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About Najib Ninaba

Najib Ninaba

Najib Ninaba

A 20-year veteran in tech startups and MNCs, Najib focuses on High- Performance Computing (HPC ) as well as Cloud, Data and Artificial Intelligence (AI). He has led engineering teams in several organisations, some of which were startups that were acquired or exited successfully. He has helped build several of the first generation HPC cluster systems and infrastructure in Singapore and the region. He was also a lecturer for NUS School of Continuing and Lifelong Education (NUS SCALE) where he conducted workshops on Reproducible Data Science, Data Engineering and Conversational AI bots (Chatbots). He currently heads the AI Platforms Engineering team in the Industry Innovation Pillar at AI Singapore (AISG) where his team focuses on building the AI infrastructure and platforms for researchers, engineers and collaborators to solve challenging problems.
AI Makerspace

Robustness testing pipeline for NLP with Microsoft CheckList

In order to streamline robustness testing into the AI engineering process, the SecureAI team has made it a priority to integrate robustness testing tools into AI Singapore’s MLOps pipelines. In this article, we will share our experience integrating Microsoft CheckList, as a black-box robustness testing tool, in our CI/CD processes along with various technologies as

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AI Makerspace

Robustness Testing of AI Systems

Standard model evaluation processes involve measuring the accuracy (or other relevant metrics) on a hold-out test set. However, the performance on these test sets do not always reflect the ability of the model to perform in the real world. This is because a fundamental assumption when deploying AI models is that all future data is

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AI Bricks/Products

Synergos in Action

We’ve launched Synergos, a Federated Learning platform AI Singapore has been working on. The launch marks the start of a long journey. We invite you to join us on this journey. Let’s start by using it as a tool in your machine learning toolbox, and even contribute your code to the repo. Do share with

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AI Makerspace

Secure AI Engineering in AI Singapore

With more AI systems being deployed into production, it becomes critical to ensure that the systems are secure and trustworthy. Here in AI Singapore, the SecureAI team is dedicated to developing processes and tools to support the creation of secure and trustworthy AI systems. As shared in the previous article, one of the key ingredients

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AI Bricks/Products

Synergos Is Officially Launched!

In previous posts (here and here), we gave a preview of AI Singapore’s Federated Learning platform Synergos. Today we are happy to announce that Synergos is officially launched! We begin with a recap of what Synergos is and its main components. The motivation for Synergos Data is at the core of machine learning. Nevertheless, in

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AI Makerspace

Using AI With Confidence

Ensuring robustness in AI systems against malicious attacks garners greater attention Over the past few years, Artificial Intelligence (AI) has exploded in capability and is becoming increasingly common in our everyday lives. From Tesla’s Autopilot[1] to wider adoption of voice assistance found on smartphones to diagnostics assistance for medical workers in medical imaging and more

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AI Makerspace

The Rise of the Data Catalogs

As the need for data to fuel AI applications grows, tools to organise data are proliferating As discussed in a previous post, the data engineering team (DE) at AI Singapore was formed to architect and deploy a data platform for our organisation. A more robust and consistent approach to data management has been expressed as

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AI Makerspace

Data Engineering at AI Singapore

The newly formed Data Engineering team at AI Singapore has plans to refine data management practices in tandem with the growth in number of projects Toward a Common Data Platform The AI Innovation team at AI Singapore has evolved from a few staff to a strong collection of engineering teams in the last two years.

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