At AgroScout, we’re taking on a massive challenge with some correspondingly exciting upside, both for us and for our customers: We’re creating an automated, AI-driven scouting platform for early detection of pests and disease in vast agricultural areas.
In our previous article, The Future of the Modern Data Stack, we examined the motivations of the modern data stack, its current state, and looked optimistically into the future to see where it is headed. If you’re new to the modern data stack, we highly recommend giving the aforementioned article a read. A question we often get from new adopters of the modern data stack is “What tech should we be looking into?”.
Congratulations on creating a clean(ish) dataset to use for training! Now while the dataset is stored where it’s accessible to everyone, the distribution itself is a hassle! Local workstations, local GPU machines, and cloud machines (that may be spun up and down without disk persistence) are getting data everywhere. …and to say it is annoying is an understatement!
A casual stroll through recent tech headlines in the past few years makes two things abundantly clear: investment in AI is at an all-time high, and companies really struggle to get value out of AI technology. At first glance, these ideas seem to be at odds with each other: why consider investing in a field that hasn’t lived up to the hype? If you dig into the details, you’ll notice that a gap exists between the development and production use of AI in many companies.
Today, I’m excited to share that I’ve joined Continual as Head of Marketing. Continual is radically simplifying the path to operational AI with the first continual AI platform built for the modern data stack. More in a bit on what that means, but the “so what?” is about opening the door for more organizations to embed AI across their business at scale.
Feature stores have arrived in 2021 as an essential piece of technology for operationalizing AI. Despite the enthusiasm for feature stores in high-tech companies, they are still absent from most legacy ML platforms and can be relatively unknown in many enterprise companies. We discussed how feature stores are critical to the data-first approach of next-gen ML platforms in our previous blog, but they are important enough to get their own treatment in a full article.
Artificial intelligence is transforming software development. From the code to the deployment, AI is slowly but surely upping its game and helping us discover a brand new paradigm for inventing technology. Algorithm-based machine learning is being used to accelerate the software development lifecycle and AI is supporting developers to optimize software workflow at every stage of the development process.
For our latest expert interview on our blog, we’ve welcomed Dhonam Pemba to share his thoughts on the topic of artificial intelligence (AI) and his journey behind founding KidX AI. Dhonam is a neural engineer by PhD, a former rocket scientist and a serial AI entrepreneur with one exit. He was CTO of the exited company, Kadho which was acquired by Roybi for its Voice AI technology. At Kadho Sports he was their Chief Scientist which had clients in MLB, USA Volleyball, NFL, NHL, NBA, and NCAA.
In 2020, the gaming market generated over 177 billion dollars, marking an astounding 23% growth from 2019. While it may be incredible how much revenue the industry develops, what’s more impressive is the massive amount of data generated by today’s games. There are more than 2 billion gamers globally, generating over 50 terabytes of data each day.
