12. Open Source Pipeline
Operationalizing a Machine
Learning model can be super hard.
It is a stage where most enterprise
Machine Learning projects fail. I
cannot tell you how many
companies I've talked to, who have
said their innovation teams had
devised these cool ML projects, but
they were struggling getting the ML
models into production. In this set
of courses, we will talk about how
to train, deploy, and predict with ML
models in a way that their
production ready. And finally, we
delve back into Machine Learning
theory.
Valliappa Lakshmanan.
Tech Lead for Big Data and Machine
Learning Professional Services on Google
Cloud Platform.
13. 1. Model Engineering 2. Model Training 3. Monitoring 4. Debugging 5. Model Serving
14. 1. Data Preparation using
Spark
7. Streaming of requests
...
Public Cloud Pipeline
Model Engineering 2. Model Training 3. Monitoring 4. Debugging 5. Model Serving
15. 1. Data Preparation using
Spark
7. Kafka stream of
requests
DIY Open Source Pipeline
1. Model
Engineering
2. Model Training 3. Monitoring 4. Debugging 5. Model Serving
16. Data Science Pipeline on DC/OS
Continuous Integration
Monitoring & Operations
Distributed Data
Storage and
Streaming
Data Preparation
and Analysis
Storage of trained
Models and
Metadata
Use trained Model
for Inference
Distributed
Training using
Machine Learning
Frameworks
Data & Streaming
Model
Engineering
Model
Management
Model Serving
Model
Training
Management
Tensorboard
17. Data Science Pipeline on DC/OS
Continuous Integration
Monitoring & Operations
Distributed Data
Storage and
Streaming
Data Preparation
and Analysis
Storage of trained
Models and
Metadata
Use trained Model
for Inference
Distributed
Training using
Machine Learning
Frameworks
Data & Streaming
Model
Engineering
Model
Management
Model Serving
Model
Training
Management
Tensorboard
20. Data Science Pipeline on DC/OS
Continuous Integration
Monitoring & Operations
Distributed Data
Storage and
Streaming
Data Preparation
and Analysis
Storage of trained
Models and
Metadata
Use trained Model
for Inference
Distributed
Training using
Machine Learning
Frameworks
Data & Streaming
Model
Engineering
Model
Management
Model Serving
Model
Training
Management
Tensorboard
23. Data Science Pipeline on DC/OS
Continuous Integration
Monitoring & Operations
Distributed Data
Storage and
Streaming
Data Preparation
and Analysis
Storage of trained
Models and
Metadata
Use trained Model
for Inference
Distributed
Training using
Machine Learning
Frameworks
Data & Streaming
Model
Engineering
Model
Management
Model Serving
Model
Training
Management
Tensorboard
34. Data Science Pipeline on DC/OS
Continuous Integration
Monitoring & Operations
Distributed Data
Storage and
Streaming
Data Preparation
and Analysis
Storage of trained
Models and
Metadata
Use trained Model
for Inference
Distributed
Training using
Machine Learning
Frameworks
Data & Streaming
Model
Engineering
Model
Management
Model Serving
Model
Training
Management
Tensorboard
36. Data Science Pipeline on DC/OS
Continuous Integration
Monitoring & Operations
Distributed Data
Storage and
Streaming
Data Preparation
and Analysis
Storage of trained
Models and
Metadata
Use trained Model
for Inference
Distributed
Training using
Machine Learning
Frameworks
Data & Streaming
Model
Engineering
Model
Management
Model Serving
Model
Training
Management
Tensorboard
39. Data Science Pipeline on DC/OS
Continuous Integration
Monitoring & Operations
Distributed Data
Storage and
Streaming
Data Preparation
and Analysis
Storage of trained
Models and
Metadata
Use trained Model
for Inference
Distributed
Training using
Machine Learning
Frameworks
Data & Streaming
Model
Engineering
Model
Management
Model Serving
Model
Training
Management
Tensorboard