deploying-machine-learning-models-for-edge-computing-on-drones 
CIO
APRIL 30, 2024
Enterprises are moving computing resources closer to where data is created, making edge locations ideal for not only collecting and aggregating local data but also for consuming it as input for generative processes. The rise of edge computing Edge computing has emerged as a strategic paradigm shift in the world of data processing.
Dataiku
MARCH 10, 2021
Dataiku and UAVIA are working together to push the boundaries of unmanned autonomous vehicles further towards ubiquitous AI and real-time aerial inspections.
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CIO
OCTOBER 5, 2023
As the pace of innovation speeds up, tomorrow’s front runners are those who readily embrace disruptive technologies to spearhead new business models and capture new avenues of growth. At the same time, the influx of new technologies and new business models is introducing myriad vulnerabilities and expanding the threat surface area.
TechCrunch
APRIL 13, 2021
One of the issues with deploying a machine learning application is that it tends to be expensive and highly compute intensive. Deeplite , a startup based in Montreal, wants to change that by providing a way to reduce the overall size of the model, allowing it to run on hardware with far fewer resources.
O'Reilly Media - Ideas
SEPTEMBER 6, 2022
Google has built a robot that incorporates a large language model so that it can respond to verbal requests. And we’ve seen a plausible argument that natural language models can be made to reflect human values, without raising the question of consciousness or sentience. DALL-E is increasingly popular, and being used in production.
O'Reilly Media - Ideas
FEBRUARY 28, 2019
In a recent post , we described what it would take to build a sustainable machine learning practice. These projects are built and supported by a stable team of engineers, and supported by a management team that understands what machine learning is, why it’s important, and what it’s capable of accomplishing.
O'Reilly Media - Ideas
MARCH 31, 2022
Are they using tools like AutoML to generate models, and other tools to streamline AI deployment? Marketers use the term AI; software developers tend to say machine learning. AI as a service includes AI packaged in ways that may not look at all like neural networks or deep learning. That’s probably a result of timing.
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