Dates: June 4-6, 2018 | Register here
Prices: starting from USD 1,275,-
Location: San Francisco, the United States
Prices: starting from USD 1,275,-
Location: San Francisco, the United States
Spark + AI Summit 2018
Apache Spark is a powerful open-source processing engine built around speed, ease of use, and sophisticated analytics. Started at UC Berkeley in 2009, it is now developed at the vendor-independent Apache Software Foundation.
Since its release, Spark has seen rapid adoption by enterprises across a wide range of industries.
Internet powerhouses such as Facebook, Hotels.com, Cisco, Microsoft and Netflix have deployed Spark at massive scale, processing multiple petabytes of data on clusters of over 8,000 nodes.
Apache Spark has also become the largest open source community in big data, with over 1000 contributors from 250+ organizations.
What will you get from the summit?
- 9 Spark Training Classes
- 225 Learning Speakers
- 193 Technical Breakout Sessions
- The latest Apache Spark updates
- Apache Spark certification
- Best practices for deep learning and machine learning
Training & Certification
Spark + AI Summit 2018 begins on June 4 with several 1-day training workshops, which include a mix of instruction and hands-on exercises.
Databricks Certification for Apache Spark 2.x is also offered as an exam with optional 1/2-day prep course.
Training or certification is purchased as an add-on to the Conference Pass.
Certification:
- 1/2-day Prep course + Databricks Certification for Apache Spark 2.x Exam
- Databricks Certification for Apache Spark 2.x Exam
Training:
Training courses at Spark+AI Summit utilize Databricks as courseware.
- Data Science with Apache Spark
- Understand and Apply Deep Learning with Keras, TensorFlow & Apache Spark
- Apache Spark Tuning and Best Practices
- Apache Spark Essentials
Who should attend?
- Apache Spark Developers
- Data Scientists
- AI and Deep Learning Developers
- Infrastructure / Site Reliability Engs
- esearchers
- Data
#Spark + AI Summit 2018: The #world’s largest event for the #Apache Spark #community
