1.
Train and deliver machine
learning models to production
with a single command
STEPAN PUSHKAREV
ILNUR GARIFULLIN

2.
Today’s webinar overview
1. Machine Learning Workflow
2. Tools overview
a. Kubeflow
b. Hydrosphere.io
3. Deep Dive into Automation
a. Steps definition
b. Steps automation

3.
Machine Learning
Workflow

4.
ML Workflow
1. Research
2. Data Preparation
3. Model Training
4. Model Cataloguing
5. Model Deployment
6. Model Integration Testing
7. Production Inferencing
8. Model Performance Monitoring
9. Model Maintenance

5.
Step 1: Research
● Defining an objective
● Defining requirements
● Defining methods
● Defining data sources
1.

6.
Step 2: Data Preparation
● Collecting data
● Preparing data
○ Cleaning
○ Feature engineering
○ Transformation
● Important! To be reused for Inferencing.
1. 2.

7.
Step 3: Model Training
● Building the model
● Training the model
● Evaluating the model
● Tuning hyper-parameters
● Versioning training data
1. 2. 3.

8.
Step 4: Model Cataloguing
● Metadata extraction
○ Graph definition
○ Weights
○ Training data version / stats
○ Other dependencies (look_up vocabulary, etc)
● Indexing model’s binaries
● Versioning a model artifact
● Storing a model in Repository
1. 2. 3. 4.

9.
Step 5: Model Deployment
● Preparing infrastructure for the model
● Preparing runtime for the model
● Deploying the model server
● Exposing API endpoints to the model
● Model Integration
1. 2. 3. 4. 5.

10.
Step 6: Model Integration Testing
● Performing integration tests
● Replaying a golden data set
● Replaying edge cases
● Replaying recent traffic
● Asserting results
1. 2. 3. 4. 5. 6.

11.
Step 7: Production Inferencing
● A/B & Canary deployment
● Model scaling
1. 2. 3. 4. 5. 6. 7.

12.
Step 8: Model Performance Monitoring
● System metrics monitoring
● Model metrics tracking
● Model comparison
● Concept drift monitoring
● Anomaly detection
● Data profiling
1. 2. 3. 4. 5. 6. 7. 8.

13.
Step 9: Model Maintenance
● Alerts & Troubleshooting
● Root Cause Analysis
● Edge Case Exploration
● Retraining Dataset Subsampling
● Retraining
1. 2. 3. 4. 5. 6. 7. 8. 9.

14.
The Toolset

15.
The Machine Learning Model Management Platform
The Machine Learning Toolkit for Kubernetes

16.
What is Kubeflow?
● Began as Kubernetes template / blueprint for running Tensorflow
● Evolved into “Toolkit” - loosely coupled tools and blueprints for ML on
Kubernetes

17.
What is Hydrosphere.io?
Hydrosphere.io is a platform for ML models Management.
- An exact value-add “tool” - a part of the toolkit
- Opensource
- Augments Cataloguing, Deployment, Inferencing,
Monitoring and Maintenance

18.
Research Data Prep Training Cataloguing Deployment Integration
Testing
Production
Inferencing
Performance
Monitoring
Model
Maintenance
Tools Landscape
Orchestrate
ModelDB

19.
Deep Dive into
Workflow Automation
Part 1: Creating executables

20.
Step 1: Research

21.
Step 1: Research
MNIST● Objective – given an image of the handwritten
digit, predict what digit it is;
● Requirements – model export with an ease;
● Tools and Methods – Tensorflow Estimator API;
● Data – Mnist dataset

22.
Step 2: Data Preparation

23.
Step 2: Data Preparation — Building Container
FROM python:3.6-slim
RUN pip install numpy==1.14.3 Pillow==5.2.0
ADD ./download.py /src/
WORKDIR /src/
ENTRYPOINT [ "python", "download.py" ]
$ docker build -t {username}/mnist-pipeline-download .
$ docker push {username}/mnist-pipeline-download
Dockerfile

24.
Step 3: Model Training — Building a model

25.
Step 3: Model Training

26.
Step 3.5: Model Training and Saving

27.
Step 4: Model Cataloguing
DIY:
Instrument training
pipeline
Store metadata
Zip model and metadata
Store in S3
Or push to Artifactory
Or push to git

28.
Step 4: Model Cataloguing
DIY:
Instrument training
pipeline
Store metadata
Zip model and metadata
Store in S3
Or push to Artifactory
Or push to git
ModelDB:
Python DSL:
- Sync Model
- Sync Test data
- Sync metrics
Nice UI

