Описание: If you are a developer looking to build machine learning models without spending months and years learning machine learning prerequisites, look no further than AutoML. This practical and concise guide will show you how to build automated models for regression and classification, both with traditional algorithms and neural networks.
Описание: Chapter 1: Introduction to Apache Spark Chapter Goal: Provide an overview of Apache SparkNo of pages 15Sub -Topics1. Overview & history2. Spark concepts & architecture3. Spark Unified Stack4. Apache Spark applications Chapter 2: Working with Apache SparkChapter Goal: Provide details about different ways of interacting with Apache SparkNo of pages: 35Sub - Topics 1. Downloading and Installing Apache Spark2. Exploring Apache Spark using Spark shells3. Exploring Apache Spark using Databricks4. Exploring Apache Spark source code Chapter 3: Spark SQL - FoundationChapter Goal: Provide an overview to Spark SQL componentNo of pages: 60Sub - Topics 1. Overview & architecture2. Introduction to DataFrames Structured APIs3. Reading & writing data with Spark SQL data sources4. Introduction to datasets Chapter 4: Spark SQL - AdvanceChapter Goal: Go over the advanced features in Spark SQLNo of pages: 50Sub - Topics: 1. Working with aggregations2. Joining data 3. Working with analytics functions4. Explore Spark SQL catalyst optimizer Chapter 5: Optimizing Apache Spark ApplicationsChapter Goal: Go over tips and techniques for dealing with performance issues No of pages: 30Sub - Topics: 1. Common performance issues2. Speed up performance by leveraging in-memory computation3. Understand the different support joins in Spark4. Leverage Spark UI to diagnose performance issue Chapter 6: Structured Streaming - FoundationChapter Goal: Overview of Structured Streaming processing engineNo of pages: 50Sub - Topics: 1. General streaming processing concepts2. Structured Streaming programming model3. Working with streaming data sources and sinks4. Understanding output modes and triggers Chapter 7: Structured Streaming - AdvancedChapter Goal: Cover complex issues in streaming processingNo of pages: 40Sub - Topics: 1. Streaming processing with event time2. Stateful streaming processing3. Handling duplicate data4. Monitoring streaming processing applications Chapter 8: Machine Learning with Apache SparkChapter Goal: How to developing Machine Learning applications using Spark MLlibNo of pages: 60Sub - Topics: 1. Machine learning overview2. Taking a tour of supported machine learning algorithms3. Building machine learning pipelines4. Machine learning tasks in action5. Parameters tuning Chapter 9: Machine Learning Application Development w/ MLflowChapter Goal: Using MLflow to manage the Machine Learning development lifecycle No of pages: 25Sub - Topics: 1. Overview of MLflow2. Tracking machine learning development experiments3. Managing & deploying machine learning models4. Leveraging Spark for batch modeling predictions
Описание: Machine learning engineering is an in-demand skill set, and it can be difficult to find a helpful guide on the topic. This book will help you solve business problems by addressing the pain points in creating standardized pipelines for taking proof-of-concept ML models to production and producing trustworthy results.
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