Kai Wähner, Technology Evangelist at Confluent: "Development of Scalable Machine Learning Microservices with Apache Kafka Streams and H2O.ai"

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Kai Wähner, Technology Evangelist at Confluent: "Development of Scalable Machine Learning Microservices with Apache Kafka Streams and H2O.ai" Kai Wähner works as Technology Evangelist at Confluent. Kai’s main area of expertise lies within the fields of Big Data Analytics, Machine Learning, Integration, Microservices, Internet of Things, Stream Processing and Blockchain. He is regular speaker at international conferences such as JavaOne, O’Reilly Software Architecture or ApacheCon, writes articles for professional journals, and shares his experiences with new technologies on his blog (www.kai-waehner.de/blog). Contact and references: kontakt@kai-waehner.de / @KaiWaehner / www.kai-waehner.de Abstract: Big Data and Machine Learning are key for innovation in many industries today. The first part of this session explains how to build analytic models with R, Python or Scala leveraging open source machine learning / deep learning frameworks like Apache Spark, TensorFlow or H2O.ai. The second part discusses the deployment of these built analytic models to your own applications or microservices leveraging the Apache Kafka cluster and Kafka Streams. The session focuses on live demos and teaches lessons learned for executing analytic models in a highly scalable and performant way.

Data & Analytics

1Confidential
Apache Kafka + H2O.ai
Machine Learning Applied to Real Time Stream Processing
Kai Waehner
Technology Evangelist
kontakt@kai-waehner.de
LinkedIn
@KaiWaehner
www.kai-waehner.de

2Apache Kafka and Machine Learning
Agenda
1) Machine Learning and Real Time Applications
2) Building an Analytic Model with H2O.ai
3) Applying an Analytic Model with Apache Kafka

3Apache Kafka and Machine Learning
Agenda
1) Machine Learning and Real Time Applications
2) Building an Analytic Model with H2O.ai
3) Applying an Analytic Model with Apache Kafka

4Apache Kafka and Machine Learning
Machine Learning
... allows computers to find hidden insights without being
explicitly programmed where to look.

5Apache Kafka and Machine Learning
Real World Examples of Machine Learning
Spam Detection
Search Results +
Product Recommendation
Picture Detection
(Friends, Locations, Products)
Your Company
The Next Disruption:
Google Beats Go Champion

6Apache Kafka and Machine Learning
Leverage Machine Learning to Analyze and Act on Critical Business Moments
Seconds Minutes Hours
Price
Optimization
Predictive
Maintenance
Fraud
Detection
Cross Selling
Transportation
Rerouting
Customer
Service
Inventory
Management
Windows of Opportunity

7Apache Kafka and Machine Learning
Big Data Analytics for Actionable Insights
From Insight to Action
(continuous loop)

8Apache Kafka and Machine Learning
Streaming Platform
Big Data Analytics
Database
IoT Device
Streaming
Producer
…..
DWH
Data
Integration
C
O
N
N
E
C
T
C
O
N
N
E
C
T
Data Lake
Model
Building
Batch
Real
Time
Stream
Processing
REST
Interface
IoT Device
Mobile
App
Streaming
Consumer
C
O
N
N
E
C
T
C
O
N
N
E
C
T
BI Tool
Messaging
Web
Application
Model
Schema Registry
/ Governance
1) Data Producer
2) Analytics Platform
3) Streaming Platform
4) Data Consumer

9Apache Kafka and Machine Learning
Agenda
1) Machine Learning and Real Time Applications
2) Building an Analytic Model with H2O.ai
3) Applying an Analytic Model with Apache Kafka

10Apache Kafka and Machine Learning
STREAMING PLATFORM
BIG DATA ANALYTICS
Oracle DB
CoaP IoT
Kafka
Java Client
…..
HP Vertica
Data
Integration
F
L
U
M
E
H2O.ai,
TensorFlow
Batch
Real
Time
Confluent
REST Proxy
MQTT IoT
iPhone
App
Kafka
Go Client
C
K O
A N
F N
K E
A C
T
H
I
V
E
Grafana
Kafka
Java EE
Web App
Hadoop
C
K O
A N
F N
K E
A C
T
Confluent
Schema Registry
Kafka Streams
H2O.ai
Mesos
Kafka Streams
TensorFlow
Kubernetes
Avro
Avro
1) Data Producer
2) Analytics Platform
3) Streaming Platform
4) Data Consumer

