Deep Learning with Databricks

This course begins by covering the basics of neural networks and the tensorflow.keras API. We will then focus on using Spark to scale our models, including distributed training, hyperparameter tuning, and inference, and the meanwhile leveraging MLflow to track, version, and manage these models. You will apply model interpretability libraries to explain model predictions. 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.

Skill Level

Professional

Duration

16h

Prerequisites

Intermediate experience with Python/pandas, Experience building machine learning models, Familiarity with Apache Spark

Outline

Day 1

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
Distributed model training with Horovod

Day 2

Distributed model training with Horovod and Petastorm
Model interpretability with LIME and SHAP
CNNs for image classification and transfer learning
Deploy REST endpoint using MLflow Model Serving on Databricks
Textual embeddings, RNNs, attention-based models, and transfer learning for named entity recognition (NER)

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Upcoming Public Classes

Machine Learning Practitioner

Machine Learning Model Deployment

This course is designed to introduce three primary machine learning deployment strategies and illustrate the implementation of each strategy on Databricks. Following an exploration of the fundamentals of model deployment, the course delves into batch inference, offering hands-on demonstrations and labs for utilizing a model in batch inference scenarios, along with considerations for performance optimization. The second part of the course comprehensively covers pipeline deployment, while the final segment focuses on real-time deployment. Participants will engage in hands-on demonstrations and labs, deploying models with Model Serving and utilizing the serving endpoint for real-time inference.

Streaming with Structured Streaming and Delta Live Tables

In this course, you’ll learn about processing data with Structure Streaming and Auto Loader. These tools allow you to incrementally process data to power analytical insights as well as enforce data quality upon incoming data. You’ll also learn how to design workloads that perform ETL in the Databricks Lakehouse Platform with Delta Live Tables (DLT).

Machine Learning Practitioner

Machine Learning in Production

In this course, you will learn the best practices for managing machine learning experiments and models with MLflow. There are two main components in this course: (i) using MLflow to track the machine learning lifecycle, package models for deployment, and manage model versions (ii) examining various production issues, different deployment paradigms, and post-production concerns. By the end of this course, you will have built an end-to-end pipeline to log, deploy, and monitor machine learning models.

Career Workshop/

March 20

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