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
2019 Tracks
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Hands-on Codelabs & Speakers Office Hours
Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator.
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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.
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Predictive Architectures in the Real World
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.
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Predictive Data Pipelines & Architectures
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix
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Sequential Data: Natural Language, Time Series, and Sound
Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.
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ML in Action
Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud
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Deep Learning in Practice
Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.
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Handling Sequential Data Like an Expert / ML Applied to Operations
Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.
Half-day tracks -
AI Meets the Physical World
Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.
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Hands-on Codelabs & Speakers Office Hours
Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator.
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Papers in Production: Modern CS in the Real World
Groundbreaking papers make real-world impact.