Discover everything you need to build robust machine learning applications with Spark 2.0
About This Book
- Get the most up-to-date book on the market that focuses on design, engineering, and scalable solutions in machine learning with Spark 2.0.0
- Use Spark's machine learning library in a big data environment
- You will learn how to develop high-value applications at scale with ease and a develop a personalized design
Who This Book Is For
This book is for data science engineers and scientists who work with large and complex data sets. You should be familiar with the basics of machine learning concepts, statistics, and computational mathematics. Knowledge of Scala and Java is advisable.
What You Will Learn
- Get solid theoretical understandings of ML algorithms
- Configure Spark on cluster and cloud infrastructure to develop applications using Scala, Java, Python, and R
- Scale up ML applications on large cluster or cloud infrastructures
- Use Spark ML and MLlib to develop ML pipelines with recommendation system, classification, regression, clustering, sentiment analysis, and dimensionality reduction
- Handle large texts for developing ML applications with strong focus on feature engineering
- Use Spark Streaming to develop ML applications for real-time streaming
- Tune ML models with cross-validation, hyperparameters tuning and train split
- Enhance ML models to make them adaptable for new data in dynamic and incremental environments
Data processing, implementing related algorithms, tuning, scaling up and finally deploying are some crucial steps in the process of optimising any application.
Spark is capable of handling large-scale batch and streaming data to figure out when to cache data in memory and processing them up to 100 times faster than Hadoop-based MapReduce. This means predictive analytics can be applied to streaming and batch to develop complete machine learning (ML) applications a lot quicker, making Spark an ideal candidate for large data-intensive applications.
This book focuses on design engineering and scalable solutions using ML with Spark. First, you will learn how to install Spark with all new features from the latest Spark 2.0 release. Moving on, you'll explore important concepts such as advanced feature engineering with RDD and Datasets. After studying developing and deploying applications, you will see how to use external libraries with Spark.
In summary, you will be able to develop complete and personalised ML applications from data collections,model building, tuning, and scaling up to deploying on a cluster or the cloud.
Style and approach
This book takes a practical approach where all the topics explained are demonstrated with the help of real-world use cases.
Table of Contents
Chapter 1. Introduction to Data Analytics with Spark
Chapter 2. Machine Learning Best Practices
Chapter 3. Understanding the Problem by Understanding the Data
Chapter 4. Extracting Knowledge through Feature Engineering
Chapter 5. Supervised and Unsupervised Learning by Examples
Chapter 6. Building Scalable Machine Learning Pipelines
Chapter 7. Tuning Machine Learning Models
Chapter 8. Adapting Your Machine Learning Models
Chapter 9. Advanced Machine Learning with Streaming and Graph Data
Chapter 10. Configuring and Working with External Libraries