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Ken Jones walks you through the core APIs for using Spark, fundamental mechanisms and basic internals of the framework, SQL and other high-level data access tools, and Spark’s streaming capabilities and machine learning APIs.

Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build a multiuse data infrastructure that is not subject to past constraints. Mark Madsen and Todd Walter explore design assumptions and principles and walk you through a reference architecture to use as you work to unify your analytics infrastructure.

Using Customer 360 and the internet of things as examples, Jonathan Seidman and Ted Malaska explain how to architect a modern, real-time big data platform leveraging recent advancements in the open source software world, including components like Kafka, Flink, Kudu, Spark Streaming, and Spark SQL and modern storage engines to enable new forms of data processing and analytics.

From analyzing risk and detecting fraud to predicting payments and improving customer experience, take a deep dive into the ways data technologies are transforming the financial industry.

The honeymoon era of data science is ending, and accountability is coming. Successful data science leaders deliver measurable impact on an increasing share of an enterprise’s KPIs. Joshua Poduska and Patrick Harrison detail how leading organizations have taken a holistic approach to people, process, and technology to build a sustainable competitive advantage

Time series are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations. Join Bruno Gonçalves to learn how to use recurrent neural networks to model and forecast time series and discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches.

To handle real-time big data, you need to solve two difficult problems: how do you ingest that much data and how will you process that much data? Jesse Anderson explores the latest real-time frameworks and explains how to choose the right one for your company.

David Arpin walks you through building a machine learning application, from data manipulation to algorithm training to deployment to a real-time prediction endpoint, using Spark and Amazon SageMaker.

The Unix command line remains an amazing environment for efficiently performing tedious but essential data science tasks. By combining small, powerful command-line tools, you can quickly scrub, explore, and model your data as well as hack together prototypes. Join Jeroen Janssens for a hands-on workshop based on his book Data Science at the Command Line.

Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks.

David Talby, Claudiu Branzan, and Alex Thomas lead a hands-on tutorial for scalable NLP using the highly performant, highly scalable open source Spark NLP library. You’ll spend about half your time coding as you work through four sections, each with an end-to-end working codebase that you can change and improve.

Transparency, auditability, and stability are crucial for business adoption and human acceptance of complex machine learning models. Patrick Hall, Avni Wadhwa, and Mark Chan share practical and productizable approaches for explaining, testing, and visualizing machine learning models using open source, Python-friendly tools such as GraphViz, H2O, and XGBoost.

Dean Wampler and Boris Lublinsky walk you through building streaming apps as microservices using Akka Streams and Kafka Streams. Dean and Boris discuss the strengths and weaknesses of each tool for particular design needs and contrast them with Spark Streaming and Flink, so you'll know when to choose them instead. You'll also discover a few ML model serving ideas along the way.

Tim Berglund leads this solid introduction to Apache Kafka as a streaming data platform. You'll cover the internal architecture, APIs, and platform components like Kafka Connect and Kafka Streams, then finish with an exercise processing streaming data using KSQL, the new SQL-like declarative stream processing language for Kafka.

Vartika Singh, Alan Silva, Alex Bleakley, Steven Totman, Mirko Kämpf, and Syed Nasar outline approaches for preprocessing, training, inference, and deployment across datasets (time series, audio, video, text, etc.) that leverage Spark, its extended ecosystem of libraries, and deep learning frameworks.

Python lets you solve data science problems by stitching together packages from the Python ecosystem, but it can be difficult to assemble the right tools to solve real-world problems. James Bednar walks you through using the 15+ packages covered by the new PyViz.org initiative to make it simple to build interactive plots and dashboards, even for large, streaming, and highly multidimensional data.

TensorFlow and Keras are popular libraries for training deep models due to hardware accelerator support. Brian Foo, Jay Smith, and Holden Karau explain how to bring deep learning models from training to serving in a cloud production environment. You'll learn how to unit-test, export, package, deploy, optimize, serve, monitor, and test models using Docker and TensorFlow Serving in Kubernetes.

