To make great products: do machine learning like the great engineer you are, not like the great machine learning expert you aren’t.

Most of the problems you will face are, in fact, engineering problems. Even with all the resources of a great machine learning expert, most of the gains come from great features, not great machine learning algorithms. So, the basic approach is:

1. Make sure your pipeline is solid end to end.
2. Start with a reasonable objective.
3. Add common­-sense features in a simple way.
4. Make sure that your pipeline stays solid.

This approach will work well for a long period of time. Diverge from this approach only when there are no more simple tricks to get you any farther. Adding complexity slows future releases.

Once you’ve exhausted the simple tricks, cutting­-edge machine learning might indeed be in your future. See the section on Phase III machine learning projects.

This document is arranged as follows:

1. The first part should help you understand whether the time is right for building a machine learning system.
2. The second part is about deploying your first pipeline.
3. The third part is about launching and iterating while adding new features to your pipeline, how to evaluate models and training-serving skew.
4. The final part is about what to do when you reach a plateau.
5. Afterwards, there is a list of related work and an appendix with some background on the systems commonly used as examples in this document.

Success in the movie industry relies on a studio’s ability to attract moviegoers—but that’s sometimes easier said than done.

Moviegoers are a diverse group, with a wide variety of interests and preferences. Historically, movie studios have relied heavily on experience when deciding to invest in a particular script—but this can lead to huge risks, particularly when investing in new, original stories.

The iterative and complex process of matching stories and audiences is something that Julie Rieger, President, Chief Data Strategist and Head of Media, and Miguel Campo-Rembado, SVP of Data Science, together with their team of data scientists at 20th Century Fox, decided to clarify with data.

Together, Google Cloud and 20th Century Fox have built privacy-robust data partnerships to better understand moviegoers, and have developed in-house deep learning models that train on granular customer data and movie scripts to identify the basic patterns in audiences’ preferences for different types of films.

In 18 months, these models have become routine considerations for important business decisions, and provide one of their most objective, data-driven, and effective barometers to evaluate the tone of a movie, its affinity with core and stretch audiences, and its potential financial performance.

Find how machine learning helped achieve this (clue: it used movie trailers). Download the case study.

Setting out a plan for facility locations is a classic challenge for organizations of all shapes and sizes. This is the case for companies around the world—from retailers to governments and corner stores to department stores.

But, in India, it’s an especially hard problem to crack.

Healthy returns and high efficiency sit at the core of both public and private entities, so leaders are continuously reaching for the optimal point where projected demand meets ROI.

Traditionally, demographic statistics and survey-based reports have been computed by standard algorithms. Now, with Google’s “location-casting,” companies can use artificial intelligence and machine learning to sift through huge amounts of anonymized data and search queries to suggest a set of locations, that simultaneously takes into consideration factors like consumer demand, effective costs, profits, and future sites.

India’s Ministry of Civil Aviation was one of the first organizations to use location-casting to address the country’s complex network of airports. Its goal was to understand not just where passenger demand was the greatest, but also which combination of airport sites would meet that demand most efficiently and with the greatest ROI.

Find out the entire story. Download the case study now!

Breakthroughs in artificial intelligence (AI) have captured the imaginations of business and technical leaders alike. The AI techniques underlying these breakthroughs are finding diverse application across every industry. Early adopters are seeing results, particularly encouraging is that AI is starting to transform processes in established industries, from retail to financial services to manufacturing.

However, an organization’s effectiveness in applying these breakthroughs is anchored in the basics: A disciplined foundation in capturing, preparing and analyzing data.

Data scientists spend up to 80% of their time on the “data wrangling,” “data munging” and “data janitor” work required well before the predictive capabilities promised by AI can be realized.

Capturing, preparing and analyzing data creates the foundation for successful AI initiatives. To help business and IT leaders create this virtuous cycle, Google Cloud has prepared a CIO’s guide to data analytics and machine learning that outlines key enabling technologies at each step. Crucially, the guide illustrates how managed cloud services greatly simplify the journey—regardless of an organization’s maturity in handling big data.

This is important because, for many companies, the more fundamental levels of data management present a larger challenge than new capabilities like AI. “Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies,” noted Oliver Wyman consultants Nick Harrison and Deborah O’Neill in a recent Harvard Business Review article (aptly titled If Your Company Isn’t Good at Analytics, It’s Not Ready for AI). “Like it or not, you can’t afford to skip the basics.

Building on new research and Google Cloud’s own contributions to big data since the beginning, this guide walks readers through each step in the data management cycle, illustrating what’s possible alongside examples.

Specifically, the CIO’s guide to data analytics and machine learning is designed to help business and IT leaders address some of the essential questions companies face in modernizing data strategy:

• For my most important business processes, how can I capture raw data to ensure a proper foundation for future business questions? How can I do this cost-effectively?
• What about unstructured data outside of my operational/transactional databases: raw files, documents, images, system logs, chat and support transcripts, social media?
• How can I tap the same base of raw data I’ve collected to quickly get answers as new business questions arise?
• Rather than processing historical data in batch, what about processes where I need a real-time view of the business? How can I easily handle data streaming in real time?
• How can I unify the scattered silos of data across my organization to provide a current, end-to-end view? What about data stored off-premises in the multiple cloud and SaaS providers I work with?
• How can I disseminate this capability across my organization—especially to business users, not just developers and data scientists?

Because managed cloud services deal with an organization’s sensitive data, security is a top consideration at each step of the data management cycle. From data ingestion into the cloud, followed by storage, preparation and ongoing analysis as additional data flows in, techniques like data encryption and the ability to connect your network directly to the Google Cloud must reflect data security best practices that keep data assets safe as they yield insights.

Wherever your company is on its path to data maturity, Google Cloud is here to help. We welcome the opportunity to learn more about your challenges and how we can help you unlock the transformational potential of data.