KDnuggets Home » News » 2019 » Jul » Webcasts & Webinars » Build your own AutoML computer vision pipeline, July 16 webinar ( 19:n25 )

Build your own AutoML computer vision pipeline, July 16 webinar


This webinar will present a step-by-step use case so you can build your own AutoML computer vision pipelines, and will go through the essentials for research, deployment and training using Keras, PyTorch and TensorFlow.



By cnvrg.io Sponsored Post.
Cnvrg Automl Computer Vision Pipeline

Computer vision is rapidly enhancing how technology reacts with the world around us. Whether it’s autonomous vehicles, handwritten text recognition, face recognition, or detecting disease from x rays, computer vision is greatly improving all industries. Teamed up with the capabilities of AutoML, data science teams can accelerate model development by automating the end-to-end process. AutoML makes machine learning workflows simpler, allowing data scientists to build more complex models.

In this webinar , data science expert Yochay Ettun will present a step-by-step use case so you can build your own AutoML computer vision pipelines. Yochay will go through the essentials for research, deployment and training using Keras, PyTorch and TensorFlow. He’ll provide an overview of infrastructure and MLOps using Docker, Kubernetes and elastic cloud services. This webinar will enable you to build your own custom AutoML computer vision pipeline and help your business apply machine learning on more use-cases, problems and projects. The aim of this webinar is to democratize machine learning so software developers, engineers, and data scientists can feel confident building a computer vision pipeline.

Register here: (July 16, 9 am PT, noon ET)

Key webinar takeaways:

  • How to identify production value of computer vision projects
  • How to manage datasets for computer vision and deep learning applications
  • How to use AutoML for computer vision
  • How to save time and achieve top performing results with transfer learning and reusable machine learning components
  • How to build a proper MLOps setup so you can focus more on research and less on IT
  • Monitor and track training with specific parameters and metrics
  • Integrate computer vision into your application by deploying it as a REST endpoint
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