People from across the data world came together in New York for the Strata Data Conference. Below you'll find links to highlights from the event. Rob Thomas and Tim O'Reilly discuss the hard work and mass experimentation that will lead to AI breakthroughs. Get a free trial today and find answers on the fly, or master something new and useful. Cassie Kozyrkov offers actionable advice for taking advantage of machine learning, navigating the AI era, and staying safe as you innovate.

Get expert knowledge on the tools and technologies you need to put your data strategies to work. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in London earlier this year. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies. This is a good time to assess enterprise activities, as there are many indications a number of companies are already beginning to use machine learning. For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production.

Neelesh Salian will host the session "How do you evolve your data infrastructure?" Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Neelesh Salian, software engineer at Stitch Fix, a company that combines machine learning and human expertise to personalize shopping. As companies integrate machine learning into their products and systems, there are important foundational technologies that come into play.

Francesca Lazzeri is a senior machine learning scientist at Microsoft on the cloud advocacy team and an expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, statistical modeling, time series econometrics and forecasting, and a range of industries--energy, oil and gas, retail, aerospace, healthcare, and professional services. Previously, she was a research fellow in business economics at Harvard Business School, where she performed statistical and econometric analysis within the technology and operations management unit. At Harvard, she worked on multiple patent, publication and social network data-driven projects to investigate and measure the impact of external knowledge networks on companies' competitiveness and innovation. Francesca periodically teaches applied analytics and machine learning classes at universities and research institutions around the world.

Optimization and ML are increasingly intersecting. Occasionally overlapping, sometimes complementary and most often best used in combination. Data Scientists should be interested in Operations Research, and Operations Researcher's are increasingly using Machine Learning. This presentation will provide a structure for understanding and applying these two techniques. It describes when Optimization techniques originating from operations research are the better solution and when it is beneficial to apply ML. More importantly, we describe how complex, high-value business problems can be solved with better by combining the techniques, rather than by using only one of them.

Two years ago when I reported from Strata NYC I shared how new data prep technologies were bringing data integration and data quality capabilities to business users. Instead of waiting for IT to ETL experimental data into the data warehouse or data lake, business users apply tools like Tableau Prep, Trifacta Wrangler, or Talend Data Preparation, to perform profiling, cleansing, and integrating data on their own. This trend continued at last week's Strata Data Conference as vendors are expanding their capabilities and services inby applying their technologies to business specific use cases. Informatica demoed me Informatica Data Catalog embedded with Claire, an "AI inside" that self classifies data and makes it easier for end users to find data sources and subject matter experts. The CEO of MemGraph demonstrated a real time graph database used to find anomalies and customer relationships.

Today marks the start of the fall Strata Data Conference in New York City, which has traditionally been the big data community's biggest show of the year. It's been a wild ride for the big data crowd in 2018, one that's brought its share of highs and lows. Now it's worth taking some time to consider where big data has come, and where it's possibly headed in the future. Here are five things to keep in mind as the Strata Data Conference kicks off. We've said this before, but it bears repeating: Hadoop is just one of many technologies angling for relevance in today's increasingly heterogeneous at-scale computing environment.

Recent advancements in computing technologies along with the increasing popularity of ecommerce platforms have radically amplified the risk of online fraud for financial services companies and their customers. Failing to properly recognize and prevent fraud results in billions of dollars of loss per year for the financial industry. This trend has urged companies to look into many popular artificial intelligence (AI) techniques, including deep learning for fraud detection. Deep learning can uncover patterns in tremendously large datasets and independently learn new concepts from raw data without extensive manual feature engineering. For this reason, deep learning has shown superior performance in domains such as object recognition and image classification.

Check out the machine learning sessions at the Strata Data Conference in London, May 21-24, 2018. The promises of AI are great, but taking the steps to build and implement AI within an organization is challenging. As companies learn to build intelligent products in real production environments, engineering teams face the complexity of the machine learning development process--from data sourcing and cleaning to feature engineering, modeling, training, deployment, and production infrastructure. Core to addressing these challenges is building an effective AI platform strategy--just as Facebook did with FBLearner Flow and Uber did with Michelangelo. Often, this task is easier said than done.

Check out the full lineup of training courses, tutorials, and sessions at the Strata Data Conference in London, May 21-24, 2018. Companies continue to use data to improve decision-making (business intelligence and analytics) and for automation (machine learning and AI). At the Strata Data Conference in London, we've assembled a program that introduces technologies and techniques, showcases use cases across many industries, and highlights the importance of ethics, privacy, and security. We are bringing back the Strata Business Summit, and this year, we have two days of executive briefings. Data Science and Machine Learning sessions will cover tools, techniques, and case studies.