Hear practical insights from household brands and global companies: the challenges they tackled, approaches they took, and the benefits—and drawbacks—of their solutions.

New regulations such as GDPR are driving new compliance, governance, and security challenges for big data. Infosec and security groups must ensure a consistently secured and governed environment across multiple workloads. Mark Donsky, Syed Rafice, Mubashir Kazia, Ifigeneia Derekli, and Camila Hiskey share hands-on best practices for meeting these challenges, with special attention paid to GDPR.

There is mounting evidence that the widespread deployment of machine learning and artificial intelligence in business and government applications is reproducing or even amplifying existing prejudices and social inequalities. Aileen Nielsen demonstrates how to identify and avoid bias and other unfairness in your analyses.

Arun Kejariwal and Karthik Ramasamy lead a journey through the landscape of state-of-the-art systems for each stage of an end-to-end data processing pipeline, covering messaging frameworks, streaming computing frameworks, storage frameworks for real-time data, and more. They also share case studies from the IoT, gaming, and healthcare and their experience operating these systems at internet scale.

Want to learn how to use Amazon's big data web services to launch your first big data application in the cloud? Jorge Lopez, Radhika Ravirala, Paul Sears, and Bruno Faria walk you through building a big data application using a combination of open source technologies and AWS managed services.

Using interesting, diverse publicly available datasets and actual problems in astronomy research, Viviana Acquaviva leads an intermediate tutorial on machine learning. You'll learn how to customize algorithms and evaluation metrics required by scientific applications and discover best practices for choosing, developing, and evaluating machine learning algorithms in "real-world" datasets.

Attend this tutorial to learn how to successfully run a data analytics pipeline in the cloud and integrate data engineering and data analytic workflows and explore considerations and best practices for data analytics pipelines in the cloud. Along the way, you'll see how to share metadata across workloads in a big data PaaS.

Zachary Glassman leads a hands-on dive into building intelligent business applications using machine learning, walking you through all the steps of developing a machine learning pipeline. You'll explore data cleaning, feature engineering, model building and evaluation, and deployment and extend these models into two applications using a real-world dataset.

Abhishek Kumar and Vijay Srinivas Agneeswaran offer an introduction to deep learning-based recommendation and learning-to-rank systems using TensorFlow. You'll learn how to build a recommender system based on intent prediction using deep learning that is based on a real-world implementation for an ecommerce client.

Janet Forbes, Danielle Leighton, and Lindsay Brin lead a primer on crafting well-conceived data science projects that uncover valuable business insights. Using case studies and hands-on skills development, Janet, Danielle, and Lindsay walk you through essential techniques for effecting real business change.

Advancing your career in data science requires learning new languages and frameworks—but learners face an overwhelming array of choices, each with different syntaxes, conventions, and terminology. Ian Cook simplifies the learning process by elucidating the abstractions common to these systems. Through hands-on exercises, you'll overcome obstacles to getting started using new tools.

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. Dylan Bargteil introduces TensorFlow's capabilities through its Python interface.

Acquiring machine learning (ML) technology is relatively straightforward, but ML must be applied to be useful. In this one-day boot camp that is equal parts hackathon, presentation, and group participation, Jerry Overton, Ashim Bose, and Samir Sehovic teach you how to apply advanced analytics in ways that reshape the enterprise and improve outcomes.

Dan Crankshaw offers an overview of the current challenges in deploying machine applications into production and the current state of prediction serving infrastructure. He then leads a deep dive into the Clipper serving system and shows you how to get started.

Carolyn Duby shows you how to find the cybersecurity threat needle in your event haystack using Apache Metron: a real-time, horizontally scalable open source platform. After this interactive overview of the platform's major features, you'll be ready to analyze your own haystack back at the office.

Enjoy delicious snacks and beverages with fellow Strata attendees, speakers, and sponsors at the Opening Reception, happening immediately after tutorials on Tuesday.