Strata Data Conference

A day in the life of a data scientist: How do we train our teams to get started with AI?

A day in the life of a data scientist: How do we train our teams to get started with AI?

January 27, 2020

With the growing buzz around data science, many professionals want to learn how to become a data scientistthe role Harvard Business Review called the "sexiest job of the 21st century." Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.
A high-performance system for deep learning inference and visual inspection

A high-performance system for deep learning inference and visual inspection

January 27, 2020

Moty Fania and Sergei Kom share their experience and lessons learned implementing an AI inference platform to enable internal visual inspection use cases. The platform is based on open source technologies and was designed for real-time, streaming, and online actuation.
Building a recommendation engine

Building a recommendation engine

January 27, 2020

Recommender systems enhance user experience and business revenue every day. Sophie Watson demonstrates how to develop a robust recommendation engine using a microservice architecture.
A/B testing at Uber: How we built a BYOM (bring your own metrics) platform

A/B testing at Uber: How we built a BYOM (bring your own metrics) platform

January 26, 2020

Every new launch at Uber is vetted via robust A/B testing. Given the pace at which Uber operates, the metrics needed to assess the impact of experiments constantly evolve. Milene Darnis explains how the team built a scalable and self-serve platform that lets users plug in any metric to analyze.
Achieving personalization with LSTMs

Achieving personalization with LSTMs

January 26, 2020

Personalization is a common theme in social networks and ecommerce businesses. Personalization at Uber involves an understanding of how each driver and rider is expected to behave on the platform. Ankit Jain explains how Uber employs deep learning using LSTMs and its huge database to understand and predict the behavior of each and every user on the platform.
Agile for data science teams

Agile for data science teams

January 26, 2020

Agile methodologies have been widely successful for software engineering teams but seem inappropriate for data science teams, because data science is part engineering, part research. Jennifer Prendki demonstrates how, with a minimum amount of tweaking, data science managers can adapt Agile techniques and establish best practices to make their teams more efficient.
AI, ML, and the IoT will destroy the data center and the cloud (just not in the way you think) (sponsored by Cisco)

AI, ML, and the IoT will destroy the data center and the cloud (just not in the way you think) (sponsored by Cisco)

January 26, 2020

DD Dasgupta explores the exciting development of the edge-cloud continuum, which is redefining business models and technology strategies while creating a vast array of new applications that will power the digital age. The continuum is also destroying what we know about the centralized data centers and cloud computing infrastructures that were so vital to the success of the previous computing eras.
Analytics maturity: Industry trends and financial impacts

Analytics maturity: Industry trends and financial impacts

January 26, 2020

Drawing on a recent study of the analytics maturity level of large enterprises by the International Institute for Analytics, Bill Franks discusses how maturity varies by industry, shares key steps organizations can take to move up the maturity scale, and explains how the research correlates analytics maturity with a wide range of success metrics, including financial and reputational measures.
Applying petabyte-scale analytics and machine learning to billions of news reading sessions

Applying petabyte-scale analytics and machine learning to billions of news reading sessions

January 26, 2020

What can we learn from a one-billion-person live poll of the internet? Andrew Montalenti explains how Parse.ly has gathered a unique dataset of news reading sessions of billions of devices, peaking at over two million sessions per minute on thousands of high-traffic news and information websites, and how the company uses this data to unearth the secrets behind online content.
Architecting a data platform for enterprise use

Architecting a data platform for enterprise use

January 26, 2020

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.
Architecting a next-generation data platform

Architecting a next-generation data platform

January 26, 2020

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.
Automating business processes with large-scale knowledge graphs

Automating business processes with large-scale knowledge graphs

January 26, 2020

Mike Tung offers an overview of available open source and commercial knowledge graphs and explains how consumer and business applications are already taking advantage of them to provide intelligent experiences and enhanced business efficiency. Mike then discusses what's coming in the future.
Best practices for developing an enterprise data hub to collect and analyze 1 TB of data a day from a multiple services with Apache Kafka and Google Cloud Platform

Best practices for developing an enterprise data hub to collect and analyze 1 TB of data a day from a multiple services with Apache Kafka and Google Cloud Platform

January 26, 2020

Recruit Group and NTT DATA Corporation have developed a platform based on a data hub, utilizing Apache Kafka. This platform can handle around 1 TB/day of application logs generated by a number of services in Recruit Group. Kenji Hayashida and Toru Sasaki share best practices for and lessons learned about topics such as schema evolution and network architecture.
Bighead: Airbnb's end-to-end machine learning platform

Bighead: Airbnb's end-to-end machine learning platform

January 26, 2020

Atul Kale and Xiaohan Zeng offer an overview of Bighead, Airbnb's user-friendly and scalable end-to-end machine learning framework that powers Airbnb's data-driven products. Built on Python, Spark, and Kubernetes, Bighead integrates popular libraries like TensorFlow, XGBoost, and PyTorch and is designed be used in modular pieces.
Building it beautiful: Analyzing the effectiveness of platform products and marketing at scale

Building it beautiful: Analyzing the effectiveness of platform products and marketing at scale

January 26, 2020

Joshua Laurito explores systems Squarespace built for acquiring and enforcing consistency on obtained data and for inferring conclusions from a companys marketing and product initiatives. Joshua discusses the intricacies of gathering and evaluating marketing and user data, from raising awareness to driving purchases, and shares results of previous analyses.
Cassandra versus cloud databases

