Rules of Machine Learning:
Best Practices for ML Engineering
Martin Zinkevich
This document is intended to help those with a basic knowledge of machine learning get thebenefit of best practices in machine learning from around Google. It presents a style for machinelearning, similar to the Google C++ Style Guide and other popular guides to practicalprogramming. If you have taken a class in machine learning, or built or worked on amachine­learned model, then you have the necessary background to read this document.
Terminology
Overview
Before Machine Learning
Rule #1: Don’t be afraid to launch a product without machine learning.
Rule #2: Make metrics design and implementation a priority.
Rule #3: Choose machine learning over a complex heuristic.
ML Phase I: Your First Pipeline
Rule #4: Keep the first model simple and get the infrastructure right.
Rule #5: Test the infrastructure independently from the machine learning.
Rule #6: Be careful about dropped data when copying pipelines.
Rule #7: Turn heuristics into features, or handle them externally.
Monitoring
Rule #8: Know the freshness requirements of your system.
Rule #9: Detect problems before exporting models.
Rule #10: Watch for silent failures.
Rule #11: Give feature sets owners and documentation.
Your First Objective
Rule #12: Don’t overthink which objective you choose to directly optimize.
Rule #13: Choose a simple, observable and attributable metric for your firstobjective.
Rule #14: Starting with an interpretable model makes debugging easier.
Rule #15: Separate Spam Filtering and Quality Ranking in a Policy Layer.
ML Phase II: Feature Engineering
Rule #16: Plan to launch and iterate.
Rule #17: Start with directly observed and reported features as opposed to learned
features.

Rule #18: Explore with features of content that generalize across contexts.
Rule #19: Use very specific features when you can.
Rule #20: Combine and modify existing features to create new features inhuman­understandable ways.
Rule #21: The number of feature weights you can learn in a linear model is roughly
proportional to the amount of data you have.
Rule #22: Clean up features you are no longer using.
Human Analysis of the System
Rule #23: You are not a typical end user.
Rule #24: Measure the delta between models.
Rule #25: When choosing models, utilitarian performance trumps predictive power.
Rule #26: Look for patterns in the measured errors, and create new features.
Rule #27: Try to quantify observed undesirable behavior.
Rule #28: Be aware that identical short­term behavior does not imply identicallong­term behavior.
Training­Serving Skew
Rule #29: The best way to make sure that you train like you serve is to save the setof features used at serving time, and then pipe those features to a log to use them attraining time.
Rule #30: Importance weight sampled data, don’t arbitrarily drop it!
Rule #31: Beware that if you join data from a table at training and serving time, thedata in the table may change.
Rule #32: Re­use code between your training pipeline and your serving pipeline
whenever possible.
Rule #33: If you produce a model based on the data until January 5th, test the modelon the data from January 6th and after.
Rule #34: In binary classification for filtering (such as spam detection or determining
interesting e­mails), make small short­term sacrifices in performance for very clean
data.
Rule #35: Beware of the inherent skew in ranking problems.
Rule #36: Avoid feedback loops with positional features.
Rule #37: Measure Training/Serving Skew.
ML Phase III: Slowed Growth, Optimization Refinement, and Complex Models
Rule #38: Don’t waste time on new features if unaligned objectives have become theissue.
Rule #39: Launch decisions will depend upon more than one metric.
Rule #40: Keep ensembles simple.
Rule #41: When performance plateaus, look for qualitatively new sources of
information to add rather than refining existing signals.
Rule #42: Don’t expect diversity, personalization, or relevance to be as correlatedwith popularity as you think they are.
Rule #43: Your friends tend to be the same across different products. Your interests
tend not to be.

Related WorkAcknowledgements
Appendix
YouTube Overview
Google Play Overview
Google Plus Overview

Terminology
The following terms will come up repeatedly in our discussion of effective machine learning:
Instance: The thing about which you want to make a prediction. For example, the instancemight be a web page that you want to classify as either "about cats" or "not about cats".
Label: An answer for a prediction task ­­ either the answer produced by a machine learning
system, or the right answer supplied in training data. For example, the label for a web pagemight be "about cats".
Feature: A property of an instance used in a prediction task. For example, a web page might
have a feature "contains the word 'cat'".
Feature Column1: A set of related features, such as the set of all possible countries in whichusers might live. An example may have one or more features present in a feature column. A
feature column is referred to as a “namespace” in the VW system (at Yahoo/Microsoft), or afield.
Example: An instance (with its features) and a label.
Model: A statistical representation of a prediction task. You train a model on examples then use
the model to make predictions.
Metric: A number that you care about. May or may not be directly optimized.
Objective: A metric that your algorithm is trying to optimize.
Pipeline: The infrastructure surrounding a machine learning algorithm. Includes gathering thedata from the front end, putting it into training data files, training one or more models, andexporting the models to production.

Overview
To make great products:
do machine learning like the great engineer you are, not like the great machine learning
expert you aren’t.
1

Google­specific terminology.

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, notgreat 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 common­sense features in a simple way
4. make sure that your pipeline stays solid.
This approach will make lots of money and/or make lots of people happy for a long period oftime. 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, cutting­edge machine learning might indeed be in yourfuture. See the section on Phase III machine learning projects.
This document is arranged in four parts:
1. The first part should help you understand whether the time is right for building a machinelearning 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 training­serving 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 thesystems commonly used as examples in this document.

Before Machine Learning
Rule #1: Don’t be afraid to launch a product without machine learning.
Machine learning is cool, but it requires data. Theoretically, you can take data from a different
problem and then tweak the model for a new product, but this will likely underperform basic
heuristics. If you think that machine learning will give you a 100% boost, then a heuristic will get
you 50% of the way there.
For instance, if you are ranking apps in an app marketplace, you could use the install rate or
number of installs. If you are detecting spam, filter out publishers that have sent spam before.
Don’t be afraid to use human editing either. If you need to rank contacts, rank the most recently
used highest (or even rank alphabetically). If machine learning is not absolutely required for yourproduct, don't use it until you have data.

