Transforming Image Processing with Machine Learning


Leia a startup paving the way for dynamic 3D imaging with stylistic possibilities through Lightfield hardware and content services. Lightfield is an emerging visual medium that transforms existing device displays with lighting effects, rich textures, and 3D depth. The technology creates a more engaging visual experience, enabling immersive, lifelike experiences. 


As a leading provider of Lightfield displays and devices, the Leia team was eager to find ways to accelerate machine learning setup and experiments. They had already experimented with machine learning environments and the Weights & Biases toolset, but still struggled with an unmet need:as a fast-paced startup, they needed to spend more time on model building and research, and less time grappling with infrastructure.The team sought a flexible and easy-to-use platform with seamless onboarding to fit into their workflow. 


Leia is rapidly growing and anticipates using Spell will supercharge their growth because its ease-of-use makes it so quick to onboard new engineers. Since Spell enables them to use their existing Google Cloud resources, it breaks the magic black box stereotype, instead helping the team understand their cloud usage. In turn, Leia is able to manage the resource usage of their clusters with ease, preventing wasted resources or surprises in billing at the end of each month. 

“Given that it's using our own Google Cloud resources, it helps us understand what’s going on,” said Puneet Kohli, Leia engineering manager. “When we get the bill for Google Cloud, it’s very clear that this was the part for machine learning training jobs and we can map that back to Spell and understand the usage very easily.”

Other features making a significant impact on their day-to-day workflow are the GCP storage configuration, defining machine types, and the ability to easily run jobs in parallel. Simple, straightforward UI has made each of these features easy to use. 

Now, the Leia Inc. team has a more efficient way to run experiments, hyperparameter searches, and concurrent jobs, and the team is excited to continue to grow with the Spell platform. 

Streamline Machine Learning Projects with Spell

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