MissionGraph QTD Training Materials

MissionGraph™
Persistent Intelligence

Copyright © 2019 Deloitte Development LLC. All rights reserved. | 2
AGENDA
Overview
• MissionGraph + Data Science
Labs
• Deploying Machine-Learning Models at Scale
• Using Cassandra as a Feature Store
• Deploying Graph Analytics at Scale

OVERVIEW

MISSIONGRAPH
+
DATASCIENCE
MissionGraph™ is an analytics accelerator
It provides teams with
• Powerful data science tools for exploration and
analysis
• Fast workflow engines for repeatably training
models and transforming data
• Fully instrumented pipelines for continuously
integrating and deploying micro-services

Graph Analytics
Data Transformation Machine-Learning
GitOps
MissionGraph™

WORKFLOW
ENGINES
TRAIN MODELS
2 SERVE MODELS
1
3 MONITOR RESULTS

CODE
PIPELINES
BUILD AND DEPLOY
APPS
1
GATHER METRICS3
ALERT AND ARCHIVE2

LAB: MACHINE-LEARNING AT SCALE

TRAIN MODEL
2 PERSIST ARTIFACTS
1
4 MONITOR REQUESTS3 SERVE MODEL

https://kubeflow.k8s.platform-furyroad.io/
Navigate to
Log in using your provided credentials

Click on the “Notebooks” tab

Click on the “+ NEW SERVER” icon

Select your Deloitte alias as your server name

Select the following runtime image

Select the default settings for “CPU”, “Memory”, “Workspace Volume” et cetera

Click the “SPAWN” button to create your new notebook

You can now see your notebook running in the main landing page
Click “CONNECT” once your
notebook is ready

Create a new terminal session in your notebook

Run the following command in the terminal session to download an example repo
git clone https://gitlab.k8s.deloitteplatform.io/opioids360/qtd-training.git
**When prompted, enter the Gitlab username/password you were provided

Run the following command in the terminal session, updating it with your alias
Then run the next command and enter your provided Gitlab username/password
cd qtd-training && /bin/bash find-and-replace.sh “your-deloitte-alias”
git push origin “your-deloitte-alias”

Click on the newly downloaded “qtd-training” directory

Click on the notebook file called “model-training-example.ipynb”

Now let’s start working through the model training example

Click on the notebook file called “model-serving-example.ipynb”

Now let’s start working through the model serving example

Let’s navigate to some of our dashboards
https://dashboard.k8s.platform-furyroad.io
https://kiali.k8s.platform-furyroad.io
https://grafana.k8s.platform-furyroad.io

Use this dashboard token to access the following site
eyJhbGciOiJSUzI1NiIsImtpZCI6IiJ9.eyJpc3MiOiJrdWJlcm5ldGVzL3NlcnZpY2VhY2NvdW50Iiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9uY
W1lc3BhY2UiOiJrdWJlLXN5c3RlbSIsImt1YmVybmV0ZXMuaW8vc2VydmljZWFjY291bnQvc2VjcmV0Lm5hbWUiOiJ2aWV3ZXItdG9rZW4td3A5Y3
giLCJrdWJlcm5ldGVzLmlvL3NlcnZpY2VhY2NvdW50L3NlcnZpY2UtYWNjb3VudC5uYW1lIjoidmlld2VyIiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb
3VudC9zZXJ2aWNlLWFjY291bnQudWlkIjoiNWJhNTlmODAtODNkOC0xMWU5LTkwYzktMGEwMmM1ZGZmNGRlIiwic3ViIjoic3lzdGVtOnNlcnZpY
2VhY2NvdW50Omt1YmUtc3lzdGVtOnZpZXdlciJ9.ifZJwDt37hLFJfvf7X_0XAAYElZwEkikwXIMdBLlBHXOM2ICE8QHz6CIW9CGYMfazl_z9dKoAjskS
bHFaGjZkOtGpCEom1yJt-nPUtAj5QVnEsUMY-RLe6qju-nfvv58Ks4MPrl2yDkyqsAZDDCLFfRb5dacsAxMYx6dOT6TCg12z9-
NJamBJVljCH_xXSDReVdXGIOhMfmWYnOEuBWRXfOwgMgpj8vBKXvxxt2utre9FRX1iDtBLjI0mS-
xaNqjEKS3gkIIb9MkzKtRYRkUUt555puCgj5DzQynQEKoPfkc1lNhWG0T1GBEmgXr4Ea1cqQDxbG47T1iBFUZ4LOVWQ

Click on the notebook file called “tf-model-training-example.ipynb”

Now let’s start working through the tensorfow model training example

Click on the notebook file called “tf-model-serving-example.ipynb”

Now let’s start working through the tensorflow model serving example

Let’s navigate to the following URL and test out our new tensorflow model
https://tf-web-ui.k8s.platform-furyroad.io/?name=mnist&addr=mnist-service-
local&port=9000

Enter your model URL, then select “CONNECT”

Click on the “TEST RANDOM IMAGE” button to generate predictions from your model

KEYTAKEAWAYS MissionGraph is amazing!

