More Data Science with Less Engineering: Machine Learning Infrastructure at Netflix

Sep 17, 20190 likes274 views

Presentation about Netlfix's human-centric machine learning infrastructure, Metaflow, at SF Big Analytics Meetup in September 2019.

More Data Science with Less Engineering
ML Infrastructure at Netflix
SF BIG ANALYTICS
SEPTEMBER 2019

“
Architecture
The model always depends on the
previous day’s model, so it needs
to be processed for consecutive
days regardless when the
upstream data updates.

“
Dependencies
“The problem seem to be that a
dependency of fancyimpute,
cvxpy released a broken version
18h ago.

“
Data Access
“Any ideas why this query takes
20-30 mins to run on Presto, and
it seems to be similarly slow on
Spark too?

“
Scalability
“I’m going to start training a set of
thousands of models. I have many
countries, and I need to estimate a
hundred models per country.
We’re training 28 separate models
and for each model, we want to
run a hyperparameter search in
parallel.

Model Operations
“This shouldn’t be happening.
Interestingly, sounds like the
Friday run was successful even
though Thursday and Saturday
both failed.
My first guess is that we’re
increasingly hitting out of memory
errors as our member count rises.

Types of Friction Data Scientists Experience

Model Development
Feature Engineering
Model Operations
Versioning
Architecture
Job Scheduler
Compute Resources
Data Warehouse
How much
data scientist
cares
How much
infrastructure
is needed

Model Development
Feature Engineering
Model Operations
Versioning
Architecture
Job Scheduler
Compute Resources
Data Warehouse

def compute(input):
...
return output
Baseline

class MyFlow(FlowSpec):
@step
def start(self):
self.next(self.a, self.b)
@step
def a(self):
self.next(self.join)
@step
def b(self):
self.next(self.join)
@step
def join(self, inputs):
self.next(self.end)
Architecture

class MyFlow(FlowSpec):
@step
def start(self):
self.next(self.a, self.b)
@conda(libs={‘tensorflow’: ‘1.14’})
@step
def a(self):
self.next(self.join)
@conda(libs={‘tensorflow’: ‘2.0’})
@step
def b(self):
self.next(self.join)
@step
def join(self, inputs):
self.next(self.end)
isolated
tasks
Dependencies

@step
def b(self):
df = MetaflowDataFrame
.from_parquet(parquet_files())
self.next(self.end)
Data Access

@resources(cpu=16)
@step
def a(self):
self.next(self.join)
@resources(memory=200000)
@step
def b(self):
self.next(self.join)
16 cores
200GB RAM
Vertical Scalability

@step
def start(self):
self.grid = [’x’,’y’,’z’]
self.next(self.a, foreach=’grid’)
@step
def a(self):
self.x = ord(self.input)
self.next(self.join)
@step
def join(self, inputs):
self.result=max(i.x for i in inputs)
self.next(self.end)
Horizontal Scalability

namespace('user:brostykus’)
run = Flow('MyFlow').latest_run
print(run.id) # -> 234
print(run.tags) # -> [‘unsampled_model’]
namespace('user:dberg’)
run = Flow('MyFlow').latest_run
print(run.id) # -> 184
print(run.tags) # -> [‘sampled_model’]
# global namespace
namespace(None)
run = Flow('MyFlow').runs('sampled_model’)
print(run.id) # -> 184
Model Ops: Versioning

run = Flow('MyFlow').latest_run
if run['A'].success:
print(run['A'].data.x)
else:
print(run['A'].stderr)
Model Ops: Monitoring

Addressing real pain
points + fanatic user
support = ❤
“The team's bias towards
helpfulness in the slack channel
(even with questions that are
definitely user error) makes
Metaflow the best developer
product at Netflix.

Thank you
If you want to give Metaflow a try in
your organization, please contact us!
Ville Tuulos
@vtuulos
vtuulos@netflix.com

ML gives machines the ability to learn from data without being explicitly programmed. At Netflix, machine learning is used across many areas including recommendation systems, streaming quality, resource management, regional failover, anomaly detection, and capacity forecasting. Netflix uses various ML algorithms like decision trees, neural networks, and regression models to optimize the customer experience and infrastructure operations.

