In this session, we'll review the differences between the most important Big Data file formats for Event Streaming, their pros and cons and how to choose the best fit for a specific use case. We'll also take a look to the proper architecture to provide greater control over data quality using Schema Management. Need to add a new column to a downstream database? You don't need an involved change process and at least 4 meetings to coordinate 15 teams. Join us to learn how it's possible to reduce operational complexity in the application development cycle.

However, despite the hype around data science, artificial intelligence, and machine learning, much of the work is still cloistered away on disjointed data science teams. Every company wants to use it, but few know how. In fact, a report coming out of MIT Sloan showed that while 85% believed AI would give them a competitive advantage, only 20% of the respondents were actually using it. Why is AI/ML so hard to implement? Much of the challenge comes down to the manual aspects of the machine learning analytic cycle – accessing data, preparing data, exploration and feature engineering, model validation and finally operationalization.

NASCAR will use the breadth and depth of AWS technologies to build cloud-based services and automate processes, including a new video series on NASCAR.com The video series will debut heading into the Monster Energy NASCAR Cup Series race at Michigan International Speedway, sharing the greatest historical moments in NASCAR racing with viewers. NASCAR is migrating its 18-petabyte video archive to AWS, and will leverage Amazon Rekognition--an AWS service that adds intelligent image and video analysis to applications--to automatically tag specific video frames with metadata, such as driver, car, race, lap, time, and sponsors so they can easily search those tags to surface the most iconic moments from past races. By using AWS's services, NASCAR expects to save thousands of hours of manual search time each year, and will be able to easily surface flashbacks like Dale Earnhardt Sr.'s 1987 "Pass in the Grass" or Denny Hamlin's 2016 Daytona 500 photo finish, and quickly deliver these to fans via video clips on NASCAR.com and social media channels. NASCAR will leverage AWS services to enhance its full range of media assets including websites, mobile applications, and social properties for its 80 million fans worldwide.

Google continues to add regional cloud partners as it seeks to differentiate its public cloud offerings while distributing its machine learning building blocks. Atos, the French big data platform and server vendor, announced a partnership with Google Cloud this week addressing secure hybrid cloud, data analytics and machine learning along with "digital workplace" initiatives. The partnership makes Google an Atos "preferred" cloud partner, the companies said Tuesday (April 24). Atos (EPA: ATO) said it would establish three machine learning and AI labs in France, U.K. and the U.S. that will use Google's training expertise to develop new machine learning models and applications. "Together, we will enable fast and smooth adoption of AI for enterprises," said Thierry Breton, chairman and CEO of Atos, Bezons, France.

Cloud machine learning platforms, sometimes referred to as machine learning as a service (MLaaS) solutions, can help make artificial intelligence (AI) affordable. But experts say enterprises and small businesses considering these services should also consider the potential challenges of these services before rushing in. Machine learning (ML), the branch of artificial intelligence concerned with creating computer systems that can learn without being explicitly programmed, is experiencing an undeniable boom. In its Technology, Media and Telecommunications Predictions, 2018, Deloitte Global wrote, "In 2018, large and medium-sized enterprises will intensify their use of machine learning. The number of implementations and pilot projects using the technology will double compared with 2017, and they will have doubled again by 2020."

The real value of BigQuery is not its speed. Because of BigQuery's scalability, you can isolate any workload on BigQuery from others. That means you can let non-engineers, such as sales, marketing, support and others, execute arbitrary quick-and-dirty SQL on BigQuery directly. Any employees in your enterprise can access its big data and quickly do data analytics without affecting performance to the production system. Now, imagine what would happen if you could use BigQuery for deep learning as well.

In a little less than 20 years, Google has evolved from a search engine experiment at Stanford University to a technology behemoth that's synonymous with the discovery of information. Many of Google's core services straddle the business and consumer worlds, and as such serve as the backdrop for many of the menial, digital tasks we execute on a daily basis. But despite its relative ubiquity, there are still pivotal areas in tech where Google faces fierce competition. Machine Learning, the concept of training large-scale AI networks to teach and improve themselves over time, is one such area. There's an arms race among public cloud providers to build the best Machine Learning capabilities for enterprises interested in creating their own intelligent applications.

Google today announced a new machine learning platform for developers at its NEXT Google Cloud Platform user conference in San Francisco. As Google chairman Eric Schmidt stressed during today's keynote, Google believes machine learning is "what's next." With this new platform, Google will make it easier for developers to use some of the machine learning smarts Google already uses to power features like Smart Reply in Inbox. The service is now available in limited preview. "Major Google applications use Cloud Machine Learning, including Photos (image search), the Google app (voice search), Translate and Inbox (Smart Reply)," the company says.

Earlier this year at GCP NEXT, we introduced new Cloud Machine Learning products with the intention to change the way businesses operate and create new customer experiences, while deepening the insights derived from data.Today, we want to share how Google aims to help more businesses benefit from the advancements in machine learning, while making it easier for them to use it. Google Cloud Machine Learning is now publicly available in beta and can empower all businesses to easily train quality machine learning models at a faster rate. With its powerful distributed training capability, you can train models on terabytes of data within hours, instead of waiting for days. Integrated with Google Cloud Platform (GCP), Cloud Machine Learning is a fully-managed service that can scale and creates a rich environment across TensorFlow and cloud computing tools such as Google Cloud Dataflow, BigQuery, Cloud Storage and Cloud Datalab. We're also introducing a new feature, HyperTune, that automatically improves predictive accuracy.

Google Cloud Machine Learning and TensorFlow are excellent tools for solving perception problems such as image classification. They work equally well on more traditional machine-learning problems such as click prediction for large-scale advertising and recommendation scenarios. To demonstrate this capability, in this post we'll train a model to predict display ad clicks on Criteo Labs clicks logs. These logs are over 1TB in size and contain feature values and click feedback from millions of display ads. We show how to train several models using different machine-learning techniques to predict the clickthrough rate.