LAB: C* AS A FEATURE STORE

WRITE MODEL PREDICTIONS
TO CASSANDRA2
READ DATA AND FEATURES
FROM CASSANDRA1

Click on the notebook file called “cassandra-feature-store-example.ipynb”

Now let’s start working through the cassandra example

LAB: GRAPH ANALYTICS AT SCALE

CREATE GRAPH
ANALYTIC 2 PERSIST ARTIFACTS
1
4 MONITOR REQUESTS3 DEPLOY GRAPH
ANALYTIC

Navigate back to your notebook from the previous lab
Click “CONNECT”
https://kubeflow.k8s.platform-furyroad.io/_/notebooks

Create a new terminal session in your notebook

Run the following command in the terminal session to download an example repo
git clone https://gitlab.k8s.deloitteplatform.io/opioids360/qtd-graph-training.git
**When prompted, enter the Gitlab username/password you were provided

Run the following command in the terminal session, updating it with your alias
Then run the next command and enter your provided Gitlab username/password
cd qtd-graph-training && /bin/bash find-and-replace.sh “your-deloitte-alias”
git push origin “your-deloitte-alias”

Click on the newly downloaded “qtd-graph-training” directory

Click on the notebook file called “graph-analytic-example.ipynb”

Now let’s start working through the graph analytic example

Click on the notebook file called “graph-serving-example.ipynb”

Now let’s start working through the graph serving example

Let’s navigate to some of our dashboards
https://kiali.k8s.platform-furyroad.io
https://grafana.k8s.platform-furyroad.io

Use this dashboard token to access the following site
eyJhbGciOiJSUzI1NiIsImtpZCI6IiJ9.eyJpc3MiOiJrdWJlcm5ldGVzL3NlcnZpY2VhY2NvdW50Iiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9uY
W1lc3BhY2UiOiJrdWJlLXN5c3RlbSIsImt1YmVybmV0ZXMuaW8vc2VydmljZWFjY291bnQvc2VjcmV0Lm5hbWUiOiJ2aWV3ZXItdG9rZW4td3A5Y3
giLCJrdWJlcm5ldGVzLmlvL3NlcnZpY2VhY2NvdW50L3NlcnZpY2UtYWNjb3VudC5uYW1lIjoidmlld2VyIiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb
3VudC9zZXJ2aWNlLWFjY291bnQudWlkIjoiNWJhNTlmODAtODNkOC0xMWU5LTkwYzktMGEwMmM1ZGZmNGRlIiwic3ViIjoic3lzdGVtOnNlcnZpY
2VhY2NvdW50Omt1YmUtc3lzdGVtOnZpZXdlciJ9.ifZJwDt37hLFJfvf7X_0XAAYElZwEkikwXIMdBLlBHXOM2ICE8QHz6CIW9CGYMfazl_z9dKoAjskS
bHFaGjZkOtGpCEom1yJt-nPUtAj5QVnEsUMY-RLe6qju-nfvv58Ks4MPrl2yDkyqsAZDDCLFfRb5dacsAxMYx6dOT6TCg12z9-
NJamBJVljCH_xXSDReVdXGIOhMfmWYnOEuBWRXfOwgMgpj8vBKXvxxt2utre9FRX1iDtBLjI0mS-
xaNqjEKS3gkIIb9MkzKtRYRkUUt555puCgj5DzQynQEKoPfkc1lNhWG0T1GBEmgXr4Ea1cqQDxbG47T1iBFUZ4LOVWQ

This publication contains general information only, and none of the member firms of Deloitte Touche Tohmatsu Limited, its member firms, or their related
entities (collective, the “Deloitte Network”) is, by means of this publication, rendering professional advice or services. Before making any decision or taking any
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As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed
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Copyright © 2019 Deloitte Development LLC.
All rights reserved. Member of Deloitte Touche Tohmatsu Limited
PLATFORM™
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