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The fourth speaker at Process Mining Camp 2018 was Wim Kouwenhoven from the City of Amsterdam. Amsterdam is well-known as the capital of the Netherlands and the City of Amsterdam is the municipality defining and governing local policies. Wim is a program manager responsible for improving and controlling the financial function. A new way of doing things requires a different approach. While introducing process mining they used a five-step approach: Step 1: Awareness Introducing process mining is a little bit different in every organization. You need to fit something new to the context, or even create the context. At the City of Amsterdam, the key stakeholders in the financial and process improvement department were invited to join a workshop to learn what process mining is and to discuss what it could do for Amsterdam. Step 2: Learn As Wim put it, at the City of Amsterdam they are very good at thinking about something and creating plans, thinking about it a bit more, and then redesigning the plan and talking about it a bit more. So, they deliberately created a very small plan to quickly start experimenting with process mining in small pilot. The scope of the initial project was to analyze the Purchase-to-Pay process for one department covering four teams. As a result, they were able show that they were able to answer five key questions and got appetite for more. Step 3: Plan During the learning phase they only planned for the goals and approach of the pilot, without carving the objectives for the whole organization in stone. As the appetite was growing, more stakeholders were involved to plan for a broader adoption of process mining. While there was interest in process mining in the broader organization, they decided to keep focusing on making process mining a success in their financial department. Step 4: Act After the planning they started to strengthen the commitment. The director for the financial department took ownership and created time and support for the employees, team leaders, managers and directors. They started to develop the process mining capability by organizing training sessions for the teams and internal audit. After the training, they applied process mining in practice by deepening their analysis of the pilot by looking at e-invoicing, deleted invoices, analyzing the process by supplier, looking at new opportunities for audit, etc. As a result, the lead time for invoices was decreased by 8 days by preventing rework and by making the approval process more efficient. Even more important, they could further strengthen the commitment by convincing the stakeholders of the value. Step 5: Act again After convincing the stakeholders of the value you need to consolidate the success by acting again. Therefore, a team of process mining analysts was created to be able to meet the demand and sustain the success. Furthermore, new experiments were started to see how process mining could be used in three audits in 2018.

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The third speaker at Process Mining Camp 2018 was Dinesh Das from Microsoft. Dinesh Das is the Data Science manager in Microsoft’s Core Services Engineering and Operations organization. Machine learning and cognitive solutions give opportunities to reimagine digital processes every day. This goes beyond translating the process mining insights into improvements and into controlling the processes in real-time and being able to act on this with advanced analytics on future scenarios. Dinesh sees process mining as a silver bullet to achieve this and he shared his learnings and experiences based on the proof of concept on the global trade process. This process from order to delivery is a collaboration between Microsoft and the distribution partners in the supply chain. Data of each transaction was captured and process mining was applied to understand the process and capture the business rules (for example setting the benchmark for the service level agreement). These business rules can then be operationalized as continuous measure fulfillment and create triggers to act using machine learning and AI. Using the process mining insight, the main variants are translated into Visio process maps for monitoring. The tracking of the performance of this process happens in real-time to see when cases become too late. The next step is to predict in what situations cases are too late and to find alternative routes. As an example, Dinesh showed how machine learning could be used in this scenario. A TradeChatBot was developed based on machine learning to answer questions about the process. Dinesh showed a demo of the bot that was able to answer questions about the process by chat interactions. For example: “Which cases need to be handled today or require special care as they are expected to be too late?”. In addition to the insights from the monitoring business rules, the bot was also able to answer questions about the expected sequences of particular cases. In order for the bot to answer these questions, the result of the process mining analysis was used as a basis for machine learning.

