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We've all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of "data-driven apps." Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by "data science." A data application acquires its value from the data itself, and creates more data as a result. It's not just an application with data; it's a data product. Data science enables the creation of data products.

Have we entered the age of NoOps infrastructures? Hardly. Old-style system administrators may be disappearing in the face of automation and cloud computing, but operations have become more significant than ever. As this O’Reilly Radar Report explains, we’re moving into a more complex arrangement known as "DevOps." Mike Loukides, O’Reilly’s VP of Content Strategy, provides an incisive look into this new world of operations, where IT specialists are becoming part of the development team. In an environment with thousands of servers, these specialists now write the code that maintains the infrastructure. Even applications that run in the cloud have to be resilient and fault tolerant, need to be monitored, and must adjust to huge swings in load. That was underscored by Amazon’s EBS outage last year. From the discussions at O’Reilly’s Velocity Conference, it’s evident that many operations specialists are quickly adapting to the DevOps reality. But as a whole, the industry has just scratched the surface. This report tells you why.

This report examines the important shifts in data products. Drawing from diverse examples, including iTunes, Google's self-driving car, and patient monitoring, author Mike Loukides explores the "disappearance" of data, the power of combining data, and the difference between discovery and recommendation. Looking ahead, the analysis finds the real changes in our lives will come from products and companies that reveal data results, not the data itself.

As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.

Introduction to system performance; Monitoring system activity; Managing the workload; Memory performance; Disk performance issues; Network performance; Terminal performance; Kernel configuration.

System Performance Tuning answers one of the most fundamental questions you can ask about your computer: How can I get it to do more work without buying more hardware? In the current economic downturn, performance tuning takes on a new importance. It allows system administrators to make the best use of existing systems and minimize the purchase of new equipment. Well-tuned systems save money and time that would otherwise be wasted dealing with slowdowns and errors. Performance tuning always involves compromises; unless system administrators know what the compromises are, they can't make intelligent decisions.Tuning is an essential skill for system administrators who face the problem of adapting the speed of a computer system to the speed requirements imposed by the real world. It requires a detailed understanding of the inner workings of the computer and its architecture. System Performance Tuning covers two distinct areas: performance tuning, or the art of increasing performance for a specific application, and capacity planning, or deciding what hardware best fulfills a given role. Underpinning both subjects is the science of computer architecture. This book focuses on the operating system, the underlying hardware, and their interactions. Topics covered include: Real and perceived performance problems, introducing capacity planning and performance monitoring (highlighting their strengths and weaknesses). An integrated description of all the major tools at a system administrator's disposal for tracking down system performance problems. Background on modern memory handling techniques, including the memory-caching filesystem implementations in Solaris and AIX. Updated sections on memory conservation and computing memory requirements. In depth discussion of disk interfaces, bandwidth capacity considerations, and RAID systems. Comprehensive discussion of NFS and greatly expanded discussion of networking. Workload management and code tuning. Special topics such as tuning Web servers for various types of content delivery and developments in cross-machine parallel computing For system administrators who want a hands-on introduction to system performance, this is the book to recommend.

The increasing push to develop products that integrate AI puts the intersection of AI and product management into sharp focus. AI brings many challenges to traditional product management, including nondeterministic outcomes and the potential for bias against particular groups. These problems aren't insurmountable, but they're real, and they cause many projects to fail before they're deployed. In this report, authors Justin Norman, Pete Skomoroch, and Mike Loukides present four in-depth essays to help business leaders, AI specialists, and data scientists examine what makes AI different. Once you're familiar with the issues, you'll be better prepared to anticipate and solve the problems you face as you develop an AI project and shepherd it into production. Originally published in O'Reilly Radar, each of these essays provides helpful supporting examples. Essays include: "What You Need to Know about Product Management for AI," by Pete Skomoroch and Mike Loukides "Practical Skills for the AI Product Manager," by Pete Skomoroch, Mike Loukides, and Justin Norman "Bringing an AI Product to Market," by Pete Skomoroch, Mike Loukides, and Justin Norman "AI Product Management after Deployment," Mike Loukides and Justin Norman.

In the past few years, we’ve seen many data products based on predictive modeling. These products range from weather forecasting to recommendation engines like Amazon's. Prediction technology can be interesting and mathematically elegant, but we need to take the next step: going from recommendations to products that can produce optimal strategies for meeting concrete business objectives. We already know how to build these products: they've been in use for the past decade or so, but they're not as common as they should be. This report shows how to take the next step: to go from simple predictions and recommendations to a new generation of data products with the potential to revolutionize entire industries.

There's no question: more people are using AI than ever before, and companies across industries are striving to get AI projects up and running. But how many have actually put revenue-bearing products into production? And how does your own organization measure up? In this new report, Mike Loukides, O'Reilly's vice president of content strategy, shares the results of a recent survey on how companies are adopting AI. You'll learn how (and where) AI use has grown over the past year, the significant barriers that remain, and the techniques and tools developers are--or should be--using for their applications.