Home Courses Applied Machine Learning Online Course Design and Productionization of low latency ML systems

Design and Productionization of low latency ML systems

Instructor: Applied AI Course

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KubeFlow: Pipelines using Kale, Rok, Katib and KfServing
Live Session(20th Feb 2022): Scenario based Interview Questions for ML engineer roles
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
2.1
Calibration of Models:Need for calibration
8 min
2.2
Calibration Plots.
17 min
2.3
Platt’s Calibration/Scaling.
8 min
2.4
Isotonic Regression
11 min
2.5
Code Samples
5 min
2.6
Modeling in the presence of outliers: RANSAC
13 min
2.7
Retraining models periodically.
8 min
2.8
A/B testing.
22 min
2.9
VC dimension
22 min
2.10
Data Science Life cycle
17 min
2.11
Productionization and deployment of Machine Learning Models
17 min
2.12
Productionization and deployment + Spark
96 min
2.13
Hands on Live Session: Deploy an ML model using Flask APIs on AWS
125 min
2.14
Building web apps for ML/AI using StreamLit
101 min
2.15
Building web apps for ML/AI using StreamLit - II
66 min
2.16
ML Model productionization using Heroku
120 min
2.17
Amazon Sagemaker--Part 1
147 min
2.18
Amazon sagemaker--part 2
161 min
2.19
SageMaker Part 3: Distributed training and Deep Learning
127 min
2.20
SageMaker Part 4: Spark and Pipelines
121 min
2.21
Amazon SageMaker : Part 5 [Miscellaneous topics]
97 min
2.22
Design and Productionization of low latency ML systems
93 min