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Zig Websoftware creates process management software for housing associations. Their workflow solution is used by the housing associations to, for instance, manage the process of finding and on-boarding a new tenant once the old tenant has moved out of an apartment. Paul Kooij shows how they could help their customer WoonFriesland to improve the housing allocation process by analyzing the data from Zig's platform. Every day that a rental property is vacant costs the housing association money. But why does it take so long to find new tenants? For WoonFriesland this was a black box. Paul explains how he used process mining to uncover hidden opportunities to reduce the vacancy time by 4,000 days within just the first six months.

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More Data Science with Less Engineering: Machine Learning Infrastructure at Netflix

1. More Data Science with Less Engineering ML Infrastructure at Netflix SF BIG ANALYTICS SEPTEMBER 2019

2. “ Architecture The model always depends on the previous day’s model, so it needs to be processed for consecutive days regardless when the upstream data updates.

3. “ Dependencies “The problem seem to be that a dependency of fancyimpute, cvxpy released a broken version 18h ago.

4. “ Data Access “Any ideas why this query takes 20-30 mins to run on Presto, and it seems to be similarly slow on Spark too?

5. “ Scalability “I’m going to start training a set of thousands of models. I have many countries, and I need to estimate a hundred models per country. We’re training 28 separate models and for each model, we want to run a hyperparameter search in parallel.

6. Model Operations “This shouldn’t be happening. Interestingly, sounds like the Friday run was successful even though Thursday and Saturday both failed. My first guess is that we’re increasingly hitting out of memory errors as our member count rises.

7. Types of Friction Data Scientists Experience

8. Model Development Feature Engineering Model Operations Versioning Architecture Job Scheduler Compute Resources Data Warehouse How much data scientist cares How much infrastructure is needed

9. Model Development Feature Engineering Model Operations Versioning Architecture Job Scheduler Compute Resources Data Warehouse

10. def compute(input): ... return output Baseline

11. class MyFlow(FlowSpec): @step def start(self): self.next(self.a, self.b) @step def a(self): self.next(self.join) @step def b(self): self.next(self.join) @step def join(self, inputs): self.next(self.end) Architecture

12. class MyFlow(FlowSpec): @step def start(self): self.next(self.a, self.b) @conda(libs={‘tensorflow’: ‘1.14’}) @step def a(self): self.next(self.join) @conda(libs={‘tensorflow’: ‘2.0’}) @step def b(self): self.next(self.join) @step def join(self, inputs): self.next(self.end) isolated tasks Dependencies

13. @step def b(self): df = MetaflowDataFrame .from_parquet(parquet_files()) self.next(self.end) Data Access

14. @resources(cpu=16) @step def a(self): self.next(self.join) @resources(memory=200000) @step def b(self): self.next(self.join) 16 cores 200GB RAM Vertical Scalability

15. @step def start(self): self.grid = [’x’,’y’,’z’] self.next(self.a, foreach=’grid’) @step def a(self): self.x = ord(self.input) self.next(self.join) @step def join(self, inputs): self.result=max(i.x for i in inputs) self.next(self.end) Horizontal Scalability

16. namespace('user:brostykus’) run = Flow('MyFlow').latest_run print(run.id) # -> 234 print(run.tags) # -> [‘unsampled_model’] namespace('user:dberg’) run = Flow('MyFlow').latest_run print(run.id) # -> 184 print(run.tags) # -> [‘sampled_model’] # global namespace namespace(None) run = Flow('MyFlow').runs('sampled_model’) print(run.id) # -> 184 Model Ops: Versioning

17. run = Flow('MyFlow').latest_run if run['A'].success: print(run['A'].data.x) else: print(run['A'].stderr) Model Ops: Monitoring

18. Addressing real pain points + fanatic user support = ❤ “The team's bias towards helpfulness in the slack channel (even with questions that are definitely user error) makes Metaflow the best developer product at Netflix.

19. Metaflow Adoption at Netflix

20. Time to First Production Deployment

21. Thank you If you want to give Metaflow a try in your organization, please contact us! Ville Tuulos @vtuulos vtuulos@netflix.com

More Data Science with Less Engineering: Machine Learning Infrastructure at Netflix

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