This year at TensorFlow World, startups within NVIDIA’s startup incubator, Inception, will showcase their latest AI-based applications accelerated by NVIDIA…
Overview
The article discusses NVIDIA's Inception program, which supports AI startups showcased at TensorFlow World 2019. It highlights three startups: Cnvrg.io, Determind AI, and PerceptiLabs, detailing their innovative applications and technologies powered by NVIDIA GPUs.
What You'll Learn
1
How to build production-ready ML pipelines with Cnvrg.io
2
Why distributed training is essential for AI applications with Determind AI
3
How to utilize a drag-and-drop interface for machine learning with PerceptiLabs
Key Questions Answered
What innovations are showcased by Cnvrg.io at TensorFlow World 2019?
Cnvrg.io showcases its new application that enables data scientists to build production-ready ML pipelines with a Continual Learning feature. This feature allows automatic retraining based on new data and model decay, ensuring high-performing models in production.
What enhancements does Determind AI demonstrate at TensorFlow World 2019?
Determind AI demonstrates several enhancements including distributed training, state-of-the-art hyperparameter optimization, and cloud GPU-backed notebooks. These features aim to improve the AI training process and infrastructure.
What is the focus of PerceptiLabs at TensorFlow World 2019?
PerceptiLabs focuses on simplifying machine learning through a flexible platform with a drag-and-drop interface. They are launching an open beta that can run on local or cloud NVIDIA GPUs, enhancing accessibility for developers.
Technologies & Tools
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Hardware
Nvidia Gpus
Used to accelerate AI-based applications showcased by the startups.
Framework
Tensorflow
The deep learning framework on which the startups' applications are built.
Key Actionable Insights
1Explore the Continual Learning feature from Cnvrg.io to enhance your ML models.This feature allows for automatic retraining based on new data, which is crucial for maintaining model accuracy in dynamic environments.
2Utilize Determind AI's distributed training capabilities to scale your AI projects effectively.Distributed training can significantly reduce training time and improve model performance, making it essential for large datasets.
3Leverage the drag-and-drop interface of PerceptiLabs to streamline your machine learning workflow.This user-friendly approach can help reduce the learning curve for new developers and speed up the model development process.
Related Concepts
AI/ML Applications
Machine Learning Pipelines
Distributed Training
User Interface Design In ML