DEV Community: Gideon Onyewuenyi

Mixture of Experts (MoE)

Gideon Onyewuenyi — Mon, 05 Jan 2026 14:12:25 +0000

How Smaller, Specialised Models Can Work Better Than One Giant Model

Mixture of experts (MoE) is a machine learning approach that divides an artificial intelligence model into separate sub-networks (or experts), each specialising in a subset of the input data, to perform a task jointly

Building Bigger Models

The belief is that if we make models bigger, they will be smarter.

So we keep increasing:

Number of parameters (the model’s internal “brain cells”)
Amount of training data
Computing power needed to train and run them

Example:

GPT-3 → 175 billion parameters
GPT-4 → more than 1 trillion!

But...

Costs are exploding
Improvement might be slowing down sometimes

The Idea of Mixture of Experts (MoE)

Instead of one huge model doing everything, MoE build many smaller models (called “experts”), each good at one thing. Then add a router (a smaller model) that will choose which expert to use for each task.

Example:

Expert A: good at math
Expert B: good at writing
Expert C: good at coding

If you ask a math question → the router sends it to Expert A only.

How It Works in simple terms

You send a question (the input).
The router examines it and determines which experts should handle it.
Only those experts “wake up” and work on the question.
Their answers are combined into one final response.

You don’t waste compute on experts you don’t need. Each expert becomes very skilled in their own area. The system gets faster and cheaper over time.

Analogy

Think of a team project:

Instead of one person trying to do everything, you have many people with different strengths.

When a task comes up, you call the right person for the job.

That’s how MoE works — it’s teamwork inside AI.

Why MoE Is Smarter, Not Larger

One Big Model

Compute: Uses all parts for every task
Cost: Expensive
Speed: Slower
Specialisation: General at everything

Mixture of Experts (MoE)

Compute: Uses only a few experts per task
Cost: More efficient
Speed: Faster
Specialisation: Great at specific things

Andrew Ng often argues that smaller, focused systems + smart orchestration can outperform huge, general-purpose models.

His thoughts are: “You don’t always need a bigger model; you need the right workflow.”

MoE is the architecture version of that same idea: smarter routing and specialisation.

While MoE enables specialisation through structure and routing, each expert can also be further specialised via fine-tuning.

MoE is not a new idea, but its relevance today comes from the need to scale AI efficiently, not just aggressively.

Why does it matter

Saves energy and money
Reduces latency, ie faster answers
Easier to update or improve individual experts
Encourages modular design - like Andrew Ng’s view that smaller systems working together can achieve more

Practical AI for Developers and Creators

Gideon Onyewuenyi — Fri, 23 Feb 2024 06:12:19 +0000

AI is the new electricity, there is no single industry it will not disrupt. This includes the industry that writes software applications. The subject of this article focuses on the future of work for developers and creators and how they can remain relevant in the fast-changing world of technologies specifically in AI and Machine Learning while delivering great experiences in their applications.

Artificial Intelligence will transform application development forever as it opens up new scenarios that were previously impossible for developers (web/mobile/embedded/cloud). Upskilling in AI will make it easier and possible for developers to deploy great apps as most real-life apps are getting smarter & better with AI.

Developers and creators will bring the most usable solutions that we are yet to think about using AI. The estimated number of developers by 2030 is 45 million, this is almost double the current number of developers at 24 million, and more than 40% of them will have AI as their full-time job.

There is a big gap, between developers who build models to developers who focus primarily on web/mobile/cloud/embedded technologies, the need to transition to AI, add AI to their apps or devices, and get those solutions into the hands of real people.

To bridge the gap, developers and creators can leverage services like AWS Bedrock on the AWS platform to start integrating AI capabilities into their projects.

AWS Bedrock provides a unified API to access a variety of foundational AI models from leading AI companies. It simplifies the process of integrating generative AI capabilities into applications, including text, image, audio, and synthetic data generation.

