

Bunmi Akinremi
Microsoft Certified AI Engineer || Machine Learning Engineer at Kochava
Lagos, Nigeria
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Bunmi Akinremi is a Microsoft Certified AI Engineer and seasoned Machine Learning Engineer with a thriving career in the advertising industry at Kochava. With 4+ years of experience in data science and AI, she has made impactful contributions across agriculture, finance, and journalism domains.
Currently driving innovation at Kochava, Bunmi's work at Kochava involves building robust data pipelines and enhancing ML systems to make data-driven choices for clients. She holds a BSc. in Computer Science and is pursuing a Micro Master in Data, Economics, and Design from MIT.
Beyond her professional accomplishments, Bunmi actively advocates for diversity and inclusion, leading the Women in Data Science Community and the Microsoft Student Community and empowering women professionals and AI enthusiasts. As a sought-after speaker, Bunmi has shared her expertise on AI Research and applications at various conferences. She enjoys photography and writing poems about nature and everyday experiences in her leisure time.
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LangChains with Azure OpenAI
LangChain revolutionizes your Large Language Model (LLM) capabilities by enabling them to efficiently extract information from APIs, databases, and a myriad of Knowledge Bases, resulting in a seamlessly enriched user experience. With LangChain, you have the power to query your LLM model for real-time internet data.
Join us for an immersive hands-on session where you'll learn how to create and deploy a Language Model on Azure OpenAI and seamlessly connect it with external APIs and databases. We'll delve into advanced techniques for querying databases using natural language, and walk through generated content from an open podcast.
Deploy an Emotion Detection App with Microsoft Azure
Want to learn about building an Emotion detector without having to write ML codes from scratch? I'll show you how to leverage Microsoft Azure AI Services to build a cross-platform solution without having to write a single ML code.
In this session, I'll talk about
- Why emotion detection and what are its use cases?
- Microsoft Azure Vision API.
- A demo walkthrough session where you'll build a simple emotion detection app.
- Useful resources to explore more use cases.
- Finally, a Q&A session to answer all your questions.
Turning research papers into working solutions
Turning a research paper into working codes for the first time can be daunting, difficult, hard, and scary.
For someone who looks into solving complex problems that require research, rather than wasting time and money reinventing the wheel or waiting till someone does it and makes it open source, he can simply use an already existing research paper that provides a solution to his problem. There's the fear of too many paper pages or over-complexity of papers or maths symbols and equations(scary and flags disinterest).
I'll talk about 9 practical steps which I employed to solve this problem which include
- understanding your problem,
- finding papers on that topic,
- filtering papers by skimming through important parts,
- finding relevant data sources and computing resources that will help build your solution,
- creating a roadmap for the methodology,
- supplementing each phase in the roadmap with additional resources to fit your use case,
- comparing your results with the paper’s,
- improving your solution with suggestions included in the summary,
- and reach out to the authors if you have questions or concerns.
By the end of the session, you'll be able to apply the transformation map to any research paper you wish to implement.
The Art of Forecasting
Forecasting is a common word used to mean predicting the future. In business scenarios, you are trying to predict sales, customer activity, or other related metrics, but there's much more to it. Data holds a lot of insight, but data alone is insufficient for decision-making, especially decisions that directly affect revenue, cost, or customers. Other qualitative than quantitative factors affect a business's proposed or predicted outcomes, which must be deliberated alongside the insights obtained from the data.
In this session, we will dive deep into the nitty gritty of forecasting - more art than science, taking a business scenario as a use case and the other physiological and psychological factors that come into play in determining its impact as a decision support system.
DataFestAfricaSessionize Event
Global Azure 2022Sessionize Event

Bunmi Akinremi
Microsoft Certified AI Engineer || Machine Learning Engineer at Kochava
Lagos, Nigeria
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