| # import valohai_yaml | |
| import sys | |
| import pyaml | |
| import yaml | |
| import pathlib | |
| from valohai_yaml.objs import Step, Parameter, Config | |
| params = [] |
In this tutorial we'll bring the TensorFlow 2 quickstart to Valohai, taking advantage of Valohai versioned experiments, data inputs, outputs and exporting metadata to easily track & compare your models.
You can use Valohai through the UI, using our command-line tools or by calling the APIs from your pipelines. This tutorial will focus on using the Valohai commandline tools.
➡️ Start by creating an account at http://app.valohai.com.
In this whiteboard design session, you will work with a group to design a solution which combines both pre-built artificial intelligence (AI) in the form of various Cognitive Services, with custom AI in the form of services built and deployed with Azure Machine Learning service. You will learn to create intelligent solutions atop unstructured text data by designing and implementing a text analytics pipeline. You will discover how to build a binary classifier using a simple neural network that can be used to classify the textual data, as well as how to deploy multiple kinds of predictive services using Azure Machine Learning service and learn to integrate with the Computer Vision API and the Text Analytics API from Cognitive Services.
At the end of this whiteboard design session, you will be better able to design solutions leveraging the Azure Machine Learning service and Cognitive Services.
In this whiteboard design session, you will work with a group to design a solution which combines both pre-built artificial intelligence (AI) in the form of various Cognitive Services, with custom AI in the form of services built and deployed with Azure Machine Learning service. You will learn to create intelligent solutions atop unstructured text data by designing and implementing a text analytics pipeline. You will discover how to build a binary classifier using a simple neural network that can be used to classify the textual data, as well as how to deploy multiple kinds of predictive services using Azure Machine Learning service and learn to integrate with the Computer Vision API and the Text Analytics API from Cognitive Services.
At the end of this whiteboard design session, you will be better able to design solutions leveraging the Azure Machine Learning service and Cognitive Services.
You can activate a free Microsoft Azure account at http://azure.com/free
For the purpose of the hackathon, we're providing you with free Azure Pass codes worth 50$ and valid for 30-days.
Azure IoT Edge can make your IoT solution more efficient by moving workloads out of the cloud and to the edge. This capability lends itself well to services that process a lot of data, like computer vision models. The Custom Vision Service lets you build custom image classifiers and deploy them to devices as containers. Together, these two services enable you to find insights from images or video streams without having to transfer all of the data off site first. Custom Vision provides a classifier that compares an image against a trained model to generate insights.
For example, Custom Vision on an IoT Edge device could determine whether a highway is experiencing higher or lower traffic than normal, or whether a parking garage has available parking spots in a row. These insights can be shared with another service to take action.
In this tutorial, you learn how to:
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You can activate a free Microsoft Azure account at http://azure.com/free
For the purpose of the hackathon, we're providing you with free Azure Pass codes worth 100$ and valid for 30-days.