Skip to content
View kbakande's full-sized avatar
2 followers · 3 following

Achievements

Achievement: Pull Sharkx3Achievement: Pair Extraordinairex3Achievement: YOLOAchievement: Arctic Code Vault Contributor
BetaSend feedback

Achievements

Achievement: Pull Sharkx3Achievement: Pair Extraordinairex3Achievement: YOLOAchievement: Arctic Code Vault Contributor
BetaSend feedback
Block or Report

Block or report kbakande

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
kbakande/README.md

Hiya! 👋

My name is Kabeer Akande, and I'm a senior machine learning engineer at OVO!

  • 💛 I love productionising ML products and building scalable ML pipelines
  • 🔭 Formerly at BigDEAL, S2DS, MindfulChef, and UOE

LinkedIn Google Scholar

Blog

🚀 (Some) highlights

🚢 Open source projects

MLOPS Platform

MLOPS_Platform is a robust machine learning platform designed to streamline ML workflows on Google Cloud Platform (GCP) resources. Following best practices in MLOps and DevOps, it offers a structured and efficient approach to managing data loading, preprocessing, model training, evaluation, and deployment. It features automated scaling, testing, integration, documentation, and deployment. If you want to find out more, I have published a development roadmap on my Medium page.

Pipeline Visualization

Below is a visualization of the MLOPS Platform pipeline components as executed on Vertex AI:

MLPlatform Pipeline on Vertex AI

Terraforming Dataform

Terraforming Dataform establishes the fundamentals of a single repo, multi-environment Dataform with least-privilege access control and infrastructure as code setup. Datailed explanation of the workflow is published on my Medium page.

Architecture

Dataform Architecture

Machine Learning Models as Product (MLMaP)

MLMaP is a paradigm shift in ML modelling. It demonstrates building ML algorithms as software products thus, enabling data scientists and ML engineers to package models similar to how software developers and engineers ship software applications. It is a step towards MLOps implementation.

Named Entity Recognition App (NER)

NER is a Named Entity Recognition (NER) application. The app is built using Hugging Face's Transformers and Gradio, allowing users to input text and receive identified named entities. It utilizes a fine-tuned BERT model (koakande/bert-finetuned-ner) for high-accuracy entity recognition. You can try it out on my Hugging space

NER

🌍🛠 Languages and tools

👨‍💻 Regularly using...

Makefile Docker Git GitHub Actions Jira YAML Jupyter Poetry pre-commit pytest SQL VS Code Sphinx

🎉 Delivered projects using...

AWS GCP Airflow Kubeflow Terraform

📚 Learning with...

Coursera

Google

🧮 GitHub statistics

Kabeer's GitHub statistics Kabeer's most used languages

Popular repositories

  1. mlops-platform mlops-platform Public

    Demonstrate the use of ops tools to automate ML workflow

    Jupyter Notebook 3 1

  2. kbakande.github.io kbakande.github.io Public

    Space for penning my thoughts

    SCSS 1

  3. ML-Model-as-Product ML-Model-as-Product Public

    ML Model as Product showcases building ML models as a software product, following best practises in MLOps and DevOps

    Jupyter Notebook

  4. kbakande kbakande Public

  5. terraform-ing terraform-ing Public template

    Templated scripts for provisioning GCP infrastructure showing best practises

    HCL

  6. named-entity-recognition named-entity-recognition Public

    A named entity recognition app

    Jupyter Notebook