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Hi there, I'm Shailesh Kumar 👋!

Who I am?

  • A Machine Learning / GenAI Engineer based in India.
  • Working as Lead Data Scientist at Katonic AI.
  • I love math, programming, data science, and books.
  • Creator of ExplainIt, an opensource package for drift detection & data quality management.
  • Open Source Enthusiast.
  • See my portfolio at shaileshkumar97.github.io.

What I'm doing?

  • Writing Python, SQL, HTML/CSS, PostgreSQL, MySQL, Redis etc...
  • Contributing to Open Source.
  • Mostly active on LinkedIn.
  • Currently, building end-to-end production ready generative ai assistants/agents to handle different types of knowledge base for variety of usecases.
  • Previously, built an end-to-end production pipeline for processing short videos with different usecases.

What are my skill sets?

  • 🎛 Machine Learning Operations (MLOps):

    • Language: PythonSQL
    • Framework: MlflowKubeflowElyraDashFastAPIStreamlit
    • Databases: PostgreSQLMySQLRedisSnowflake
    • Concepts: Data PipelineFeature StoreData GovernanceModel PipelineModel DeploymentApp DeploymentModel MonitoringDrift DetectionModel Explainability
  • 👨‍💻 Python Developer:

    • Open Source Projects:

      • Explainit: A modern enterprise-ready business intelligence web application SDK for Drift Detection, Monitoring & Data Quality Management.
    • In-House SDK: (for katonic.ai)

      • Feature Store: To manage end-to-end life-cycle of features & integrate with existing data stores, feature pipelines, data governance, and ML platforms.
      • Connectors: To access the data from different databases/warehouses and stores to a given destination.
      • FileManager: To access, store and update/manipulate objects within the katonic file browser.
      • Pipeline: To convert an existing notebook into a Kubeflow pipeline.
      • AutoML: To build, Train & Log different Machine Learning, Deep Learning models.
      • Log: To quick register the trained models with mlflow in platform for deployment to the production environment.
  • 🧮 Machine Learning:

    • Language: PythonSQL
    • Framework: Scikit-LearnXgboostCatboostPandasPlotlyMatplotlibPyspark
    • Databases: PostgreSQLMySQLPostgreSQL
    • Big Data: SparkData Lake (Delta, Hudi, Hive)
    • Protocol: REST
  • 🤖 Deep Learning:

    • Language: Python
    • Framework: PyTorchTensorflowKerasOpenCVLibrosa
  • 🗄️ Backend:

    • Language: Python
    • Framework: FastAPIFlaskStreamlit, Dash
    • Databases: PostgreSQLMySQLAWS S3RedisSnowFlake
    • System Architecture: MonolithicModular
    • Protocol: REST
  • 🖥 Frontend:

    • Language: HTMLCSSPython
    • Framework/Library: Dash, Streamlit
    • Utils: BootstrapModular CSS
  • 🎡 Ecosystem:

    • Containerization: Docker
    • Version Control: GitGitHub
    • CI/CD: Github Actions
    • Project Management: GitHub Projects

How to reach me?

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Shailesh Kumar's GitHub stats

Shailesh Kumar's Projects

awesome-llm icon awesome-llm

Awesome-LLM: a curated list of Large Language Model

chatql icon chatql

ChatQL: Querying Databases Through Conversation with Power of LLMs

llm-course icon llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

mocapnet icon mocapnet

We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance

mooc-coursera-advanced-machine-learning icon mooc-coursera-advanced-machine-learning

Content from Coursera's ADVANCED MACHINE LEARNING Specialization (Deep Learning, Bayesian Methods, Natural Language Processing, Reinforcement Learning, Computer Vision).

openpose icon openpose

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

resourcebank_cv_nlp_mlops_2022 icon resourcebank_cv_nlp_mlops_2022

This repository offers a goldmine of materials for students of computer vision, natural language processing, and machine learning operations.

sampleproject icon sampleproject

A sample project that exists for PyPUG's "Tutorial on Packaging and Distributing Projects"

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