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Deploying Machine Learning Models Online with Watson Machine Learning | Python Scikit-Learn
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You’ve scoured the web and collected a ton of data.
You’ve done a bunch of Exploratory Data Analysis.
You’ve trained a bunch of models.
They’re pretty accurate.
Now what?
Now, you deploy them! This is one of the final steps in the CRISP-DM lifecycle. Once you’ve evaluated your models and you’re happy to release them into the wild, this is generally the step you’re going to want to take.
But how?
Well, in this video you’ll learn exactly how to do that. You’ll learn step by step how to take a trained Scikit-Learn model and deploy it to the Cloud using Watson Machine Learning. This model can then be used for a whole bunch of applications and can even be used in different languages like Javascript, Scala, Java and Go. And what’s best? You’ll do it all in less than half an hour.
In this video you’ll learn how to:
1. Saving models to Watson Machine Learning
2. Creating online deployments with Python
3. Scoring your model using the Python API
GET THE CODE!
Links Mentioned
Watson Machine Learning Endpoint URLs
If you have any questions, please drop a comment below!
Oh, and don't forget to connect with me!
Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand!
You’ve done a bunch of Exploratory Data Analysis.
You’ve trained a bunch of models.
They’re pretty accurate.
Now what?
Now, you deploy them! This is one of the final steps in the CRISP-DM lifecycle. Once you’ve evaluated your models and you’re happy to release them into the wild, this is generally the step you’re going to want to take.
But how?
Well, in this video you’ll learn exactly how to do that. You’ll learn step by step how to take a trained Scikit-Learn model and deploy it to the Cloud using Watson Machine Learning. This model can then be used for a whole bunch of applications and can even be used in different languages like Javascript, Scala, Java and Go. And what’s best? You’ll do it all in less than half an hour.
In this video you’ll learn how to:
1. Saving models to Watson Machine Learning
2. Creating online deployments with Python
3. Scoring your model using the Python API
GET THE CODE!
Links Mentioned
Watson Machine Learning Endpoint URLs
If you have any questions, please drop a comment below!
Oh, and don't forget to connect with me!
Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand!
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