Rule #2: First, design and implement metrics.
Before formalizing what your machine learning system will do, track as much as possible in yourcurrent system. Do this for the following reasons:
1. It is easier to gain permission from the system’s users earlier on.
2. If you think that something might be a concern in the future, it is better to get historical
data now.
3. If you design your system with metric instrumentation in mind, things will go better foryou in the future. Specifically, you don’t want to find yourself grepping for strings in logs
to instrument your metrics!
4. You will notice what things change and what stays the same. For instance, suppose youwant to directly optimize one­day active users. However, during your early manipulationsof the system, you may notice that dramatic alterations of the user experience don’t
noticeably change this metric.
Google Plus team measures expands per read, reshares per read, plus­ones per read,comments/read, comments per user, reshares per user, etc. which they use in computing thegoodness of a post at serving time. Also, note that an experiment framework, where you
can group users into buckets and aggregate statistics by experiment, is important. See
Rule #12.
By being more liberal about gathering metrics, you can gain a broader picture of your system.
Notice a problem? Add a metric to track it! Excited about some quantitative change on the last
release? Add a metric to track it!
Rule #3: Choose machine learning over a complex heuristic.
A simple heuristic can get your product out the door. A complex heuristic is unmaintainable.
Once you have data and a basic idea of what you are trying to accomplish, move on to machinelearning. As in most software engineering tasks, you will want to be constantly updating yourapproach, whether it is a heuristic or a machine­learned model, and you will find that themachine­learned model is easier to update and maintain (see Rule #16).

ML Phase I: Your First Pipeline
Focus on your system infrastructure for your first pipeline. While it is fun to think about all theimaginative machine learning you are going to do, it will be hard to figure out what is happening
if you don’t first trust your pipeline.
Rule #4: Keep the first model simple and get the infrastructure right.
The first model provides the biggest boost to your product, so it doesn't need to be fancy. But
you will run into many more infrastructure issues than you expect. Before anyone can use yourfancy new machine learning system, you have to determine:1. How to get examples to your learning algorithm.
2. A first cut as to what “good” and “bad” mean to your system.
3. How to integrate your model into your application. You can either apply the model live, orpre­compute the model on examples offline and store the results in a table. For example,
you might want to pre­classify web pages and store the results in a table, but you mightwant to classify chat messages live.
Choosing simple features makes it easier to ensure that:
1. The features reach your learning algorithm correctly.
2. The model learns reasonable weights.
3. The features reach your model in the server correctly.
Once you have a system that does these three things reliably, you have done most of the work.
Your simple model provides you with baseline metrics and a baseline behavior that you can use
to test more complex models. Some teams aim for a “neutral” first launch: a first launch that
explicitly de­prioritizes machine learning gains, to avoid getting distracted.
Rule #5: Test the infrastructure independently from the machine learning.
Make sure that the infrastructure is testable, and that the learning parts of the system areencapsulated so that you can test everything around it. Specifically:
1. Test getting data into the algorithm. Check that feature columns that should be populated
are populated. Where privacy permits, manually inspect the input to your training
algorithm. If possible, check statistics in your pipeline in comparison to elsewhere, suchas RASTA.
2. Test getting models out of the training algorithm. Make sure that the model in yourtraining environment gives the same score as the model in your serving environment(see Rule #37).
Machine learning has an element of unpredictability, so make sure that you have tests for thecode for creating examples in training and serving, and that you can load and use a fixed model
during serving. Also, it is important to understand your data: see Practical Advice for Analysis of
Large, Complex Data Sets.
Rule #6: Be careful about dropped data when copying pipelines.
Often we create a pipeline by copying an existing pipeline (i.e. cargo cult programming), and theold pipeline drops data that we need for the new pipeline. For example, the pipeline for Google
Plus What’s Hot drops older posts (because it is trying to rank fresh posts). This pipeline wascopied to use for Google Plus Stream, where older posts are still meaningful, but the pipeline
was still dropping old posts. Another common pattern is to only log data that was seen by theuser. Thus, this data is useless if we want to model why a particular post was not seen by theuser, because all the negative examples have been dropped. A similar issue occurred in Play.
While working on Play Apps Home, a new pipeline was created that also contained examples
from two other landing pages (Play Games Home and Play Home Home) without any feature to
disambiguate where each example came from.

Rule #7: Turn heuristics into features, or handle them externally.
Usually the problems that machine learning is trying to solve are not completely new. There isan existing system for ranking, or classifying, or whatever problem you are trying to solve. Thismeans that there are a bunch of rules and heuristics. These same heuristics can give you alift when tweaked with machine learning. Your heuristics should be mined for whatever
information they have, for two reasons. First, the transition to a machine learned system will besmoother. Second, usually those rules contain a lot of the intuition about the system you don’t
want to throw away. There are four ways you can use an existing heuristic:
1. Preprocess using the heuristic. If the feature is incredibly awesome, then this is an
option. For example, if, in a spam filter, the sender has already been blacklisted, don’t try
to relearn what “blacklisted” means. Block the message. This approach makes the mostsense in binary classification tasks.
2. Create a feature. Directly creating a feature from the heuristic is great. For example, if
you use a heuristic to compute a relevance score for a query result, you can include thescore as the value of a feature. Later on you may want to use machine learningtechniques to massage the value (for example, converting the value into one of a finite
set of discrete values, or combining it with other features) but start by using the raw
value produced by the heuristic.
3. Mine the raw inputs of the heuristic. If there is a heuristic for apps that combines thenumber of installs, the number of characters in the text, and the day of the week, thenconsider pulling these pieces apart, and feeding these inputs into the learning
separately. Some techniques that apply to ensembles apply here (see Rule #40).
4. Modify the label. This is an option when you feel that the heuristic captures information
not currently contained in the label. For example, if you are trying to maximize thenumber of downloads, but you also want quality content, then maybe the solution is tomultiply the label by the average number of stars the app received. There is a lot of
space here for leeway. See the section on “Your First Objective”.
Do be mindful of the added complexity when using heuristics in an ML system. Using oldheuristics in your new machine learning algorithm can help to create a smooth transition, but
think about whether there is a simpler way to accomplish the same effect.

