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Learning Series: Machine Learning Engineering and DevOps (every Thursday, 4PM PST)
This Webinar is over
Date | Apr 30, 2020 |
Time | 06:00 PM EDT |
Cost | Free |
Online
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Description
Overview
This series will focus on Machine Learning Engineering (MLE) and DevOps.
This will be a weekly series, each session about 2 hours.
Detailed agenda here
Intended Audience
Developers, DevOps
When
No worries. Go ahead and sign up and we will send you recordings of sessions.
You can see all the past recordings in the 'Machine Learning Engineering' page and on each session link below.
PreRequisites
Sessions
(You can access past recordings in the session links below)
Session 1: A Tour of TensorFlow 2 (2020-Apr-09)
We will discuss
Session 2 : Speeding up training using GPU and TPU (2020-Apr-16)
How to utilze GPU and TPU to speed up trainng
More details and recording
Session 3: Monitoring training (2020-Apr-23)
We will discuss:
Session 4: Training in the cloud with GPU (2020-Apr-30)
Session 5: Model Serving (2020-May-07)
More sessions will be added
Check here for details
Overview
This series will focus on Machine Learning Engineering (MLE) and DevOps.
This will be a weekly series, each session about 2 hours.
Detailed agenda here
Intended Audience
Developers, DevOps
When
- Starting on April 09, 2020, 4pm - 6pm PDT
- Repeats every Thursday, weekly
No worries. Go ahead and sign up and we will send you recordings of sessions.
You can see all the past recordings in the 'Machine Learning Engineering' page and on each session link below.
PreRequisites
- Must have : Development experience
- Nice to have: Python knowledge
- Please bring a reasonably modern laptop (Corporate laptops with overly restrictive firewalls may not work well; Personal laptops are recommended)
- Need to have a Machine Learning Environment setup on your laptop. Please follow this guide
- [nice to have] download our docker image elephantscale/es-training
- Each session is about 2 hours
- These will be intensely hands-on
Sessions
(You can access past recordings in the session links below)
Session 1: A Tour of TensorFlow 2 (2020-Apr-09)
We will discuss
- Changes from TF1
- TF Datasets library
- Efficient loading of data
- Using Google Colab to run ML code
Session 2 : Speeding up training using GPU and TPU (2020-Apr-16)
How to utilze GPU and TPU to speed up trainng
More details and recording
Session 3: Monitoring training (2020-Apr-23)
We will discuss:
- Using callbacks and TensorBoard to monitor training progress
- Set model to train until to a point (90% accuracy) using callbacks instead of fixed set # of epochs
- Send periodic summary statistics using Slack and/or Twilio
- Notify MLE when training is done!
Session 4: Training in the cloud with GPU (2020-Apr-30)
- Choosing GPU resources in the cloud
- Price comparision of different GPU types
- Running a GPU intensive neural net program on the instance; we will use the CIFAR program we developed in the previous session
Session 5: Model Serving (2020-May-07)
- Prepping the model for serving
- Inference architecture
- Simple model serving using Python web service
- Model servers: Tensorflow Model Server, AWS Sagemaker
- Serving a model using Tensorflow Model Server
- Inference load balancing and best practices
More sessions will be added
Check here for details
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