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Analytics

How to Optimize Huggingface Models for Production

Deploying models is becoming easier every day, especially thanks to excellent tutorials like Transformers-Deploy. It talks about how to convert and optimize a Hugging face model and deploy it on the Nvidia Triton inference server. Nvidia Triton is an exceptionally fast and solid tool and should be very high on the list when searching for ways to deploy a model. If you haven’t read the blogpost yet, do it now first, I will be referencing it quite a bit in this blogpost.

The Growing Need for Advanced Analytics to Fuel 5G and Edge Solutions

Organizations have been focused on enhancing customer experiences to enable quicker responses to services and to provide localized behavior for many years now. However, with the Internet of Things (IoT), Smart Cities, Gaming technologies and Self-Driving Cars going more mainstream, there is an even greater need for organizations to react faster to customer behavior and bring solutions closer to the customers.

Questions around Transparency in AI models with Tom Davenport

Often the question around bias is raised whenever the conversation turns to AI. Tom Davenport, author of “Working with AI: Real Stories of Human-Machine Collaboration” points out that bias is not limited to AI, but also finds root in many human decision-makers as well. Actually, according to Tom, the bigger threat is ignoring that working with AI is going to increasingly be a part of our human work experience.

Data Mesh and other Alternatives for Data Chiefs in 2023

Title: Data Mesh and other Alternatives for Data Chiefs in 2023 Description: The data world exploded in 2022 with a heated debate around data mesh. We had to talk to Tony Baer of DBinsights to get a better understanding of his perspective and criticism of data mesh. Most importantly, we needed to know what it is he recommends we use instead!

Understanding The Risks and Rewards of Data Observability

Data observability is the ability to monitor and understand the data that flows through an organization's systems. Organizations can monitor their data in real-time, detect anomalies, and take corrective action based on alerts. Organizations use data observability to collect, analyze, and visualize data from various sources to manage their system's behaviour across the data ecosystem.