Monitoring
In general, practice good alerting hygiene, such as making alerts actionable and having adashboard page.
Rule #8: Know the freshness requirements of your system.
How much does performance degrade if you have a model that is a day old? A week old? Aquarter old? This information can help you to understand the priorities of your monitoring. If you
lose 10% of your revenue if the model is not updated for a day, it makes sense to have anengineer watching it continuously. Most ad serving systems have new advertisements to handle

every day, and must update daily. For instance, if the ML model for Google Play Search is not
updated, it can have an impact on revenue in under a month. Some models for What’s Hot in
Google Plus have no post identifier in their model so they can export these models infrequently.
Other models that have post identifiers are updated much more frequently. Also notice that
freshness can change over time, especially when feature columns are added or removed fromyour model.
Rule #9: Detect problems before exporting models.
Many machine learning systems have a stage where you export the model to serving. If there is
an issue with an exported model, it is a user­facing issue. If there is an issue before, then it is a
training issue, and users will not notice.
Do sanity checks right before you export the model. Specifically, make sure that the model’s
performance is reasonable on held out data. Or, if you have lingering concerns with the data,
don’t export a model. Many teams continuously deploying models check the area under the
ROC curve (or AUC) before exporting. Issues about models that haven’t been exported
require an e­mail alert, but issues on a user­facing model may require a page. So better towait and be sure before impacting users.
Rule #10: Watch for silent failures.
This is a problem that occurs more for machine learning systems than for other kinds ofsystems. Suppose that a particular table that is being joined is no longer being updated. The
machine learning system will adjust, and behavior will continue to be reasonably good, decaying
gradually. Sometimes tables are found that were months out of date, and a simple refresh
improved performance more than any other launch that quarter! For example, the coverage of afeature may change due to implementation changes: for example a feature column could be
populated in 90% of the examples, and suddenly drop to 60% of the examples. Play once had atable that was stale for 6 months, and refreshing the table alone gave a boost of 2% in install
rate. If you track statistics of the data, as well as manually inspect the data on occassion, you
can reduce these kinds of failures.
Rule #11: Give feature column owners and documentation.
If the system is large, and there are many feature columns, know who created or is maintaining
each feature column. If you find that the person who understands a feature column is leaving,
make sure that someone has the information. Although many feature columns have descriptive
names, it's good to have a more detailed description of what the feature is, where it came from,and how it is expected to help.

Your First Objective
You have many metrics, or measurements about the system that you care about, but yourmachine learning algorithm will often require a single objective, a number that your algorithm

is “trying” to optimize. I distinguish here between objectives and metrics: a metric is any
number that your system reports, which may or may not be important. See also Rule #2.
Rule #12: Don’t overthink which objective you choose to directly optimize.
You want to make money, make your users happy, and make the world a better place. There aretons of metrics that you care about, and you should measure them all (see Rule #2). However,
early in the machine learning process, you will notice them all going up, even those that you do
not directly optimize. For instance, suppose you care about number of clicks, time spent on thesite, and daily active users. If you optimize for number of clicks, you are likely to see the timespent increase.
So, keep it simple and don’t think too hard about balancing different metrics when you can stilleasily increase all the metrics. Don’t take this rule too far though: do not confuse your objectivewith the ultimate health of the system (see Rule #39). And, if you find yourself increasing thedirectly optimized metric, but deciding not to launch, some objective revision may berequired.
Rule #13: Choose a simple, observable and attributable metric for your first objective.
Often you don't know what the true objective is. You think you do but then you as you stare at
the data and side­by­side analysis of your old system and new ML system, you realize you want
to tweak it. Further, different team members often can't agree on the true objective. The ML
objective should be something that is easy to measure and is a proxy for the “true”
objective2. So train on the simple ML objective, and consider having a "policy layer" on top thatallows you to add additional logic (hopefully very simple logic) to do the final ranking.
The easiest thing to model is a user behavior that is directly observed and attributable to an
action of the system:
1. Was this ranked link clicked?
2. Was this ranked object downloaded?
3. Was this ranked object forwarded/replied to/e­mailed?
4. Was this ranked object rated?
5. Was this shown object marked as spam/pornography/offensive?
Avoid modeling indirect effects at first:
1. Did the user visit the next day?
2. How long did the user visit the site?
3. What were the daily active users?
Indirect effects make great metrics, and can be used during A/B testing and during launchdecisions.
Finally, don’t try to get the machine learning to figure out:
1. Is the user happy using the product?
2. Is the user satisfied with the experience?
3. Is the product improving the user’s overall well­being?
2

There is often no “true” objective. See Rule #39.

