English | Size: 1.03 GB
Genre: eLearning
Detailed conversation about Declarative AutoML with Piero Molino author of Ludwig and co-founder Predibase.
00:00 Intro
00:50 First meeting Piero at Strata in Moscone Center
10:50 Why Declarative AutoML?
12:00 Introducing Declarative ML Systems
16:44 Ludwig: declarative ML systems on PyTorch
18:06 Github Statistics for Ludwig
19:51 Ludwig training example via CLI
21:09 pip install ludwig and using the Programmatic API
26:32 How does Ludwig work?
26:53 Training with Ludwig
27:34 Predicting with Ludwig
29:59 Running Experiments with Ludwig
31:04 ludwig experiment –dataset
32:09 Input – Encoder – Decoder – Output
34:15 ParallelCNN encoding
35:42 Pretrained Transformers: bert distilbert t5 roberta gpt-2
40:44 concat combiner
41:18 Number features decoding
41:37 Vector featuers decoding
41:52 Sequence features decoding
42:30 Training parameters: batch_size, epochs, learning_rate, …
43:07 Preprocessing parameters
43:33 Speaker Verification
44:03 Expected Time of Delivery
44:35 Summarization
45:04 Distributed Training, Ludwig on Ray
45:23 Running Ludwig on Ray
47:18 Ludwig Hyperpot with RayTune (Advanced)
48:51 Ludwig on Kubernetes
49:15 Managed Ludwig in Predibase
49:54 Predibase: Low-code ML, High-Performance
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