New course! Orchestrating Workflows for GenAI Applications
Skills:
AI Workflow Automation85%
Learn more: https://bit.ly/45BbJ0D
When building generative AI applications, it's common to start in a Jupyter Notebook or script, but notebooks aren’t built for automation, monitoring, or scale. To run GenAI workflows reliably in production, you need orchestration.
In Orchestrating Workflows for GenAI Applications, a new short course taught by Kenten Danas and Tamara Fingerlin from Astronomer, you’ll learn how to transform a RAG prototype into a robust, automated pipeline using Apache Airflow 3.
You’ll build two production-ready workflows: one to ingest and embed book descriptions into a vector database, and another to query that database to recommend books, each composed of discrete, trackable tasks managed by Airflow dags.
What you’ll learn includes:
- Scheduling pipelines using both time-based and event-driven triggers
- Parallelizing tasks with dynamic task mapping
- Adding retries, alerts, and backfills to ensure reliability
- Scaling orchestration using real-world techniques from apps like Astronomer’s Ask Astro
This course is ideal for AI builders who want to move from prototype to production. No prior Airflow experience required.
Enroll now: https://bit.ly/45BbJ0D
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Forward and Backward Propagation (C1W4L06)
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deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
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deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
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deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
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deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
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deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
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deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
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Using an Appropriate Scale (C2W3L02)
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Gradient Checking (C2W1L13)
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Gradient Checking Implementation Notes (C2W1L14)
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Learning Rate Decay (C2W2L09)
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Understanding Mini-Batch Gradient Dexcent (C2W2L02)
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Mini Batch Gradient Descent (C2W2L01)
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The Problem of Local Optima (C2W3L10)
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Exponentially Weighted Averages (C2W2L03)
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Tuning Process (C2W3L01)
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Understanding Exponentially Weighted Averages (C2W2L04)
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Bias Correction of Exponentially Weighted Averages (C2W2L05)
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Gradient Descent With Momentum (C2W2L06)
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Normalizing Activations in a Network (C2W3L04)
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Hyperparameter Tuning in Practice (C2W3L03)
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Adam Optimization Algorithm (C2W2L08)
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RMSProp (C2W2L07)
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Fitting Batch Norm Into Neural Networks (C2W3L05)
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Why Does Batch Norm Work? (C2W3L06)
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Batch Norm At Test Time (C2W3L07)
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Softmax Regression (C2W3L08)
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Deep Learning Frameworks (C2W3L10)
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Neural Network Overview (C1W3L01)
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Training Softmax Classifier (C2W3L09)
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Why Deep Representations? (C1W4L04)
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Gradient Descent For Neural Networks (C1W3L09)
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Neural Network Representations (C1W3L02)
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TensorFlow (C2W3L11)
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Activation Functions (C1W3L06)
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Explanation For Vectorized Implementation (C1W3L05)
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Getting Matrix Dimensions Right (C1W4L03)
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Understanding Dropout (C2W1L07)
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Building Blocks of a Deep Neural Network (C1W4L05)
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Why Non-linear Activation Functions (C1W3L07)
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Computing Neural Network Output (C1W3L03)
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Backpropagation Intuition (C1W3L10)
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Train/Dev/Test Sets (C2W1L01)
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Deep L-Layer Neural Network (C1W4L01)
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Random Initialization (C1W3L11)
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Other Regularization Methods (C2W1L08)
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Normalizing Inputs (C2W1L09)
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Derivatives Of Activation Functions (C1W3L08)
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Parameters vs Hyperparameters (C1W4L07)
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Vectorizing Across Multiple Examples (C1W3L04)
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What does this have to do with the brain? (C1W4L08)
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Dropout Regularization (C2W1L06)
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Vanishing/Exploding Gradients (C2W1L10)
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Basic Recipe for Machine Learning (C2W1L03)
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Bias/Variance (C2W1L02)
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Forward Propagation in a Deep Network (C1W4L02)
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Weight Initialization in a Deep Network (C2W1L11)
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Numerical Approximations of Gradients (C2W1L12)
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Regularization (C2W1L04)
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Why Regularization Reduces Overfitting (C2W1L05)
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