Scikit-learn is a great tool for building your models. When it comes time to deploy them to prediction, scale up using Google Cloud ML Engine. In this episode of AI Adventures, Yufeng shows you how to set up your own deployment pipeline with scikit-learn so you can go back to focusing on tuning your model!
Scikit-learn is a well-documented and well-loved Python machine learning library. The library is maintained and reliable, offering a vast collection of machine learning algorithms for you to incorporate into your projects. If you haven’t tried scikit-learn, you definitely should! In this episode if AI Adventures, Yufeng gives an overview of scikit-learn and shows an example of scikit-learn in a kaggle kernel.
In part one of the AI Adventures intro to AutoML Vision, Yufeng talked about what AutoML Vision is used for and showed how to gather and prepare our training data. Stick around for part two where he shows how to use the data to train our model!
In this episode of AI Adventures, Yufeng Guo uses AutoML Vision to build and employ a machine learning model that recognizes different types of….chairs! In part 1, he’ll walk you through gathering the data and creating a csv file that describes the location and label for all the images in the dataset. Don’t miss part 2 to see how the model performs!
It can take a lot of tools to do data science, but Kaggle is a one-stop shop that provides all the tools to share and collaborate on data science projects. In the episode of AI Adventures, Yufeng is joined by Megan Risdal, product lead for datasets at Kaggle. They’ll teach you how to make a data science project with Kaggle, and more!
We all love data. But it can be hard to make practical use of large datasets. In this episode of AI Adventures, Yufeng Guo introduces BigQuery public datasets, which allow you to query huge datasets with great responsiveness without needing to worry about the storage costs. Time to break out your big data toolbox, because these queries are going to be big!
Looking to get more insights from your data, but don’t know where to begin? Dive into machine learning and the discovery journey of applying it to your datasets with this session based on the YouTube series “AI Adventures”.
In this episode of AI Adventures, Yufeng explores the massive “Quick, Draw!” dataset, a collection of over 1 billion doodles, drawn by users all over the world!
In this interview of AI Adventures, Yufeng interviews Developer Advocate Sara Robinson to talk about a custom object detection iOS app she built to detect Taylor Swift. We’ll cover everything from training a model with transfer learning, to serving the model in the cloud, to making prediction requests to the model from an iOS device (in Swift!).
In this episode of AI Adventures, Yufeng discusses some of the options available when it comes to managing your Python environment for machine learning and data science, and helps you make an informed decision based on your needs.
Training machine learning models at scale has never been simpler. By leveraging in the cloud machine learning engine, we can quickly and easily train TensorFlow models without worrying about configuring servers installing drivers and libraries.
In this episode of AI Adventures, Yufeng interviews Google Research engineer Justin Zhao to talk about natural text generation, recurrent neural networks, and state of the art research!
What do you do when your data is too big to fit on your machine? In this episode of Cloud AI Adventures, Yufeng walks through some of the options and rationale for putting your big data in the cloud.