4. How will this affect the company’s overall health?
These are all important, but also incredibly hard. Instead, use proxies: if the user is happy, they
will stay on the site longer. If the user is satisfied, they will visit again tomorrow. Insofar aswell­being and company health is concerned, human judgement is required to connect any
machine learned objective to the nature of the product you are selling and your business plan,so we don’t end up here.
Rule #14: Starting with an interpretable model makes debugging easier.
Linear regression, logistic regression, and Poisson regression are directly motivated by aprobabilistic model. Each prediction is interpretable as a probability or an expected value. This
makes them easier to debug than models that use objectives (zero­one loss, various hinge
losses, et cetera) that try to directly optimize classification accuracy or ranking performance. For
example, if probabilities in training deviate from probabilities predicted in side­by­sides or byinspecting the production system, this deviation could reveal a problem.
For example, in linear, logistic, or Poisson regression, there are subsets of the data where theaverage predicted expectation equals the average label (1­moment calibrated, or justcalibrated)3. If you have a feature which is either 1 or 0 for each example, then the set ofexamples where that feature is 1 is calibrated. Also, if you have a feature that is 1 for every
example, then the set of all examples is calibrated.
With simple models, it is easier to deal with feedback loops (see Rule #36).
Often, we use these probabilistic predictions to make a decision: e.g. rank posts in decreasing
expected value (i.e. probability of click/download/etc.). However, remember when it comestime to choose which model to use, the decision matters more than the likelihood of thedata given the model (see Rule #27).
Rule #15: Separate Spam Filtering and Quality Ranking in a Policy Layer.
Quality ranking is a fine art, but spam filtering is a war. The signals that you use to determine
high quality posts will become obvious to those who use your system, and they will tweak theirposts to have these properties. Thus, your quality ranking should focus on ranking content thatis posted in good faith. You should not discount the quality ranking learner for ranking spamhighly. Similarly, “racy” content should be handled separately from Quality Ranking.
Spam filtering is a different story. You have to expect that the features that you need to generate
will be constantly changing. Often, there will be obvious rules that you put into the system (if apost has more than three spam votes, don’t retrieve it, et cetera). Any learned model will have to
be updated daily, if not faster. The reputation of the creator of the content will play a great role.
At some level, the output of these two systems will have to be integrated. Keep in mind, filtering
spam in search results should probably be more aggressive than filtering spam in email
3This is true assuming that you have no regularization and that your algorithm has converged. It isapproximately true in general.

messages. Also, it is a standard practice to remove spam from the training data for the quality
classifier.

ML Phase II: Feature Engineering
In the first phase of the lifecycle of a machine learning system, the important issue is to get thetraining data into the learning system, get any metrics of interest instrumented, and create aserving infrastructure. After you have a working end to end system with unit and systemtests instrumented, Phase II begins.
In the second phase, there is a lot of low­hanging fruit. There are a variety of obvious featuresthat could be pulled into the system. Thus, the second phase of machine learning involves
pulling in as many features as possible and combining them in intuitive ways. During this phase,
all of the metrics should still be rising. There will be lots of launches, and it is a great time to pullin lots of engineers that can join up all the data that you need to create a truly awesome learning
system.
Rule #16: Plan to launch and iterate.
Don’t expect that the model you are working on now will be the last one that you will launch, oreven that you will ever stop launching models. Thus consider whether the complexity you areadding with this launch will slow down future launches. Many teams have launched a model per
quarter or more for years. There are three basic reasons to launch new models:
1. you are coming up with new features,
2. you are tuning regularization and combining old features in new ways, and/or
3. you are tuning the objective.
Regardless, giving a model a bit of love can be good: looking over the data feeding into theexample can help find new signals as well as old, broken ones. So, as you build your model,
think about how easy it is to add or remove or recombine features. Think about how easy it is tocreate a fresh copy of the pipeline and verify its correctness. Think about whether it is possible
to have two or three copies running in parallel. Finally, don’t worry about whether feature 16 of
35 makes it into this version of the pipeline. You’ll get it next quarter.
Rule #17: Start with directly observed and reported features as opposed to learned
features.
This might be a controversial point, but it avoids a lot of pitfalls. First of all, let’s describe what alearned feature is. A learned feature is a feature generated either by an external system (suchas an unsupervised clustering system) or by the learner itself (e.g. via a factored model or deep

learning). Both of these can be useful, but they can have a lot of issues, so they should not be in
the first model.
If you use an external system to create a feature, remember that the system has its own
objective. The external system's objective may be only weakly correlated with your current
objective. If you grab a snapshot of the external system, then it can become out of date. If you
update the features from the external system, then the meanings may change. If you use an
external system to provide a feature, be aware that they require a great deal of care.
The primary issue with factored models and deep models is that they are non­convex. Thus,
there is no guarantee that an optimal solution can be approximated or found, and the local
minima found on each iteration can be different. This variation makes it hard to judge whether
the impact of a change to your system is meaningful or random. By creating a model without
deep features, you can get an excellent baseline performance. After this baseline is achieved,
you can try more esoteric approaches.
Rule #18: Explore with features of content that generalize across contexts.
Often a machine learning system is a small part of a much bigger picture. For example, if you
imagine a post that might be used in What’s Hot, many people will plus­one, re­share, orcomment on a post before it is ever shown in What’s Hot. If you provide those statistics to thelearner, it can promote new posts that it has no data for in the context it is optimizing. YouTube
Watch Next could use number of watches, or co­watches (counts of how many times one video
was watched after another was watched) from YouTube search. You can also use explicit user
ratings. Finally, if you have a user action that you are using as a label, seeing that action on the
document in a different context can be a great feature. All of these features allow you to bringnew content into the context. Note that this is not about personalization: figure out if someone
likes the content in this context first, then figure out who likes it more or less.
Rule #19: Use very specific features when you can.
With tons of data, it is simpler to learn millions of simple features than a few complex features.
Identifiers of documents being retrieved and canonicalized queries do not provide muchgeneralization, but align your ranking with your labels on head queries.. Thus, don’t be afraid ofgroups of features where each feature applies to a very small fraction of your data, but overall
coverage is above 90%. You can use regularization to eliminate the features that apply to too
few examples.
Rule #20: Combine and modify existing features to create new features inhuman­understandable ways.
There are a variety of ways to combine and modify features. Machine learning systems such as
TensorFlow allow you to pre­process your data through transformations. The two most standard
approaches are “discretizations” and “crosses” .