29.
Step 4: Model Cataloguing
DIY:
Instrument training
pipeline
Store metadata
Zip model and metadata
Store in S3
Or push to Artifactory
Or push to git
ModelDB:
Python DSL:
- Sync Model
- Sync Test data
- Sync metrics
Nice UI
Hydrosphere.io:
$ hs upload /models/mnist/
$ hs profile push /data/mnist/

30.
Step 4: Model Cataloguing
Version
Extract metadata
Build model docker
Image
Store in Docker
Registry
Hydrosphere.io:
$ hs upload /models/mnist/
$ hs profile push /data/mnist/

31.
Step 4: Model Cataloguing

32.
Step 5: Model Deployment
DIY:
Implement model
server (Flask App)
Lookup for model
Dockerize
Add Kube configs, tags
Expose API (HTTP,
gRPC, batch, Streaming)

33.
Step 5: Model Deployment
DIY:
Implement model
server (Flask App)
Lookup for Model
Dockerize
Add Kube configs, tags
Expose API (HTTP,
gRPC, batch, Streaming)
Niche tools:
TensorFlow Serving
PyTorch Serving
Nvidia TensorRT Serving

34.
Step 5: Model Deployment
DIY:
Implement model
server (Flask App)
Lookup for Model
Dockerize
Add Kube configs, tags
Expose API (HTTP,
gRPC, batch, Streaming)
Niche tools:
TensorFlow Serving
PyTorch Serving
Nvidia TensorRT Serving
Hydrosphere.io
$ hs apply -f - << EOF
kind: Application
name : “MyPredictionApp”
singular:
model: mnist:1
runtime:
“serving-runtime-python:1.7.0-latest”
EOF

35.
Step 5: Model Deployment
Hydrosphere.io
$ hs apply -f - << EOF
kind: Application
name : “MyPredictionApp”
singular:
model: mnist:1
runtime:
“serving-runtime-python:1.7.0-latest”
EOF
metadata
runtime
model
Model launched on Kube
HTTP, gRPC, Kafka API

36.
Step 6: Model Integration Testing
DIY:
Implement testing
script
Dockerize, add to Kube
Replay a golden data
Replay edge cases
Replay recent traffic
Asserting results

37.
Step 6: Model Integration Testing
DIY:
Implement testing
script
Dockerize, add to Kube
Replay a golden data
Replay edge cases
Replay recent traffic
Asserting results

38.
Step 6: Model Integration Testing
DIY:
Implement testing
script
Dockerize, add to Kube
Replay a golden data
Replay edge cases
Replay recent traffic
Asserting results
Hydrosphere Serving (Q2 2019)
$ hs test -f /test/dataset
$ hs test replay anomalies
$ hs test replay <from_date>

39.
Step 7: Production Inference

40.
Step 8: Model Performance Monitoring

41.
Step 9: Model Maintenance
alert
accuracy
drops
data
changed
what exactly

42.
Step 9: Model Maintenance - explainability of monitoring alert

43.
Deep Dive into
Workflow Automation
Part 2: Defining Kubeflow Pipeline

44.
Defining Kubeflow Pipeline

45.
Parametrizing function

46.
Stage 1: Defining Downloading Container

47.
Stage 1: Mounting Volumes

48.
Stage 2: Defining Training Container

49.
Stage 2: Defining Training Container

50.
Stage 3: Defining Uploading Container

51.
Stage 4: Defining Deploying Container

52.
Stage 5: Defining Testing Container

53.
Stage 6: Defining Cleaning Container

54.
Compiling Pipeline
$ python pipeline.py pipeline.tar.gz
$ tar -xvf pipeline.tar.gz # produces pipeline.yaml

55.
Executing Pipeline with a single command
$ argo submit pipeline.yaml --watch

56.
Executing Pipeline with UI

57.
Source code
https://github.com/Hydrospheredata/hydro-serving-kubeflow-demo

58.
Contact Us
GENERAL INQUIRIES
hydrosphere.io
info@hydrosphere.io
linkedin.com/company/hydrospherebigdata
twitter.com/hydrospheredata
facebook.com/hydrosphere.io
ADDRESS
125 University Avenue, Suite 290
Palo Alto, CA, 94301
tel: 650-521-7875
BUSINESS AND TECHNICAL
Stepan Pushkarev
spushkarev@hydrosphere.io
Ilnur Garifullin
igarifullin@provectus.com