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Seo Master present to you:
By Marc Cohen, Developer Relations

This holiday season, the Google Prediction API Team is bringing you four presents and, thanks to the joys of cloud computing, no reindeer are required for delivery. Here’s what you’ve already received:

• Faster on-ramp: We’ve made it easier to get started by enabling you to create an empty model (by sending a trainedmodels.insert request with no storageDataLocation specified) and add training data using the trainedmodels.update method. This change allows you to submit your model contents without needing to stage the data in Google Cloud Storage.
• Improved updates: The algorithms used to implement model updates (adding additional data to existing models) have been modified to work faster than ever.
• More classification algorithms: We’ve increased the number of classification algorithms used to build predictive models, resulting in across-the-board improvements in accuracy.
• Integration with Google Apps Script: Prediction services are now available as part of Google Apps Script, which means you can integrate prediction services with Google Docs, Google Maps, Gmail, and other great Google products.

All of the above enhancements are supported by the current Prediction API version 1.4 so you can enjoy these features using the existing client libraries.

Happy Holidays from the Google Prediction API Team. We’re looking forward to bringing you more exciting features in 2012!


Marc Cohen is a member of Google’s Developer Relations Team in Seattle. When not teaching Python programming and listening to indie rock music, he enjoys using the Google Prediction API to peer into the future.

Posted by Scott Knaster, Editor

2013, By: Seo Master

Seo Master present to you:
By Dan Holevoet, Developer Relations Team

A platform is more than the sum of its component parts. You can read about it or hear about it, but to really learn what makes up a platform you have to try it out for yourself, play with the parts, and discover what you can build.

With that in mind, we started a project called Au-to-do: a full sample application implementing a ticket tracker, built using Google APIs, that developers can download and dissect.


Au-to-do currently uses the following APIs and technologies:

• Google App Engine (with the Python runtime, Datastore API, and Task Queues API)
• Google Cloud Storage
• Google Prediction API
• Google Tasks API
• OAuth 2.0

Additional integrations with Google APIs are on their way. We are also planning a series of follow-up blog posts discussing each of the integrations in depth, with details on our design decisions and best practices you can use in your own projects.

By the way, if you’re wondering how to pronounce Au-to-do, you can say "auto-do" or "ought-to-do" — either is correct.

Ready to take a look at the code? Check out the getting started guide. Found a bug? Have a great idea for a feature or API integration? Let us know by filing a request.

Happy hacking!


Dan Holevoet joined the Google Developer Relations team in 2007. When not playing Starcraft, he works on Google Apps, with a focus on the Calendar and Contacts APIs. He's previously worked on iGoogle, OpenSocial, Gmail contextual gadgets, and the Google Apps Marketplace.

Posted by Scott Knaster, Editor

2013, By: Seo Master

Seo Master present to you:
By Zachary Goldberg, Product Manager

Since the general availability launch of the Prediction API this year at Google I/O, we have been working hard to give every developer access to machine learning in the cloud to build smarter apps. We’ve also been working on adding new features, accuracy improvements, and feedback capability to the API. Today we take another step by announcing Prediction v1.4. With the launch of this version, Prediction is graduating from Google Code Labs, reflecting Google’s commitment to the API’s development and stability. Version 1.4 also includes two new features:

• Data Anomaly Analysis
• One of the hardest parts of building an accurate predictive model is gathering and curating a high quality data set. With Prediction v1.4, we are providing a feature to help you identify problems with your data that we notice during the training process. This feedback makes it easier to build accurate predictive models with proper data.
• PMML Import
• PMML has become the de facto industry standard for transmitting predictive models and model data between systems. As of v1.4, the Google Prediction API can programmatically accept your PMML for data transformations and preprocessing.
• The PMML spec is vast and covers many, many features. You can find more details about the specific features that the Google Prediction API supports here.

We’re looking forward to seeing what you create with these new capabilities!

Feel free to find us and ask questions about these new features on our discussion group or submit feedback via our feedback form.


Zachary Goldberg is Product Manager for the Google Prediction API. He has a strange fascination with the Higgs Boson.

