Title: | Serverless Machine Learning with TensorFlow |
Date: | 9:00am-4:30pm, 1/25, Friday |
Instructor: | Chris Rawles, Google
Chris is a ML Solutions Engineer in Google Cloud where he teaches ML to Google customers and builds ML models using TensorFlow and Google Cloud. In his past work as a researcher, Chris used machine learning to study earthquakes. |
Course Outline: |
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Who should learn: | Developers, Data Scientists who are working on machine learning, deep learning. |
Level: | Beginner to Intermediate |
Prerequsite: | The following is prefered but not required.
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Title: | Accelerating AI through Automated ML |
Date: | 9am-12pm, 1/25, Friday |
Instructor: | Sujatha Sagiraju, Microsoft |
Course Outline: |
As a data scientist, for a given machine learning problem, you run multiple ML models to find the right model. For example, for a machine learning classification problem, you could be running your data through many different classifiers available such as SVM, Logistic Regression, Boosted Decision Tress etc. In addition, you are also trying many different hyper parameters such as learning rate, depth of the tree etc. There is no way to find out which model & hyper parameters is best other than trying out many different combinations manually. That is lot of training jobs and manual tuning before you find an optimal model that gives you the performance characteristics that you are satisfied with. AutoML uses intelligent optimization techniques to build a high quality model and does so by providing a simple interface to the user. It is just one method call.
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Who should learn: | Data scientists, Developers, BI professionals, Analysts |
Level: | Beginner to Intermediate |
Prerequsite: | |
Title: | Fast and lean data science with Tensorflow, Keras and TPUs |
Date: | 9:00am-12:00pm, 1/26, Saturday |
Instructor: | Martin Gorner, Google |
Course Outline: |
Training deep learning models used to be a game of patience. Training runs took hours and you needed hundreds of them to tune a model. Today, as software engineers add machine learning to their skill sets, they need to work faster because they have products to ship. Making use of the cloud is a big component of that, since it provides nearly unlimited compute resources, but introduces costs that you need to keep an eye on. This workshop gets you up and running designing, training and deploying state of the art vision models in minutes instead of hours by using Google's Tensor Processing Units (TPUs). We will also share tips and best practices for working with TPUs using modern Keras and Tensorflow 2.0-ready code.
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Who should learn: | Developers who would like to add deep learning skills to their skill set. Prior deep learning experience is welcome but all base concepts will be re-explained. The code is Keras in Python. This session focuses on practical data science tasks like model development training and deployment as part of a regular software development project where efficiency and agility are key. |
Level: | Beginner to Intermediate |
Prerequsite: | The workshop requires good proficiency in general software development and some basic Python skills. Prior deep learning experience is welcome but all base concepts will be re-explained.
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Title: | Build and Manage Machine Learning Pipelines |
Date: | 1:30pm-4:30pm, 1/26, Saturday |
Instructor: | Amy Unruh, Google |
Course Outline: |
Kubeflow is an open source Kubernetes-native platform for developing, orchestrating, deploying, and running scalable and portable ML workloads — including support for distributed training, data preprocessing and feature engineering, scalable serving, and more. It helps support reproducibility and collaboration in ML workflow lifecycles, allowing you to manage end-to-end orchestration of ML pipelines, to run your workflow in multiple or hybrid environments (such as swapping between on-premises and cloud building blocks, depending upon context), and makes it easy to reuse building blocks across different workflows.
In this workshop, we will deep dive on building and managing machine learning workloads and can scale with kubeflow.
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Who should learn: | Data scientist and engineers, Developers, ML engineers |
Level: | Beginner to Intermediate |
Prerequsite: |