Discretization consists of taking a continuous feature and creating many discrete features fromit. Consider a continuous feature such as age. You can create a feature which is 1 when age isless than 18, another feature which is 1 when age is between 18 and 35, et cetera. Don’t
overthink the boundaries of these histograms: basic quantiles will give you most of the impact.
Crosses combine two or more feature columns. A feature column, in TensorFlow's terminology,
is a set of homogenous features, (e.g. {male, female}, {US, Canada, Mexico}, et cetera). A crossis a new feature column with features in, for example, {male, f emale} {U S, C anada, M exico} .
This new feature column will contain the feature (male, Canada). If you are using TensorFlow
and you tell TensorFlow to create this cross for you, this (male, Canada) feature will be present
in examples representing male Canadians. Note that it takes massive amounts of data to learn
models with crosses of three, four, or more base feature columns.

×

Crosses that produce very large feature columns may overfit. For instance, imagine that you aredoing some sort of search, and you have a feature column with words in the query, and you
have a feature column with words in the document. You can combine these with a cross, but
you will end up with a lot of features (see Rule #21). When working with text there are twoalternatives. The most draconian is a dot product. A dot product in its simplest form simply
counts the number of common words between the query and the document. This feature canthen be discretized. Another approach is an intersection: thus, we will have a feature which ispresent if and only if the word “pony” is in the document and the query, and another feature
which is present if and only if the word “the” is in the document and the query.
Rule #21: The number of feature weights you can learn in a linear model is roughly
proportional to the amount of data you have.
There are fascinating statistical learning theory results concerning the appropriate level ofcomplexity for a model, but this rule is basically all you need to know. I have had conversations
in which people were doubtful that anything can be learned from one thousand examples, orthat you would ever need more than 1 million examples, because they get stuck in a certain
method of learning. The key is to scale your learning to the size of your data:
1. If you are working on a search ranking system, and there are millions of different words
in the documents and the query and you have 1000 labeled examples, then you should
use a dot product between document and query features, TF­IDF, and a half­dozen
other highly human­engineered features. 1000 examples, a dozen features.
2. If you have a million examples, then intersect the document and query feature columns,
using regularization and possibly feature selection. This will give you millions of features,
but with regularization you will have fewer. Ten million examples, maybe a hundred
thousand features.
3. If you have billions or hundreds of billions of examples, you can cross the featurecolumns with document and query tokens, using feature selection and regularization.
You will have a billion examples, and 10 million features.
Statistical learning theory rarely gives tight bounds, but gives great guidance for a starting point.
In the end, use Rule #28 to decide what features to use.

Rule #22: Clean up features you are no longer using.
Unused features create technical debt. If you find that you are not using a feature, and thatcombining it with other features is not working, then drop it out of your infrastructure. You wantto keep your infrastructure clean so that the most promising features can be tried as fast as
possible. If necessary, someone can always add back your feature.

Keep coverage in mind when considering what features to add or keep. How many examples
are covered by the feature? For example, if you have some personalization features, but only
8% of your users have any personalization features, it is not going to be very effective.
At the same time, some features may punch above their weight. For example, if you have afeature which covers only 1% of the data, but 90% of the examples that have the feature arepositive, then it will be a great feature to add.

Human Analysis of the System
Before going on to the third phase of machine learning, it is important to focus on something thatis not taught in any machine learning class: how to look at an existing model, and improve it.
This is more of an art than a science, and yet there are several anti­patterns that it helps to
avoid.
Rule #23: You are not a typical end user.
This is perhaps the easiest way for a team to get bogged down. While there are a lot of benefits
to fishfooding (using a prototype within your team) and dogfooding (using a prototype withinyour company), employees should look at whether the performance is correct. While a change
which is obviously bad should not be used, anything that looks reasonably near production
should be tested further, either by paying laypeople to answer questions on a crowdsourcing
platform, or through a live experiment on real users.
There are two reasons for this. The first is that you are too close to the code. You may belooking for a particular aspect of the posts, or you are simply too emotionally involved (e.g.
confirmation bias). The second is that your time is too valuable. Consider the cost of 9
engineers sitting in a one hour meeting, and think of how many contracted human labels thatbuys on a crowdsourcing platform.
If you really want to have user feedback, use user experience methodologies. Create user
personas (one description is in Bill Buxton’s Designing User Experiences) early in a process anddo usability testing (one description is in Steve Krug’s Don’t Make Me Think) later. User
personas involve creating a hypothetical user. For instance, if your team is all male, it might help
to design a 35­year old female user persona (complete with user features), and look at theresults it generates rather than 10 results for 25­40 year old males. Bringing in actual people towatch their reaction to your site (locally or remotely) in usability testing can also get you a fresh
perspective.
Rule #24: Measure the delta between models.
One of the easiest, and sometimes most useful measurements you can make before any users
have looked at your new model is to calculate just how different the new results are fromproduction. For instance, if you have a ranking problem, run both models on a sample of queries
through the entire system, and look at the size of the symmetric difference of the results(weighted by ranking position). If the difference is very small, then you can tell without running
an experiment that there will be little change. If the difference is very large, then you want tomake sure that the change is good. Looking over queries where the symmetric difference is highcan help you to understand qualitatively what the change was like. Make sure, however, that thesystem is stable. Make sure that a model when compared with itself has a low (ideally zero)
symmetric difference.
Rule #25: When choosing models, utilitarian performance trumps predictive power.
Your model may try to predict click­through­rate. However, in the end, the key question is what
you do with that prediction. If you are using it to rank documents, then the quality of the final
ranking matters more than the prediction itself. If you predict the probability that a document isspam and then have a cutoff on what is blocked, then the precision of what is allowed throughmatters more. Most of the time, these two things should be in agreement: when they do notagree, it will likely be on a small gain. Thus, if there is some change that improves log loss but
degrades the performance of the system, look for another feature. When this starts happening
more often, it is time to revisit the objective of your model.
Rule #26: Look for patterns in the measured errors, and create new features.
Suppose that you see a training example that the model got “wrong”. In a classification task, this
could be a false positive or a false negative. In a ranking task, it could be a pair where a positive
was ranked lower than a negative. The most important point is that this is an example that themachine learning system knows it got wrong and would like to fix if given the opportunity. If you
give the model a feature that allows it to fix the error, the model will try to use it.
On the other hand, if you try to create a feature based upon examples the system doesn’t see
as mistakes, the feature will be ignored. For instance, suppose that in Play Apps Search,someone searches for “free games”. Suppose one of the top results is a less relevant gag app.
So you create a feature for “gag apps”. However, if you are maximizing number of installs, andpeople install a gag app when they search for free games, the “gag apps” feature won’t have theeffect you want.
Once you have examples that the model got wrong, look for trends that are outside your current
feature set. For instance, if the system seems to be demoting longer posts, then add post
length. Don’t be too specific about the features you add. If you are going to add post length,don’t try to guess what long means, just add a dozen features and the let model figure out what
to do with them (see Rule #21). That is the easiest way to get what you want.
Rule #27: Try to quantify observed undesirable behavior.
Some members of your team will start to be frustrated with properties of the system they don’t
like which aren’t captured by the existing loss function. At this point, they should do whatever ittakes to turn their gripes into solid numbers. For example, if they think that too many “gag apps”
are being shown in Play Search, they could have human raters identify gag apps. (You canfeasibly use human­labelled data in this case because a relatively small fraction of the queries
account for a large fraction of the traffic.) If your issues are measurable, then you can start using
them as features, objectives, or metrics. The general rule is “measure first, optimize second”.
Rule #28: Be aware that identical short­term behavior does not imply identical long­term
behavior.
Imagine that you have a new system that looks at every doc_id and exact_query, and thencalculates the probability of click for every doc for every query. You find that its behavior isnearly identical to your current system in both side by sides and A/B testing, so given its
simplicity, you launch it. However, you notice that no new apps are being shown. Why? Well,since your system only shows a doc based on its own history with that query, there is no way tolearn that a new doc should be shown.
The only way to understand how such a system would work long­term is to have it train only on
data acquired when the model was live. This is very difficult.