Posted by Scott Knaster, Editor

2013, By: Seo Master

Seo Master present to you: By Aleem Mawani, Co-Founder of Streak

Cross-posted with the Google App Engine Blog

This guest post was written by Aleem Mawani, Co-Founder of Streak, a startup alum of Y Combinator, a Silicon Valley incubator. Streak is a CRM tool built into Gmail. In this post, Aleem shares his experience building and scaling their product using Google Cloud Platform.

Everyone relies on email to get work done – yet most people use separate applications from their email to help them with various business processes. Streak fixes this problem by letting you do sales, hiring, fundraising, bug tracking, product development, deal flow, project management and almost any other business process right inside Gmail. In this post, I want to illustrate how we have used Google Cloud Platform to build Streak quickly, scalably and with the ability to deeply analyze our data.

• App Engine to serve our app.

• App Engine Datastore to persist user data.

• Memcache to make operations fast.

• BigQuery to analyze our logs and power dashboards.

• App Engine Search API to let users sift through their data.

• Prediction API to machine learn over user data.

• Google Translate API to translate our app to over 40 languages.

Our core learning is that you should use the best tool for the job. No one technology will be able to solve all your data storage and access needs. Instead, for each type of functionality, you should use a different service. In our case, we aggressively mirror our data in all the services mentioned above. For example, although the source of truth for our user data is in the App Engine Datastore, we mirror that data in the App Engine Search API so that we can provide full text search, Gmail style, to our users. We also mirror that same data in BigQuery so that we can power internal dashboards.

System Architecture

App Engine - We use App Engine for Java primarily to serve our application to the browser and mobile clients in addition to serving our API. App Engine is the source of truth for all our data, so we aggressively cache using Memcache. We also use Objectify to simplify access to the Datastore, which I highly recommend.

Google Cloud Storage - We mirror all of our Datastore data as well as all our log data in Cloud Storage, which acts as a conduit to other Google cloud services. It lets us archive the data as well as push it to BigQuery and the Prediction API.

BigQuery - Pushing the data into BigQuery allows us to run non-realtime queries that can help generate useful business metrics and slice user data to better understand how our product is getting used. Not only can we run complex queries over our Datastore data but also over all of our log data. This is incredibly powerful for analyzing the request patterns to App Engine. We can answer questions like:

• Which requests cost us the most money?
• What is the average response time for every URL on our site over the last 3 days?

BigQuery helps us monitor error rates in our application. We process all of our log data with debug statements, as well as something called an “error type” for any request that fails. If it’s a known error, we'll log something sensible, and we log the exception type if we haven’t seen it before. This is beneficial because we built a dashboard that queries BigQuery for the most recent errors in the last hour grouped by error type. Whenever we do a release, we can monitor error rates in the application really easily.

A Streak dashboard powered by BigQuery showing current usage statistics

In order to move the data into Cloud Storage from the Datastore and LogService, we developed an open source library called Mache. It’s a drop-in library that can be configured to automatically push data into BigQuery via Cloud Storage. The data can come from the Datastore or from LogService and is very configurable - feel free to contribute and give us feedback on it!

Google Cloud Platform also makes our application better for our users. We take advantage of the App Engine Search API and again mirror our data there. Users can then query their Streak data using the familiar Gmail full text search syntax, for example, “before:yesterday name:Foo”. Since we also push our data to the Prediction API, we can help users throughout our app by making smart suggestions. In Streak, we train models based on which emails users have categorized into different projects. Then, when users get a new email, we can suggest the most likely box that the email belongs to.

One issue that arises is how to keep all these mirrored data sets in sync. It works differently for each service based on the architecture of the service. Here’s a simple breakdown:



Having these technologies easily available to us has been a huge help for Streak. It makes our products better and helps us understand our users. Streak’s user base grew 30% every week for 4 consecutive months after launch, and we couldn’t have scaled this easily without Google Cloud Platform. To read more details on why Cloud Platform makes sense for our business, check out our case study and our post on the Google Enterprise blog.


Aleem Mawani is the co-founder of Streak.com, a CRM tool built into Gmail. Previously, Aleem worked on Google Drive and various ads products at Google. He has a degree from the University of Waterloo in Software engineering and an MBA from Harvard University.

Posted by Scott Knaster, Editor

2013, By: Seo Master