1.
Alex Zeltov
An Open Source Platform for the Machine
Learning Lifecycle for On-Prem or in the
Cloud
Introductionto Ml

2.
Alex Zeltov
Big Data Solutions Architect / AI Engineer
Background:
• Sr. Solutions Architect - part of a Global Black Belt Team Big
Data & AI in Microsoft
• Sr. Solutions Engineer at Hortonworks specializing in HDP and
HDF
• Research Scientist at Independent Blue Cross – BigData and
ML
• Sr. Software Engineer at Oracle

3.
Machine Learning E2E Development is Complex!!!
Typical E2E Process
…
Prepare Experiment Deploy
Orchestrate
Result: It is difficult to productionize and share.

4.
Motivation: the Deployment Process
5
Data Engineer
Dev Ops
Pickled Model
What’sPickle?
Data Scientist

5.
ML Development Challenges
• 100s of software tools to leverage
• Hard to track & reproduce results: code, data, params, etc
• Hard to productionize models
• Needs large scale for best results

6.
Custom MLPlatforms
Facebook FBLearner,Uber Michelangelo, Google TFX
+Standardize the data prep /training / deployloop:
if you work with theplatform,you get these!
–Limited to a few algorithms orframeworks
–Tied to one company’sinfrastructure
–Out of luck if you left the company….
Can we provide similar benefits in an open manner?

7.
• Open source platform for the machine learning lifecycle.
• API First:
 Allow submitting runs, models, works with any ML
library & language
 MlFlow APIs are available for the Python, R and Java
 REST API
• Runs the same way anywhere: on-prem or any cloud
• > 3,700 stars on GitHub , 92 contributors from > 40
companies. > 200 companies are now using MlFlow*
Introductionto Ml
* https://www.oreilly.com/ideas/specialized-tools-for-machine-learning-development-and-model-governance-are-becoming-essential

8.
MlFlowComponents
Tracking
Record and query
experiments: code,
data, config, results
Projects
Packaging format
for reproducibleruns
onany platform
Models
General modelformat
that supports diverse
deploymenttools
distinct components: use different components individually based on your needs

9.
Tracking Experiments with MlFlowTracking Server
MLflow Tracking is…
• a logging API specific for machine learning
• agnostic to libraries and environments that do the training
• organized around the concept of runs, which are executions of data
science code
• runs are aggregated into experiments where many runs can be a part
of a given experiment
• An MLflow server can host many experiments

10.
MlFlowTracking Server
Azure Machine
Learning
DatabricksIaaS CloudOn-Premise
pip install mlflow
mlflow.set_tracking_uri(URI)
CDH / HDP

11.
Experiments inTracking Server
Parameters: key-value inputs to your code
Metrics: numeric values (can update over time)
Artifacts: arbitrary files, including models
Tags/Notes: info about a run
Source: what code ran
Version: git version
import mlflow
# log model’s tuning parameters
with mlflow.start_run():
mlflow.log_param("layers", layers)
mlflow.log_param("alpha", alpha)
# log model’s metrics
mlflow.log_metric("mse", model.mse())
mlflow.log_artifact("plot", model.plot(test_df))
mlflow.tensorflow.log_model(model)

12.
Experiments Tracking API
Record and query
experiments: code,
configs, results,etc

13.
Demo MlFlow
Tracking Server

14.
MlFlow Projects
There are a number of different reasons why teams need to package their
machine learning projects:
• Projects have various library dependencies
• shipping a machine learning solution involves the environment in which it was built
• MLflow allows for this environment to be a conda environment or docker container
• This means that teams can easily share and publish their code for others to use
• Machine learning projects become increasingly complex as time goes on
• This includes ETL and featurization steps, machine learning models used for pre-
processing, and finally the model training itself
• Each component of a machine learning pipeline needs to allow for tracing
its lineage
• If there's a failure at some point, tracing the full end-to-end lineage of a model
allows for easier debugging.

