud
AWS (S3, Lambda, API Gateway, CloudWatch)SageMaker DockerGitHub ActionsTerraform (IaC) CI/CDMonitoring & Logging
Data & Analytics
EDAFeature Engineering Anomaly DetectionTime‑series Forecasting Drift (PSI)Calibration AB TestingSQL
Web & Visualization
FastAPIFlaskGradioStreamlit PlotlyMatplotlibPower BITableau

💼 Experience

  • Instructional Design Assistant — EdPlus @ ASU Aug 2024 – Present · Scottsdale, AZ
    • Analyzed 10K+ learner interactions to surface insights that improved course outcomes.
    • Built datasets and pipelines for machine learning experiments in collaboration with a cross-functional QA team.
    • Supported A/B testing and contributed to building AI solutions to automate Canvas course page error detection and scoring, helping guide course design decisions for 60+ courses.
  • Data Analyst Intern — Suzlon Energy May 2021 – Aug 2021 · Pune, India
    • Developed predictive models for wind turbine RUL; improved detection accuracy to ~95%.
    • Preprocessed high-volume SCADA data; optimized features for time-series modeling.
    • Built Tableau dashboards to monitor anomalies and KPIs across turbines.

🎓 Education

  • M.S. in Data Science — Arizona State University · Tempe, AZ
  • PG Diploma in AI/ML — MIT World Peace University · Pune, India
  • B.C.A. in Computer Applications — MIT World Peace University · Pune, India

🚀 Projects

  • NLP App: Sentiment/Emotion/Text Generation using GPT-2 & BERT; Docker + FastAPI.
  • Speech Emotion Recognition (SER): MFCC + spectrogram features → hybrid CNN-RNN; error analysis with confusion matrix for robust emotion classification.
  • Stock.io (Realtime Prediction):Streamlit app: finance news sentiment (web-scraped, 3-day window) + LSTM on OHLC for dual stock recommendations.
  • Predictive Maintenance: Time-series models for turbines; anomaly detection & alerts (Suzlon)

🏅 Certifications

  • HarvardX — High-Dimensional Data Analysis
  • IBM — Data Science / NLP
  • AWS — Machine Learning Specialty (ML‑SC01) Verify
  • AWS — Cloud Practitioner (coursework)
  • Infosys — Applied NLP / Cloud

About Me

My path into data science, from hands-on projects to graduate research and machine learning at scale.

  1. BCA

    🎓 Bachelor of Computer Science/Applications

    I began with a Bachelor of Computer Applications (programming, DSA, databases). My capstones and an internship at Suzlon sparked my interest in applied data.

    • Internship @ Suzlon: predictive modeling for turbine health, time-series preprocessing, dashboards.
    • GPA: 3.8 / 4.0
  2. PGD

    🧠 Post Graduate Diploma in AI/ML

    As AI accelerated, I completed a PG Diploma in Artificial Intelligence and machine learning and assisted on an NLP research capstone in Speech Emotion Recognition (SER), getting hands-on experience with transformers and modern NLP.

    • Capstone: SER using deep learning & NLP.
    • GPA: 3.7 / 4.0
  3. MS

    📚 M.S. in Data Science, Analytics and Engineering (ASU)

    I’m currently in my last semester of M.S. at Arizona State University (CGPA: 3.8), aiming for ~4.0 by graduation. Work spans ML systems, statistical modeling, big-data analytics, and cloud.

    • Now: building ML pipelines & model evaluations; supporting A/B testing and analytics.

📘 Relevant Coursework — ASU

Data Mining (CSE 572) Statistics for Data Analytics (DSE 501) Analyzing Big Data (IFT 511) Design of Experiments (IEE 572) Advanced DB Management (IFT 530) Data Visualization (CSE 578) Prob. & Random Processes (EEE 554) Probability & Stats for Eng (IEE 380) Data Driven Optimization (CSE 506)

Focused on ML, statistics, AI, Algorithms, data engineering, and scalable analytics.

🧠 Relevant Coursework — PGD AI/ML & B.C.A.

Machine Learning Deep Learning Natural Language Processing Data Structures & Algorithms Database Systems Software Engineering

🌄 Outside Work

  • Swimming & trekking
  • Photography
  • Making explainer videos on new tech

📫 Say Hello

I hope you enjoyed my portfolio. Feel free to reach out, always happy to talk.

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