AWS also offers a range of other AI services beyond Bedrock, such as Amazon SageMaker for building, training, and deploying machine learning models, Amazon Rekognition for adding image and video analysis to your applications, and so on

Getting Started using AWS Bedrock

Sign Up for AWS: Create an AWS account if you don't already have one. AWS provides a free tier for new users, which is a great way to explore and experiment with various services without incurring significant costs.

Explore the Documentation: AWS provides extensive documentation and tutorials for its services. The AWS Bedrock documentation offers a good starting point to understand how to integrate foundation models into your applications.

Experiment with Pre-built Models: Before building your models, experiment with the pre-built models available through AWS Bedrock. This can help you understand the capabilities and limitations of current AI technologies.

Integration into Your Projects: Start integrating AI features into your existing projects. For instance, you could add natural language processing capabilities to improve user interactions or utilize image recognition to enhance the functionality of your app.

Practical Applications for developers and creators

Small Projects: Begin with small, manageable projects that integrate AI features. For example, create a simple chatbot using natural language processing or a tool that automatically tags images uploaded by users.

Focus on User Experience: Consider how AI can enhance the user experience in your applications. AI should not be used just for the sake of it but should add real value to your app's functionality.

Learn from the Community: Join AWS and AI-focused communities online. Platforms like GitHub, Stack Overflow, and Reddit have active communities where you can ask questions, share your projects, and learn from others' experiences.

Stay Updated: The field of AI is evolving rapidly. Keep learning about new models, tools, and best practices. AWS regularly updates its services and introduces new features, so staying informed will help you make the most of these technologies.

Learn more about Amazon Bedrock

KaggleX Showcase - The Unified platform to build custom and private LLMs

Gideon Onyewuenyi — Tue, 31 Oct 2023 17:30:52 +0000

In today's fast-evolving world of artificial intelligence (AI), organizations are amassing extensive volumes of text data through their daily operations. This data, often overlooked, holds a lot of potential, especially when leveraged to train Large Language Models (LLMs).

Introducing "Naya," a unified platform that streamlines the journey from data integration to the deployment of customized and private LLMs.

Platform Overview

An overview of the Nayarr platform, showcasing its comprehensive features and layers.

Bridging the Gap in AI Utilization

Many organizations possess vast reservoirs of text data but face challenges in unlocking its full potential. Naya addresses this by providing a seamless connection between in-house data, pre-trained models, and the deployment of domain-specific models on private infrastructures.

Data to Model

From data integration to model deployment, Naya ensures a smooth and secure workflow.

Key Components of Naya

Data Integration Layer
Naya ensures effortless data ingestion from diverse sources, with a particular emphasis on website content. Tools within the Data Wrangler section clean and preprocess this data, preparing it for the next stages of model training.

Model Management Layer
The platform houses a repository for storing and cataloging pre-trained models, complemented by a robust training infrastructure for scalable model fine-tuning. Automated hyperparameter tuning ensures optimal model performance.

Deployment Layer
The platform doesn’t just stop at model training; it ensures that the trained models are packaged optimally and deployed securely to the required infrastructure, be it cloud, on-premises servers, or edge devices.

Security & Compliance Layer
In the modern age, data security and compliance are paramount. The platform incorporates tools for data anonymization, and compliance checking, and maintains an audit trail for all activities, ensuring operations are transparent and adhere to industry standards.

User-Friendly Interface
A web dashboard, API gateway, and comprehensive documentation are provided to ensure a seamless user experience, catering to both novice users and experienced developers.

Tapping into Overlooked Data Resources
The platform transforms overlooked text data from various sources into valuable assets, enhancing AI capabilities and unlocking new potentials. From customer support tickets to social media interactions, every piece of text data becomes a stepping stone towards a more intelligent future.

Conclusion
Naya provides a unified and simplified solution for organizations aiming to harness the power of their text data. By ensuring a streamlined process from data integration to model deployment, while adhering to the highest standards of security and compliance, Naya paves the way for a future where the full potential of Large Language Models is within reach for every organization.