Cassandra versus cloud databases

January 25, 2020

Is open source Apache Cassandra still relevant in an era of hosted cloud databases? Jonathan Ellis discusses Cassandras strengths and weaknesses relative to Amazon DynamoDB, Microsoft CosmosDB, and Google Cloud Spanner.
Circuit breakers to safeguard for garbage in, garbage out

Circuit breakers to safeguard for garbage in, garbage out

January 25, 2020

Do your analysts always trust the insights generated by your data platform? Ensuring insights are always reliable is critical for use cases in the financial sector. Sandeep Uttamchandani outlines a circuit breaker pattern developed for data pipelines, similar to the common design pattern used in service architectures, that detects and corrects problems and ensures always reliable insights.
Clouds and containers: Case studies for big data

Clouds and containers: Case studies for big data

January 25, 2020

Once the data has been captured, how can the cloud, containers, and a data fabric combine to build the infrastructure to provide the business insights? Paul Curtis explores three customer deployments that leverage the best of the private clouds and containers to provide a flexible big data environment.
Conda, Docker, and Kubernetes: The cloud-native future of data science (sponsored by Anaconda)

Conda, Docker, and Kubernetes: The cloud-native future of data science (sponsored by Anaconda)

January 25, 2020

The days of deploying Java code to Hadoop and Spark data lakes for data science and ML are numbered. Welcome to the future. Containers and Kubernetes make great language-agnostic distributed computing clusters: it's just as easy to deploy Python as it is Java. Mathew Lodge shows you how.
Conda, Docker, and Kubernetes: The cloud-native future of data science (sponsored by Anaconda)

Conda, Docker, and Kubernetes: The cloud-native future of data science (sponsored by Anaconda)

January 25, 2020

The days of deploying Java code to Hadoop and Spark data lakes for data science and ML are numbered. Welcome to the future. Containers and Kubernetes make great language-agnostic distributed computing clusters: it's just as easy to deploy Python as it is Java. Mathew Lodge shows you how.
Continuous machine learning over streaming data: The story continues.

Continuous machine learning over streaming data: The story continues.

January 25, 2020

Roger Barga, Sudipto Guha, and Kapil Chhabra explain how unsupervised learning with the robust random cut forest (RRCF) algorithm enables insights into streaming data and share new applications to impute missing values, forecast future values, detect hotspots, and perform classification tasks. They also demonstrate how to implement unsupervised learning over massive data streams.
Data discovery and lineage: Integrating streaming data in the public cloud with on-prem, classic data stores, and heterogeneous schema types

Data discovery and lineage: Integrating streaming data in the public cloud with on-prem, classic data stores, and heterogeneous schema types

January 25, 2020

Comcasts streaming data platform comprises ingest, transformation, and storage services in the public cloud, with Apache Atlas for data discovery and lineage. Barbara Eckman explains how Comcast recently integrated on-prem data sources, including traditional data warehouses and RDBMSs, which required its data governance strategy to include relational and JSON schemas in addition to Apache Avro.
Data governance: A big job that's getting bigger

Data governance: A big job that's getting bigger

January 25, 2020

Data governance has grown from a set of mostly data management-oriented technologies in the data warehouse era to encompass catalogs, glossaries, and more in the data lake era. Now new requirements are emerging, and new products are rising to meet the challenge. Andrew Brust tracks data governance's past and present and offers a glimpse of the future.
Data operations problems created by deep learning and how to fix them (sponsored by MapR)

Data operations problems created by deep learning and how to fix them (sponsored by MapR)

January 25, 2020

Drawing on his experience working with customers across many industries, including chemical sciences, healthcare, and oil and gas, Jim Scott details the major impediments to successful completion of deep learning projects and solutions while walking you through a customer use case.
Deep learning on audio in Azure to detect sounds in real time

Deep learning on audio in Azure to detect sounds in real time

January 25, 2020

In this auditory world, the human brain processes and reacts effortlessly to a variety of sounds. While many of us take this for granted, there are over 360 million in this world who are deaf or hard of hearing. Swetha Machanavajhala and Xiaoyong Zhu explain how to make the auditory world inclusive and meet the great demand in other sectors by applying deep learning on audio in Azure.
Deep learning on YARN: Running distributed TensorFlow, MXNet, Caffe, and XGBoost on Hadoop clusters

Deep learning on YARN: Running distributed TensorFlow, MXNet, Caffe, and XGBoost on Hadoop clusters

January 24, 2020

In order to train deep learning and machine learning models, you must leverage applications such as TensorFlow, MXNet, Caffe, and XGBoost. Wangda Tan discusses new features in Apache Hadoop 3.x to better support deep learning workloads and demonstrates how to run these applications on YARN.
Deep learning-based search and recommendation systems using TensorFlow

Deep learning-based search and recommendation systems using TensorFlow

January 24, 2020

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.
Deep learning: Assessing analytics project feasibility and requirements (sponsored by NVIDIA)

Deep learning: Assessing analytics project feasibility and requirements (sponsored by NVIDIA)

January 24, 2020

Ward Eldred offers an overview of the types of analytical problems that can be solved using deep learning and shares a set of heuristics that can be used to evaluate the feasibility of analytical AI projects.