Training­Serving Skew
Training­serving skew is a difference between performance during training and performance
during serving. This skew can be caused by:● a discrepancy between how you handle data in the training and serving pipelines, or● a change in the data between when you train and when you serve, or● a feedback loop between your model and your algorithm.
We have observed production machine learning systems at Google with training­serving skew
that negatively impacts performance. The best solution is to explicitly monitor it so that systemand data changes don’t introduce skew unnoticed.
Rule #29: The best way to make sure that you train like you serve is to save the set offeatures used at serving time, and then pipe those features to a log to use them attraining time.
Even if you can’t do this for every example, do it for a small fraction, such that you can verify theconsistency between serving and training (see Rule #37). Teams that have made thismeasurement at Google were sometimes surprised by the results. YouTube home page

switched to logging features at serving time with significant quality improvements and areduction in code complexity, and many teams are switching their infrastructure as we speak.
Rule #30: Importance weight sampled data, don’t arbitrarily drop it!
When you have too much data, there is a temptation to take files 1­12, and ignore files 13­99.
This is a mistake: dropping data in training has caused issues in the past for several teams (seeRule #6). Although data that was never shown to the user can be dropped, importanceweighting is best for the rest. Importance weighting means that if you decide that you are goingto sample example X with a 30% probability, then give it a weight of 10/3. With importanceweighting, all of the calibration properties discussed in Rule #14 still hold.
Rule #31: Beware that if you join data from a table at training and serving time, the data inthe table may change.
Say you join doc ids with a table containing features for those docs (such as number ofcomments or clicks). Between training and serving time, features in the table may be changed.
Your model's prediction for the same document may then differ between training and serving.
The easiest way to avoid this sort of problem is to log features at serving time (see Rule #32). If
the table is changing only slowly, you can also snapshot the table hourly or daily to getreasonably close data. Note that this still doesn’t completely resolve the issue.
Rule #32: Re­use code between your training pipeline and your serving pipeline
whenever possible.
Batch processing is different than online processing. In online processing, you must handle
each request as it arrives (e.g. you must do a separate lookup for each query), whereas in batchprocessing, you can combine tasks (e.g. making a join). At serving time, you are doing online
processing, whereas training is a batch processing task. However, there are some things thatyou can do to re­use code. For example, you can create an object that is particular to yoursystem where the result of any queries or joins can be stored in a very human readable way,and errors can be tested easily. Then, once you have gathered all the information, duringserving or training, you run a common method to bridge between the human­readable object
that is specific to your system, and whatever format the machine learning system expects. Thiseliminates a source of training­serving skew. As a corollary, try not to use two different
programming languages between training and serving ­ that decision will make it nearly
impossible for you to share code.
Rule #33: If you produce a model based on the data until January 5th, test the model on
the data from January 6th and after.
In general, measure performance of a model on the data gathered after the data you trained themodel on, as this better reflects what your system will do in production. If you produce a model
based on the data until January 5th, test the model on the data from January 6th. You will
expect that the performance will not be as good on the new data, but it shouldn’t be radicallyworse. Since there might be daily effects, you might not predict the average click rate orconversion rate, but the area under the curve, which represents the likelihood of giving thepositive example a score higher than a negative example, should be reasonably close.
Rule #34: In binary classification for filtering (such as spam detection or determining
interesting e­mails), make small short­term sacrifices in performance for very clean data.
In a filtering task, examples which are marked as negative are not shown to the user. Suppose
you have a filter that blocks 75% of the negative examples at serving. You might be tempted to
draw additional training data from the instances shown to users. For example, if a user marks anemail as spam that your filter let through, you might want to learn from that.
But this approach introduces sampling bias. You can gather cleaner data if instead duringserving you label 1% of all traffic as “held out”, and send all held out examples to the user. Now
your filter is blocking at least 74% of the negative examples. These held out examples canbecome your training data.
Note that if your filter is blocking 95% of the negative examples or more, this becomes lessviable. Even so, if you wish to measure serving performance, you can make an even tiniersample (say 0.1% or 0.001%). Ten thousand examples is enough to estimate performance quite
accurately.
Rule #35: Beware of the inherent skew in ranking problems.
When you switch your ranking algorithm radically enough that different results show up, youhave effectively changed the data that your algorithm is going to see in the future. This kind of
skew will show up, and you should design your model around it. There are multiple different
approaches. These approaches are all ways to favor data that your model has already seen.
1. Have higher regularization on features that cover more queries as opposed to thosefeatures that are on for only one query. This way, the model will favor features that are
specific to one or a few queries over features that generalize to all queries. Thisapproach can help prevent very popular results from leaking into irrelevant queries. Notethat this is opposite the more conventional advice of having more regularization on
feature columns with more unique values.
2. Only allow features to have positive weights. Thus, any good feature will be better than afeature that is “unknown”.
3. Don’t have document­only features. This is an extreme version of #1. For example, even
if a given app is a popular download regardless of what the query was, you don’t want toshow it everywhere4. Not having document­only features keeps that simple.

