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Lecture 23 of 32

Streaming datasets

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Section 1: Introduction4 videos

Introduction

1m

What you should know

1m

Model walkthrough

4m

GitHub Codespaces

3m
Section 2: Batch Systems8 videos

What are batch features

3m

Getting started with batch features in Codespaces

18m

Making predictions with our model

6m

Challenge Feature X

1m

Advantages and disadvantages of batch forecasting

4m

Building a store for batch features

11m

Training our model to predict

15m

Solution Feature X

6m
Section 3: Near Real-Time Systems9 videos

What are near real-time systems

2m

Requirements for near real-time forecasting systems

3m

Recalculating features

6m

Frequency considerations

3m

Online prediction

3m

End-to-end example

3m

Advantages and disadvantages of near real-time

2m

Challenge Feature Y

2m

Solution Feature Y

8m
Section 4: Real-Time Systems8 videos

Requirements of real-time forecasting systems

4m

Streaming datasets

5mNow Playing

What are real-time forecasting systems

3m

Online features

5m

Real-time forecasting and latency considerations

1m

Challenge Feature Z

2m

Advantages and disadvantages of real-time forecasting

4m

Solution Feature Z

2m
Section 5: Evaluating Time Series Forecasting Systems2 videos

Evaluating forecasting models

4m

Best practices for retraining time series models

4m
Section 6: Conclusion1 videos

Next steps for AI forecasting

2m