Home Courses Applied Machine Learning Online Course SageMaker Part 3: Distributed training and Deep Learning

SageMaker Part 3: Distributed training and Deep Learning

Instructor: Applied AI Course Duration: 127 mins

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Amazon sagemaker--part 2
SageMaker Part 4: Spark and Pipelines
1.1
Introduction
15 min
1.2
Moving window for Time Series Data
15 min
1.3
Fourier decomposition
22 min
1.4
Deep learning features: LSTM
8 min
1.5
Image histogram
15 min
1.6
Keypoints: SIFT.
10 min
1.7
Deep learning features: CNN
4 min
1.8
Relational data
10 min
1.9
Graph data
12 min
1.10
Indicator variables
7 min
1.11
Feature binning
14 min
1.12
Interaction variables
9 min
1.13
Mathematical transforms
4 min
1.14
Model specific featurizations
9 min
1.15
Feature orthogonality
12 min
1.16
Domain specific featurizations
4 min
1.17
Feature slicing
10 min
1.18
Kaggle Winners solutions
7 min
3.1
Live session on Time Series Analysis and Forecasting
3.2
Testing and Debugging ML/AI systems end to end
3.3
Interview Questions on Productionization, Deployment
3.4
Interview Questions on Productionization and Deployment-PART II
3.5
KubeFlow: Architecture and Components
3.6
KubeFlow: Installation, Setup and Config
3.7
KubeFlow: Dashboard, Notebook Servers and Pipelines
3.8
KubeFlow: Pipelines using Kale, Rok, Katib and KfServing
3.9
Design and Productionization of low latency ML systems
3.10
Live Session(20th Feb 2022): Scenario based Interview Questions for ML engineer roles
102 min
3.11
Live Session(11th Sep 2022): ML Deployment Scenarios
3.12
Live Session(29th Nov 2022): Practical Time series Forecasting