4

The reason you don’t want to show a specific popular app everywhere has to do with the importance ofmaking all the desired apps reachable. For instance, if someone searches for “bird watching app”, theymight download “angry birds”, but that certainly wasn’t their intent. Showing such an app might improve
download rate, but leave the user’s needs ultimately unsatisfied.

Rule #36: Avoid feedback loops with positional features.
The position of content dramatically affects how likely the user is to interact with it. If you put an
app in the first position it will be clicked more often, and you will be convinced it is more likely to
be clicked. One way to deal with this is to add positional features, i.e. features about the positionof the content in the page. You train your model with positional features, and it learns to weight,
for example, the feature "1st­position" heavily. Your model thus gives less weight to other factors
for examples with "1st­position=true". Then at serving you don't give any instances the
positional feature, or you give them all the same default feature, because you are scoring
candidates before you have decided the order in which to display them.
Note that it is important to keep any positional features somewhat separate from the rest of themodel because of this asymmetry between training and testing. Having the model be the sum ofa function of the positional features and a function of the rest of the features is ideal. For
example, don’t cross the positional features with any document feature.
Rule #37: Measure Training/Serving Skew.
There are several things that can cause skew in the most general sense. Moreover, you candivide it into several parts:
1. The difference between the performance on the training data and the holdout data. In
general, this will always exist, and it is not always bad.
2. The difference between the performance on the holdout data and the “next­day” data.
Again, this will always exist. You should tune your regularization to maximize thenext­day performance. However, large drops in performance between holdout andnext­day data may indicate that some features are time­sensitive and possibly degrading
model performance.
3. The difference between the performance on the “next­day” data and the live data. If you
apply a model to an example in the training data and the same example at serving, itshould give you exactly the same result (see Rule #5). Thus, a discrepancy here
probably indicates an engineering error.

ML Phase III: Slowed Growth, Optimization
Refinement, and Complex Models
There will be certain indications that the second phase is reaching a close. First of all, yourmonthly gains will start to diminish. You will start to have tradeoffs between metrics: you will seesome rise and others fall in some experiments. This is where it gets interesting. Since the gains
are harder to achieve, the machine learning has to get more sophisticated.

A caveat: this section has more blue­sky rules than earlier sections. We have seen many teams
go through the happy times of Phase I and Phase II machine learning. Once Phase III has beenreached, teams have to find their own path.
Rule #38: Don’t waste time on new features if unaligned objectives have become theissue.
As your measurements plateau, your team will start to look at issues that are outside the scopeof the objectives of your current machine learning system. As stated before, if the product goals
are not covered by the existing algorithmic objective, you need to change either your objective
or your product goals. For instance, you may optimize clicks, plus­ones, or downloads, but makelaunch decisions based in part on human raters.
Rule #39: Launch decisions are a proxy for long­term product goals.
Alice has an idea about reducing the logistic loss of predicting installs. She adds a feature. The
logistic loss drops. When she does a live experiment, she sees the install rate increase.
However, when she goes to a launch review meeting, someone points out that the number ofdaily active users drops by 5%. The team decides not to launch the model. Alice isdisappointed, but now realizes that launch decisions depend on multiple criteria, only some ofwhich can be directly optimized using ML.
The truth is that the real world is not dungeons and dragons: there are no “hit points” identifying
the health of your product. The team has to use the statistics it gathers to try to effectively
predict how good the system will be in the future. They need to care about engagement, 1 day
active users (DAU), 30 DAU, revenue, and advertiser’s return on investment. These metrics thatare measureable in A/B tests in themselves are only a proxy for more long­term goals: satisfying
users, increasing users, satisfying partners, and profit, which even then you could consider
proxies for having a useful, high quality product and a thriving company five years from now.
The only easy launch decisions are when all metrics get better (or at least do not get
worse). If the team has a choice between a sophisticated machine learning algorithm, and asimple heuristic, if the simple heuristic does a better job on all these metrics, it should choose
the heuristic. Moreover, there is no explicit ranking of all possible metric values. Specifically,
consider the following two scenarios:
Experiment

Daily Active Users

Revenue/DayA

1 million$4 millionB

2 million$2 million

If the current system is A, then the team would be unlikely to switch to B. If the current system isB, then the team would be unlikely to switch to A. This seems in conflict with rational behavior:
however, predictions of changing metrics may or may not pan out, and thus there is a large risk
involved with either change. Each metric covers some risk with which the team is concerned.

