Deep Learning with Databricks
This course begins by covering the basics of neural networks and the tensorflow.keras API. We will focus on how to leverage Spark to scale our models, including distributed training, hyperparameter tuning, and inference, while leveraging MLflow to track, version, and manage these models. We will deep dive into distributed deep learning, including hands-on examples to compare and contrast various techniques for distributed data preparation, including Petastorm and TFRecord, as well as distributed training techniques such as Horovod and spark-tensorflow-distributor. To better understand the model’s predictions, you will apply model interpretability libraries. Further, you will learn the concepts behind Convolutional Neural Networks (CNNs) and transfer learning, and apply them to solve image classification tasks. We will wrap up the course by covering Recurrent Neural Networks (RNNs) and attention-based models for natural language processing (NLP) applications.
2 full days or 4 half days
Build deep learning models using tensorflow.keras
Tune hyperparameters at scale with Hyperopt and Spark
Track, version, and manage experiments using MLflow
Perform distributed inference at scale using pandas UDFs
Scale and train distributed deep learning models using Horovod
Apply model interpretability libraries, such as SHAP, to understand model predictions
Use CNNs and transfer learning for image classification tasks
Use RNNs, attention-based models, and transfer learning for NLP tasks
Intermediate experience with Python and pandas (or completion of Introduction to Python for Data Science & Data Engineering)
Familiarity with Apache Spark (or completion of Apache Spark Programming)
Working knowledge of machine learning and data science (or completion of Scalable Machine Learning with Apache Spark)
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Neural network and tf.keras fundamentals
Improve models by adding data standardization, callbacks, checkpointing, etc.
Track and version models with MLflow
Distributed inference with pandas UDFs
Distributed hyperparameter tuning with Hyperopt
Large scale data preparation with Petastorm
Distributed model training with Horovod and Petastorm
Model interpretability with SHAP
CNNs for image classification and transfer learning
Distributed training with TFRecord using spark-tensorflow-distributor
Deploy REST endpoint using MLflow Model Serving on Databricks
Textual embeddings, RNNs, attention-based models, and transfer learning for named entity recognition (NER)