Spark + AI Summit 2018 features a number of 1-day training workshops that include a mix of instruction and hands-on exercises to help you improve your Apache Spark™ skills. Courses will run on either Azure or AWS. You will be able to select the platform at registration.
Training is offered as an add-on to the Conference Pass.
Students will need to bring their own laptop with Chrome or Firefox Browsers and unfettered access to *.databricks.com.
The Data Science with Apache Spark workshop will show how to use Apache Spark to perform exploratory data analysis (EDA), develop machine learning pipelines, and use the APIs and algorithms available in the Spark MLlib DataFrames API. It is designed for software developers, data analysts, data engineers, and data scientists.
It will also cover parallelizing machine learning algorithms at a conceptual level. The workshop will take a pragmatic approach, with a focus on using Apache Spark for data analysis and building models using MLlib, while limiting the time spent on machine learning theory and the internal workings of Spark.
We will work through examples using public datasets that will show you how to apply Apache Spark to help you iterate faster and develop models on massive datasets. This workshop will provide you with tools to be productive using Spark on practical data analysis tasks and machine learning problems. After completing this workshop you should be comfortable using DataFrames, the DataFrames MLlib API, and related documentation. These building blocks will enable you to use Apache Spark to solve a variety of data analysis and machine learning tasks.
Some experience coding in Python or Scala and a basic understanding of data science topics and terminology are recommended. Experience using Spark and familiarity with the concept of a DataFrame is helpful.
Brief conceptual reviews of data science techniques will be performed before the techniques are used. Labs and demos will be available in both Python and Scala.
Instructor: Adam Breindel
This Deep Learning workshop introduces the conceptual background as well as implementation for key architectures in neural network machine learning models. We will see how and why deep learning has become such an important and popular technology, and how it is similar to and different from other machine learning models as well as earlier attempts at neural networks.
We’ll see how deep learning models can be used to enhance your traditional business analytics, in addition to covering the famous cases like image recognition, language processing, and autonomous agents. Most of our models will be built with the Keras API/Library, but we’ll also take a look at “what’s under the hood” with TensorFlow. But we won’t just hack demos: our goal is to develop an intuition for the key concepts and issues at play in deep learning.
The class will also feature a discussion about using Apache Spark for training and inference, and other deployment / operational concerns. Along the way, we’ll hopefully explain enough ideas and terminology that you’ll be comfortable going further with deep learning on your own!
Familiarity with the basics of Python and with common ideas and techniques in machine learning / predictive analytics. You should be be familiar with classification vs. regression problems, supervised vs. unsupervised learning, bias-variance tradeoff, and common evaluation metrics like RMSE, precision, and recall.
No prior deep learning knowledge, vector calculus, or Spark experience is required.
This 1-day course is for data engineers, analysts, architects, dev-ops, and team-leads interested in troubleshooting and optimizing Apache Spark applications. It covers troubleshooting, tuning, best practices, anti-patterns to avoid, and other measures to help tune and troubleshoot Spark applications and queries.
Each topic includes lecture content along with hands-on use of Spark through an elegant web-based notebook environment. Inspired by tools like IPython/Jupyter, notebooks allow attendees to code jobs, data analysis queries, and visualizations using their own Spark cluster, accessed through a web browser. Students may keep the notebooks and continue to use them with the free Databricks Community Edition offering; all examples are guaranteed to run in that environment. Alternatively, each notebook can be exported as source code and run within any Spark environment.
This 1-day course is for data engineers, analysts, architects, data scientist, software engineers, IT operations, and technical managers interested in a brief hands-on overview of Apache Spark.
The course provides an introduction to the Spark architecture, some of the core APIs for using Spark, SQL and other high-level data access tools, as well as Spark’s streaming capabilities and machine learning APIs. The class is a mixture of lecture and hands-on labs.
Each topic includes lecture content along with hands-on labs in the Databricks notebook environment. Students may keep the notebooks and continue to use them with the free Databricks Community Edition offering after the class ends; all examples are guaranteed to run in that environment.
This 1/2 day lecture is for anyone seeking to become a Databricks Certified Apache Spark Developer or Databricks Certified Apache Spark Systems Architect. It includes test-taking strategies, sample questions, preparation guidelines and exam requirements. The primary goal of this course is to help potential applicants understand the breadth and depth to which individuals will be tested and to provide guidelines as to how to prepare for the exam.
Each topic includes lecture content and reference material presented in the Databricks notebook environment. Students may keep the notebooks and continue to use them with the free Databricks Community Edition offering after the class ends.
Attendees who select the prep course will take the exam after the course is completed.
Please Note: attending the certification prep course should NOT, by itself, be considered sufficient preparation for successfully taking the Databricks Apache Spark certification exam.
Databricks Certified Developer for Apache Spark 2.x—validates your overall knowledge on Apache Spark and ensures employers that you are up-to-date with the fast moving apache project with significant features and enhancements being rolled out rapidly. The test is about 90 minutes with a series of randomly generated questions.
A testing room will be available from 11:45 am- 5:00 pm on Tuesday and Wednesday during the Summit. When registering, you will select the day when you would like to take your exam. Entrance to the room will be on a rolling basis. As a seat becomes available we will let the next person in.
No outside phones or computers will be allowed in the testing room. We will provide a computer for the exam.
