20–23 April 2020

In-depth Training Courses

These expert-led presentations on Monday 20 April and Tuesday 21 April give you a chance to dive deep into the subject matter. These courses often sell out, so reserve your spot today.

2-Day Training (Mon & Tue) 1-Day Training (Mon) 1-Day Training (Tue)

Add to your personal schedule
9:00 – 17:00 Tuesday, April 21
Oliver Hughes (Pivotal), Alberto C. Ríos (Pivotal)
Location: Capital Suite 11
Today's data engineer needs a deep understanding of the key tools and concepts within the vast, rapidly evolving Kubernetes ecosystem. This training will provide developers with a thorough grounding on Kubernetes concepts, suggest best practices and get hands-on with some of the essential tooling. Topics will include Read more.
Add to your personal schedule
9:00 – 17:00 Tuesday, April 21
Pramod Singh (Walmart Labs ), Rajesh Shreedhar Bhat (Walmart Labs)
Location: Capital Suite 12
With the latest developments and improvements in the field of deep learning and artificial intelligence, many demanding natural language processing tasks become easy to implement and execute. Text summarization is one of the tasks that can be done using attention networks. Read more.
Add to your personal schedule
9:00 – 17:00 Tuesday, April 21
Alex Thomas (John Snow Labs), Maziyar Panahi (John Snow Labs)
Location: Capital Suite 10
Alex Thomas and Maziyar Panahi detail the application of the latest advances in deep learning for common natural language processing (NLP) tasks such as named entity recognition, document classification, sentiment analysis, spell checking, and OCR. You'll learn to build complete text analysis pipelines using the highly performant, scalable, open source Spark NLP library in Python. Read more.
Add to your personal schedule
9:00 – 17:00 Tuesday, April 21
Dean Wampler (Anyscale)
Location: Capital Suite 2
Surprisingly, there's no simple way to scale up Python applications from your laptop to the cloud. Ray is an open source framework for parallel and distributed computing that makes it easy to program and analyze data at any scale by providing general-purpose high-performance primitives. Dean Wampler teaches you how to use Ray to scale up Python applications, data processing, and machine learning. Read more.
Add to your personal schedule
9:00 – 17:00 Tuesday, April 21
Janisha Anand (Amazon Web Services), Nikki Rouda (Amazon Web Services)
Location: Capital Suite 14
Janisha Anand and Nikki Rouda teach you how to build a serverless data lake on AWS. You'll ingest Instacart's public dataset to the data lake and draw valuable insights on consumer grocery shopping trends. You’ll build data pipelines, leverage data lake storage infrastructure, configure security and governance policies, create a persistent catalog of data, perform ETL, and run an ad hoc analysis. Read more.
Add to your personal schedule
9:00 – 17:00 Tuesday, April 21
Nathalie Rauschmayr (Amazon Web Services), Satadal Bhattacharjee (Amazon Web Services), Aparna Elangovan (Amazon Web Services)
Location: S11 D
Build, train, and deploy a deep learning model on Amazon SageMaker with Nathalie Rauschmayr, Satadal Bhattacharjee, and Aparna Elangovan, and learn how to use some of the latest SageMaker features such as SageMaker Debugger and SageMaker Model Monitor. Read more.
Add to your personal schedule
10:00 - 17:30 Monday, 20 April & 9:00 - 17:00 Tuesday, 21 April
Michael Cullan (Pragmatic Institute)
Location: Capital Suite 7
The TensorFlow library provides for the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. Read more.
Add to your personal schedule
10:00 - 17:30 Monday, 20 April & 9:00 - 17:00 Tuesday, 21 April
Nikki Rouda (Amazon Web Services)
Location: Capital Suite 13
Nikki Rouda walks you through the steps of building a data lake on Amazon S3 using different ingestion mechanisms, performing incremental data processing on the data lake to support transactions on S3, and securing the data lake with fine-grained access control policies. Read more.
Add to your personal schedule
10:00 - 17:30 Monday, 20 April & 9:00 - 17:00 Tuesday, 21 April
Grishma Jena (IBM)
Location: Capital Lounge 1/2
Data science is rapidly changing every industry. This has resulted in a shift away from traditional software development and toward data-driven decision making. Grishma Jena uses Python to extract, wrangle, explore, and understand data so you can leverage it in the real world. Read more.
Add to your personal schedule
10:00 - 17:30 Monday, 20 April & 9:00 - 17:00 Tuesday, 21 April
Thomas Nield (Nield Consulting Group)
Location: Capital Suite 15
There's been an explosion of tools for machine learning, but two have emerged as practical go-to solutions: scikit-learn and Apache Spark. Using Python, Thomas Nield leads a deep dive into examples in parallel (no pun intended) for both of these tools and learn how to tackle machine learning at small, medium, and large scales. Read more.
Add to your personal schedule
10:00 - 17:30 Monday, 20 April & 9:00 - 17:00 Tuesday, 21 April
Hugo Bowne-Anderson (DataCamp)
Location: Capital Suite 16
Hugo Bowne-Anderson walks you through the basics of the math and stats you need to know to do data science and interpret your results correctly (the calculus, linear algebra, statistical intuition, and probabilistic thinking, among others) through hands-on examples from machine learning, online experiments and hypothesis testing, natural language processing, data ethics, and more. Read more.
Add to your personal schedule
9:00 – 17:00 Tuesday, April 21
Russell Jurney (Data Syndrome)
Location: Capital Suite 4
Russell Jurney surveys machine learning techniques from across the field of unsupervised learning and explains the theory behind each technique as well as working examples in Python using open source software. Read more.
Add to your personal schedule
9:00 – 17:00 Tuesday, April 21
Matt Kirk (YourChiefScientist.com)
Location: Capital Suite 8
Join us as we dig into the theory, the practice, and the implementation of this highly promising field of machine learning. Read more.

Contact us

confreg@oreilly.com

For conference registration information and customer service

partners@oreilly.com

For more information on community discounts and trade opportunities with O’Reilly conferences

Become a sponsor

For information on exhibiting or sponsoring a conference

pr@oreilly.com

For media/analyst press inquires