Workshop: Building Recommender Systems w/ Apache Spark 2.x
Apache Spark has become one of the must-know big data technologies due to its speed, ease of use, and versatility. Spark can be used for performing data analysis and building big-data applications. Increasingly, companies are leveraging Apache Spark to build intelligent applications that use Machine Learning techniques. This workshop will start with covering the major features in Spark 2.x and then focus on building a recommendation system using Spark MLlib library. It will include focused and interactive hands-on exercises.
Signup for a free Databricks Community Edition account - https://community.cloud.
Tutorial materials can be found at - https://sites.google.com/view/apache-spark-workshop/
Here is what you can expect to learn from this tutorial:
- Spark architecture and execution model
- Structured data processing with Spark SQL, DataFrames, and Datasets
- Streaming processing with Structure Streaming
- Major concepts and utilities in Spark ML library for building intelligent applications
- Build a recommender system using Spark ML library
Other Workshops:
2019 Tracks
-
Groking Timeseries & Sequential Data
Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.
-
Deep Learning in Practice
Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.
-
AI Meets the Physical World
Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.
-
Papers in Production: Modern CS in the Real World
Groundbreaking papers make real-world impact.
-
Solving Software Engineering Problems with Machine Learning
Interesting machine learning use cases changing how we develop software today, including planned topics touching on infrastructure optimization, developer experience, security, and more.
-
Predictive Architectures in the Real World
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.