Ruqiya Bin Safi

Ruqiya Bin Safi

Google Developer Expert in AI / ML and Sr. AI Engineer

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Reflections on Product Prioritization Strategies

3 minute read

Published:

Reflections on Product Prioritization Strategies

From an AI Product Management Course Strategies A journey of understanding, evaluating, and gaining deeper awareness of what truly makes a difference in the world of products.

Fine-Tune Gemma 2 2b with Keras and LoRA (Part 3)

3 minute read

Published:

Gemma2 Continuing our educational series focusing on Arabic language handling with large language models, in this part, we will explore how to fine-tune the Gemma2-9b model on an Arabic dataset using the Keras library, Keras_nlp, and LoRA technique. We will cover how to set up the environment, load the model, make necessary modifications, and train the model using model parallelism to distribute model parameters across multiple accelerators.

Fine-Tune Gemma 2 2b Using Transformers and qLoRA (Part 2)

4 minute read

Published:

Gemma2 As part of our tutorial series focusing on handling Arabic with large language models, we will continue in the second part by exploring specialized methods for fine-tuning the Gemma-2b model on an Arabic dataset to enhance its performance. We will use the Transformers library and qLoRA (Quantized Low-Rank Adaptation) technique to reduce memory usage.

Build a RAG Application Using Gemma 2 (Part 1)

3 minute read

Published:

Gemma2 When working on a project that requires handling the Arabic language, you might wonder whether to use Retrieval-Augmented Generation (RAG) or to fine-tune a model with a new Arabic dataset. In this series of tutorials with two parts, we will explore both options: using RAG and fine-tuning a model with Arabic data, specifically Wikipedia. Throughout the project, we will focus on open-source models, utilizing Gemma 2 Instruct and an open-source embeddings model. We will also leverage the LangChain framework to streamline the process of building the RAG and fine-tuning the model. Let’s dive into the practical implementation.

Next-Gen Form Filling with Gemini 1.5 Pro

7 minute read

Published:

Filling out forms can be boring and time-consuming. This often leads to user frustration and incomplete submissions. However, conversational AI, like the Gemini 1.5 Pro language model, is changing how we interact with forms.

Retrieval Augmented Generation (RAG) using Gemma to Explain Basic Data Science Concepts

11 minute read

Published:

The world of data science can be daunting for newcomers, filled with complex terminology and intricate concepts. But what if you had an AI assistant by your side, ready to explain these concepts in simple terms and guide you through the learning process? This is where the power of Retrieval Augmented Generation (RAG) comes into play. In this blog post, we’ll embark on a hands-on journey, building an AI-powered explainer for data science concepts using the RAG model and the Gemma language model.

portfolio

publications

talks

teaching

Deep Learning Course

Course, Online, 2021

A course on deep learning methods with applications to computer vision, natural language processing . you will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow.

Data Engineering Course

Course, Online, 2022

A data engineering course that focuses on how to create a real-time Twitter analytics dashboard for streaming tweets with a sentiment analysis (NLP) feature.