15.
MlFlow Projects
Project Spec
Code DataConfig
Local Execution
Remote Execution

16.
Example MlFlowProject
my_project/
├── MLproject
│
│
│
│
│
├── conda.yaml
├── main.py
└── model.py
...
conda_env: conda.yaml
entry_points:
main:
parameters:
training_data: path
lambda: {type: float, default: 0.1}
command: python main.py {training_data} {lambda}
$ mlflow run ml-production/mlflow-model-training/ -P
data_path=airbnb-cleaned-mlflow.csv
$ mlflow run git://https://github.com/mlflow/mlflow-example
mlflow.run(
uri="https://github.com/mlflow/mlflow-example",
parameters={'alpha':0.4}
)

17.
Demo MlFlow Project

18.
MlFlow Models
• Once a model has been trained and bundled with the environment it
was trained in. The next step is to package the model so that it can be
used by a variety of serving tools
• Current deployment options include:
• Container-based REST servers
• Continuous deployment using Spark streaming
• Batch
• Managed cloud platforms such as Azure ML and AWS SageMaker
• Packaging the final model in a platform-agnostic way offers the most
flexibility in deployment options and allows for model reuse across a
number of platforms.

19.
MlFlow Models
MLFrameworks
InferenceCode
Batch &Stream Scoring
Serving ToolsStandard for MLmodels
ModelFormat
mlflow.pyfunc
mlflow.h2o
mlflow.keras
mlflow.pytorch
mlflow.sklearn
mlflow.spark
mlflow.tensorflow

20.
Example MlFlow Model
my_model/
├── MLmodel
│
│
│
│
│
└── estimator/
├── saved_model.pb
└── variables/
...
Usable by tools thatunderstand
TensorFlowmodel format
Usable by any tool that can run
Python (Docker,Spark, etc!)
run_id: 769915006efd4c4bbd662461
time_created: 2018-06-28T12:34
flavors:
tensorflow:
saved_model_dir: estimator
signature_def_key: predict
python_function:
loader_module: mlflow.tensorflow
>>> mlflow.tensorflow.log_model(...)

21.
Demo MlFlow Model

22.
MlFlow +
Azure Machine Learning +
Azure Databricks
https://www.zdnet.com/article/microsoft-to-join-mlflow-
project-add-native-support-to-azure-machine-learning/

23.
What is Azure Machine Learning service?
Set of Azure
Cloud Services
Python
SDK
 Prepare Data
 Build Models
 Train Models
 Manage Models
 Track Experiments
 Deploy Models
That enables
you to:

24.
Machine Learning on Azure
Domain specific pretrained models
To reduce time to market
Azure
Databricks
Machine
Learning VMs
Popular frameworks
To build advanced deep learning solutions
TensorFlowPytorch Onnx
Azure Machine
Learning
LanguageSpeech
…
SearchVision
Productive services
To empower data science and development teams
Powerful infrastructure
To accelerate deep learning
Scikit-Learn
PyCharm Jupyter
Familiar Data Science tools
To simplify model development
Visual Studio Code Command line
CPU GPU FPGA
From the Intelligent Cloud to the Intelligent Edge

25.
Azure ML service
Key Artifacts
Workspace

26.
Azure ML: How to deploy models at scale

27.
Demo MlFlow + AML +
Azure Databricks
https://eastus2.azuredatabricks.net/?o=3336252523001260#n
otebook/2623556200920093/command/2623556200920094

28.
Conclusion + Q & A
• MlFlow can greatly simplify the ML lifecycle
• Simplify lifecycle development
• Lightweight, open platform that integrates easily
• Available APIs: Python, Java &R
• Easy to install and use
• Develop locally and track locally or remotely
• Deploy locally, cloud, on premise…
• Visualize experiments

29.
Learning More AboutMlFlow
• pip install mlflow to get started
• Find docs &examples atmlflow.org
• https://github.com/mlflow/mlflow
• tinyurl.com/mlflow-slack
• https://docs.azuredatabricks.net/applications/mlflow/quick-
start.html