Machine Learning

Interview with Machine Learning Engineer Semih Cantürk

In the latest instalment of our interviews speaking to leaders throughout the world of tech, we’ve welcomed Semih Cantürk. Semih is a Machine Learning Engineer at Zetane Systems and an MSc & incoming PhD student at the University of Montréal and MILA Institute. At Zetane, he’s responsible for the development and integration of explainable AI algorithms in addition to leading various project work.

TurinTech's evoML reduces AI's carbon emissions by 50% with multi-objective optimisation

TurinTech, the UK company which empowers businesses to build efficient and scalable AI by automating the whole data science lifecycle, has announced its greener AI platform- evoML- which reduces AI's carbon emissions by 50%.

ODSC Webinar: Git Based CI/CD for ML

In this session, Yaron Haviv, Iguazio's Co-Founder and CTO, discussed how to enable continuous delivery of machine learning to production using Git-based ML pipelines (Github Actions) with hosted training and model serving environments. He touched upon how to leverage Git to solve rigorous MLOps needs: automating workflows, reviewing models, storing versioned models as artifacts, and running CI/CD for ML. He also covered how to enable controlled collaboration across ML teams using Git review processes and how to implement an MLOps solution based on available open-source tools and hosted ML platforms. The session includes a live demo.

Building an MLOps infrastructure on OpenShift

Most data science projects don’t pass the PoC phase and hence never generate any business value. In 2019, Gartner estimated that “through 2022, only 20% of analytic insights will deliver business outcomes”. One of the main reasons for this is undoubtedly that data scientists often lack a clear vision of how to deploy their solutions into production, how to integrate them with existing systems and workflows and how to operate and maintain them.

Looking into 2022: Predictions for a New Year in MLOps

In an era where the passage of time seems to have changed somehow, it definitely feels strange to already be reflecting on another year gone by. It’s a cliche for a reason–the world definitely feels like it’s moving faster than ever, and in some completely unexpected directions. Sometimes it feels like we’re living in a time lapse when I consider the pace of technological progress I’ve witnessed in just a year.

How to use AI & ML to optimize supply chain analytics with Sisu

Supply chain incidents and delays are painful and costly—impacting customers and resulting in lost revenue. However, many businesses lack the resources required to quickly and accurately diagnose, resolve, and reduce these incidents. By leveraging augmented analytics, data analysts and decision-makers can reduce these incidents to increase profits, improve customer satisfaction, and mitigate risk.

Adopting a Production-First Approach to Enterprise AI

After a year packed with one machine learning and data science event after another, it’s clear that there are a few different definitions of the term ‘MLOps’ floating around. One convention uses MLOps to mean the cycle of training an AI model: preparing the data, evaluating, and training the model. This iterative or interactive model often includes AutoML capabilities, and what happens outside the scope of the trained model is not included in this definition.

How to use AI & ML to optimize supply chain analytics with Sisu

Supply chain incidents and delays are painful and costly—impacting customers and resulting in lost revenue. However, many businesses lack the resources required to quickly and accurately diagnose, resolve, and reduce these incidents. By leveraging augmented analytics, data analysts and decision-makers can reduce these incidents to increase profits, improve customer satisfaction, and mitigate risk.