Moreover, no metric covers the team’s ultimate concern, “where is my product going to be five
years from now”?
Individuals, on the other hand, tend to favor one objective that they can directly optimize.
Most machine learning tools favor such an environment. An engineer banging out new features
can get a steady stream of launches in such an environment. There is a type of machinelearning, multi­objective learning, which starts to address this problem. For instance, one canformulate a constraint satisfaction problem that has lower bounds on each metric, and optimizes
some linear combination of metrics. However, even then, not all metrics are easily framed asmachine learning objectives: if a document is clicked on or an app is installed, it is because thatthe content was shown. But it is far harder to figure out why a user visits your site. How to
predict the future success of a site as a whole is AI­complete, as hard as computer vision or
natural language processing.

Rule #40: Keep ensembles simple.
Unified models that take in raw features and directly rank content are the easiest models to
debug and understand. However, an ensemble of models (a “model” which combines the scoresof other models) can work better. To keep things simple, each model should either be an
ensemble only taking the input of other models, or a base model taking many features,
but not both. If you have models on top of other models that are trained separately, then
combining them can result in bad behavior.
Use a simple model for ensembling that takes only the output of your “base” models as inputs.
You also want to enforce properties on these ensemble models. For example, an increase in thescore produced by a base model should not decrease the score of the ensemble. Also, it is best
if the incoming models are semantically interpretable (for example, calibrated) so that changesof the underlying models do not confuse the ensemble model. Also, enforce that an increasein the predicted probability of an underlying classifier does not decrease the predictedprobability of the ensemble.
Rule #41: When performance plateaus, look for qualitatively new sources of information
to add rather than refining existing signals.
You’ve added some demographic information about the user. You've added some information
about the words in the document. You have gone through template exploration, and tuned theregularization. You haven’t seen a launch with more than a 1% improvement in your key metrics
in a few quarters. Now what?
It is time to start building the infrastructure for radically different features, such as the history of
documents that this user has accessed in the last day, week, or year, or data from a different
property. Use wikidata entities or something internal to your company (such as Google’s
knowledge graph). Use deep learning. Start to adjust your expectations on how much return youexpect on investment, and expand your efforts accordingly. As in any engineering project, youhave to weigh the benefit of adding new features against the cost of increased complexity.
Rule #42: Don’t expect diversity, personalization, or relevance to be as correlated withpopularity as you think they are.
Diversity in a set of content can mean many things, with the diversity of the source of thecontent being one of the most common. Personalization implies each user gets their own
results. Relevance implies that the results for a particular query are more appropriate for thatquery than any other. Thus all three of these properties are defined as being different from theordinary.
The problem is that the ordinary tends to be hard to beat.
Note that if your system is measuring clicks, time spent, watches, +1s, reshares, et cetera, youare measuring the popularity of the content. Teams sometimes try to learn a personal modelwith diversity. To personalize, they add features that would allow the system to personalize(some features representing the user’s interest) or diversify (features indicating if this document
has any features in common with other documents returned, such as author or content), andfind that those features get less weight (or sometimes a different sign) than they expect.
This doesn’t mean that diversity, personalization, or relevance aren’t valuable. As pointed out inthe previous rule, you can do post­processing to increase diversity or relevance. If you see
longer term objectives increase, then you can declare that diversity/relevance is valuable, aside
from popularity. You can then either continue to use your post­processing, or directly modify theobjective based upon diversity or relevance.
Rule #43: Your friends tend to be the same across different products. Your interests tend
not to be.
Teams at Google have gotten a lot of traction from taking a model predicting the closeness of aconnection in one product, and having it work well on another. Your friends are who they are. On
the other hand, I have watched several teams struggle with personalization features acrossproduct divides. Yes, it seems like it should work. For now, it doesn’t seem like it does. What has
sometimes worked is using raw data from one property to predict behavior on another. Also,
keep in mind that even knowing that a user has a history on another property can help. For
instance, the presence of user activity on two products may be indicative in and of itself.

Related Work
There are many documents on machine learning at Google as well as externally.
● Machine Learning Crash Course: an introduction to applied machine learning

●
●

●
●
●

Machine Learning: A Probabilistic Approach by Kevin Murphy for an understanding of
the field of machine learning
Practical Advice for the Analysis of Large, Complex Data Sets: a data science approachto thinking about data sets.
Deep Learning by Ian Goodfellow et al for learning nonlinear models
Google paper on technical debt, which has a lot of general advice.
Tensorflow Documentation

Acknowledgements
Thanks to David Westbrook, Peter Brandt, Samuel Ieong, Chenyu Zhao, Li Wei, Michalis
Potamias, Evan Rosen, Barry Rosenberg, Christine Robson, James Pine, Tal Shaked, Tushar
Chandra, Mustafa Ispir, Jeremiah Harmsen, Konstantinos Katsiapis, Glen Anderson, Dan
Duckworth, Shishir Birmiwal, Gal Elidan, Su Lin Wu, Jaihui Liu, Fernando Pereira, andHrishikesh Aradhye for many corrections, suggestions, and helpful examples for this document.
Also, thanks to Kristen Lefevre, Suddha Basu, and Chris Berg who helped with an earlier
version. Any errors, omissions, or (gasp!) unpopular opinions are my own.

Appendix
There are a variety of references to Google products in this document. To provide more context,
I give a short description of the most common examples below.

YouTube Overview
YouTube is a streaming video service. Both YouTube Watch Next and YouTube Home Page
teams use ML models to rank video recommendations. Watch Next recommends videos towatch after the currently playing one, while Home Page recommends videos to users browsing
the home page.

Google Play Overview
Google Play has many models solving a variety of problems. Play Search, Play Home Page
Personalized Recommendations, and ‘Users Also Installed’ apps all use machine learning.

Google Plus Overview
Google Plus uses machine learning in a variety of situations: ranking posts in the “stream” ofposts being seen by the user, ranking “What’s Hot” posts (posts that are very popular now),
ranking people you know, et cetera.