The O’Reilly Data Show Podcast: Ameet Talwalkar on large-scale machine learning.
Using silly datasets as examples, Janelle Shane talks about ways that algorithms fail.
Eric Colson explains why companies must now think very differently about the role and placement of data science in organizations.
Anoop Dawar shares principles successful companies are using to inspire an insight-driven ethos and build data-competent organizations.
William Vambenepe walks through an interesting use case of machine learning in action and discusses the central role AI will play in big data analysis moving forward.
Ajey Gore explains why GO-JEK is focusing its attention beyond urban Indonesia to help people across the country’s rural areas.
Seth Stephens-Davidowitz explains how to use Google searches to uncover behaviors or attitudes that may be hidden from traditional surveys.
How to find promising candidates for upskilling within your organization.
Tobias Ternstrom explains why you should objectively evaluate the problem you're trying to solve before choosing the tool to fix it.
Nancy Lublin and Bob Filbin explore findings from crisis data.
Ben Lorica explores emerging security best practices for business intelligence, machine learning, and mobile computing products.
Natalie Evans Harris discusses the Community Principles on Ethical Data Practices (CPEDP), a code of ethics for data collection, sharing, and utilization.
Dinesh Nirmal explains how real-world machine learning reveals assumptions embedded in business processes that cause expensive misunderstandings.
Alex Smola shares lessons learned from AWS SageMaker, an integrated framework for handling all stages of analysis.
Watch highlights covering machine learning, business intelligence, data privacy, and more. From the Strata Data Conference in San Jose 2018.
Li Fan shows how Pinterest is using AI to predict what’s in an image, what a user wants, and what they’ll want next.
The O’Reilly Data Show Podcast: Ofer Ronen on the current state of chatbots.
A product manager's guide to employing data as a feature.
The O’Reilly Data Show Podcast: Danny Lange on how reinforcement learning can accelerate software development and how it can be democratized.
A comparison of the accuracy and performance of Spark-NLP vs. spaCy, and some use case recommendations.
A step-by-step guide to building and running a natural language processing pipeline.
A step-by-step guide to initialize the libraries, load the data, and train a tokenizer model using Spark-NLP and spaCy.
A look at the new streaming SQL engine for Apache Kafka.
Attend a day-long exploration of Jupyter's best practices and practical use cases in business and industry.
Ingest the data you need in an agile manner.
The O’Reilly Data Show Podcast: Leo Meyerovich on building large-scale, interactive applications that enable visual investigations.
Alysa Hutnik discusses the Fair Credit Reporting Act, the Equal Credit Opportunity Act, the Gramm-Leach Bliley Act, and the FTC’s focus on FinTech.
How companies such as athenahealth can transform legacy data into insights.
Gain agility by loading first and transforming later.
A glimpse into what lies ahead for response automation, model compliance, and repeatable experiments.
The media and ad tech sessions at the Strata Data Conference in San Jose will dig deep into how media businesses are changing.
The O’Reilly Data Show Podcast: Mark Hammond on applications of reinforcement learning to manufacturing and industrial automation.
Regardless of country or culture, any solid data science plan needs to address veracity, storage, analysis, and use.
By packaging and delivering actionable data in applications, product managers can help users achieve their goals.
The ability to appeal may be the most important part of a fair system, and it's one that isn't often discussed in data circles.
Designing application architectures for real-time decisions.
Facilitating data exchange across the enterprise.
The classic “write once, run everywhere” principle comes to life in streaming data.
Exploring many small regions of a graph with low latency using specialized graph and multi-model databases.
The O’Reilly Data Show Podcast: Fabian Yamaguchi on the potential of using large-scale analytics on graph representations of code.
It’s time to think about how the systems we build interact with our world and build systems that make our world a better place.
The sessions and training courses at Strata Data San Jose 2018 will focus on practical use cases of machine learning for data scientists, engineers, managers, and executives.
Top five characteristics to consider when deciding which library to use.
As the use of analytics proliferate, companies will need to be able to identify models that are breaking bad.
The O’Reilly Data Show Podcast: Kris Hammond on business applications of AI technologies and educating future AI specialists.
AI, blockchain, payment regionalization, and other fintech trends to watch.
How new developments in algorithms, machine learning, analytics, infrastructure, data ethics, and culture will shape data in 2018.
The O’Reilly Data Show Podcast: Tim Kraska on why ML will change how we build core algorithms and data structures.
Decoding simple regex features to match complex text patterns.
Every line of business must have access to the digital tools needed to innovate at the edge.
Tony Lee outlines the unique big data and AI challenges JD.com is tackling.
Pascale Fung explains how emotional interaction is being integrated into machines.
Ajey Gore looks at how the impossible can be made possible with technology and data insights.
Amr Awadallah explains the historic importance of the next wave in automation.
Watch highlights covering machine learning, smart cities, automation, and more. From Strata Data Conference in Singapore 2017.
Kira Radinsky describes a system that mines medical records and Wikipedia to reduce spurious correlations and provide guidance about drug repurposing.
Carme Artigas asks: Are innovations like autonomous vehicles and flying drones making our societies more intelligent?
The O’Reilly Data Show Podcast: Christine Hung on using data to drive digital transformation and recommenders that increase user engagement.
A look at the rise of the deep learning library PyTorch and simultaneous advancements in recommender systems.
Without the proper cataloging, curation, and security that self-service data platforms allow, companies are left vulnerable to cybersecurity threats and misinformation.