Continuing my series on how to leverage the various Machine Learning and AI services from Amazon, Azure and Google, previously we used Amazon’s Rekognition and Python to perform image recognition with KNIME.
Rather than having to custom code solutions, I can, in just a few minutes build a workflow or integrate into my existing workflows a comprehensive sentiment analysis service. More impressive, not a single line of code will be written.
We’ll be leveraging KNIME’s built in Twitter functionality, along with Microsoft Azure’s Cognitive Services. …
Building an accurate image recognition engine with deep learning tools can be a difficult undertaking. You may require tens of thousands (or millions) of curated, tagged images to get an accurate model and a powerful enough server to run the deep learning model.
Well, luckily for us, all major cloud vendors have solved this challenge by providing API’s for their machine learning image recognition services. Google has Cloud Vision, Microsoft Azure has Computer Vision and Amazon has Rekogniton.
The approach I take with my tutorial using Amazon can be adapted and used for any of the services above. At the end of the day, an image is uploaded to cloud storage, an API call issued and a JSON output is returned with the images classification. …
In my role at Forest Grove Technology, I’m always trying to find a way to provide clients with innovative solutions and leverage tools they already have. In summary as little to no coding as possible.