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@erinshellman
Wrangle Conf
July 20th, 2017
Building Robust
Pipelines with Airflow
Zymology: is the science of fermentation and it’s applied to
make materials and molecules
!
"
#
$
Beer
Insulin
Food
additives
Plastics
Zymergen provides
a platform for
rapid improvement
of microbial strains
through genetic
engineering.
Robotic automation
Our experimentation
is increasingly
orchestrated with
robotics and machine
learning.
Learning how to efficiently
navigate the genome is the mission
of data science at Zymergen
Blocker: process failure
Orchestrating complex experiments with
robots is hard, and there are process failures.
These failures often cause sporadic, extreme
measurement values.
Blocker: batch effects
We see temporal
effects based on
when experiments
were performed
Blocker:
different interpretations of results
We’re building a platform that can
support any microbe and any molecule.
Sometimes that results in a proliferation
of solutions with disagreement on which
is best.
Processing pipeline
1.Identify process failures
2.Quantify and remove process-
related bias
3.Identify strains that show
improvement using consistent
criteria
Clean model inputs
Outlier detection
Normalization
Hit detection
Rolling our own ETL pipeline
There are many
ways to measure
the concentration of
a molecule.
Any microbe, any
molecule… any
experiment, many
data formats.
Describing complex
processing
dependencies is
hard.
Rolling our own ETL pipeline
Airflow
https://airflow.incubator.apache.org/
“Airflow is a platform to programmatically author, schedule and
monitor workflows.”
Airflow gives us flexibility to apply a common
set of processing steps to variable data
inputs, schedule complex processing
workflows, and has become a delivery
mechanism for our products.
Structure
and Flexibility
e.g. Normalization
Airflow workflows are
described as directed
acyclic graphs (DAGs).
Each task node in the
DAG is an operator.
The anatomy of a
DAG
Custom operators
Ordering
Instantiate DAG
Modularity and flexibility
Airflow + PyStan
With Bayesian hierarchical models we estimate
(and monitor) the distribution of batch effects.
Experimental bias
DropBox
• Scientists at Zymergen work with data using
many different tools including JMP, SQL, and
Excel.
• We use a custom DropBox hook to make
quick data ingestion pipelines.
Alerting /
Communication
3rd-party hooks & operators
Operator
Pairs well with Superset!
“Apache Superset is a
modern, enterprise-ready
business intelligence web
application”
https://github.com/apache/incubator-superset
Constructing machine
learning workflows
Fairflow: Functional Airflow
• The core of Fairflow is an
abstract base class foperator
that takes care of
instantiating your Airflow
operators and setting their
dependencies.
• In Fairflow, DAGs are
constructed from foperators
that create the upstream
operators when the final
foperator is called.
Configuring complex ML
workflows… functionally
Defining ML workflows
In the DAG
definition, create
an instance of the
task.
Then, instantiate a
DAG like usual and
call the compare
task on the DAG.
Defining ML workflows
The design allows for simple creation of
complicated experimental workflows with arbitrary
sets of models, parameters, and evaluation metrics.
Is Airflow for you?
Do you have heterogeneous data sources?
Do you have complex dependencies between
processing tasks?
Do you have data with different velocities?
Do you have constraints on your time?
Probably!
Thanks team!
%%
& '()
*
+

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