11Apache Kafka and Machine Learning
Languages, Frameworks and Tools
Many more ….
Portable Format
for Analytics (PFA)

12Apache Kafka and Machine Learning
Live Demo
Use Case:
Airline Flight Delay Prediction
Machine Learning Algorithm:
Gradient Boosted Machines (GBM)
using Decision Trees
Technology:
H2O.ai

13Apache Kafka and Machine Learning
Agenda
1) Machine Learning and Real Time Applications
2) Building an Analytic Model with H2O.ai
3) Applying an Analytic Model with Apache Kafka

14Apache Kafka and Machine Learning
STREAMING PLATFORM
BIG DATA ANALYTICS
Oracle DB
CoaP IoT
Kafka
Java Client
…..
HP Vertica
Data
Integration
F
L
U
M
E
H2O.ai,
TensorFlow
Batch
Real
Time
Confluent
REST Proxy
MQTT IoT
iPhone
App
Kafka
Go Client
C
K O
A N
F N
K E
A C
T
H
I
V
E
Grafana
Kafka
Java EE
Web App
Hadoop
C
K O
A N
F N
K E
A C
T
Confluent
Schema Registry
Kafka Streams
H2O.ai
Mesos
Kafka Streams
TensorFlow
Kubernetes
Avro
Avro
1) Data Producer
2) Analytics Platform
3) Streaming Platform
4) Data Consumer

15Apache Kafka and Machine Learning
When to use Kafka Streams for Stream Processing?
No need for a
Big Data cluster
Deploy in your
existing infrastructure
Kafka manages
scalability / fail-over
Focus on development
of business logic
in your department

16Apache Kafka and Machine Learning
Use Case:
Airline Flight Delay Prediction
Machine Learning Algorithm:
Any! (in our example, H2O.ai GBM)
Streaming Platform:
Apache Kafka Core, Kafka’s Streams API
Live Demo with Open Source Technologies

17Apache Kafka and Machine Learning
H2O.ai Model + Kafka Streams
Filter
Map
1) Create H2O ML model
2) Configure Kafka Streams Application
3) Apply H2O ML model to Streaming Data
4) Start Kafka Streams App

18Apache Kafka and Machine Learning
Online Model Training
How to improve models?
1.Manual Update
2.Automated Batch
3.Real Time
à Apache Kafka for Messaging and Real Time Apps

19Apache Kafka and Machine Learning
Caveats for Online Model Training
• Processes and infrastructure not ready
• Validation needed before production
• Slows down the system
• Only a few ML implementations
supported
• Many use cases do not need it

20Apache Kafka and Machine Learning
Kai Waehner
Technology Evangelist
kontakt@kai-waehner.de
@KaiWaehner
www.kai-waehner.de
LinkedIn
Questions? Feedback?
Please contact me!

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2019 04 seattle_meetup___kafka_machine_learning___kai_waehner

Nitin Kumar

Introduction to Apache Kafka and why it matters - Madrid

Paolo Castagna

Data scientists and data engineers love Python for transforming, filtering, and processing data to train and deploy analytic models with frameworks such as TensorFlow. However, in real-world deployments, all of these steps require a scalable and reliable infrastructure. This session shows how data experts can use Python for data processing and model inference at scale, leveraging Python, Jupyter, Apache Kafka, and KSQL. Talk from Oracle Code One / Oracle World 2019 in San Francisco.

Streaming Machine Learning with Python, Jupyter, TensorFlow, Apache Kafka and...

Kai Wähner

Talk at Strate Conference in London: Unleashing Apache Kafka and TensorFlow in Hybrid Cloud Architectures with Confluent: How do you leverage the flexibility and extreme scale of the public cloud and the Apache Kafka ecosystem to build scalable, mission-critical machine learning infrastructures that span multiple public clouds—or bridge your on-premises data centre to the cloud? Join Kai Wähner to learn how to use technologies such as TensorFlow with Kafka’s open source ecosystem for machine learning infrastructures. You’ll learn how to build a scalable, mission-critical machine learning infrastructure for data ingestion and processing, model training, deployment, and monitoring. The discussed architecture includes capabilities like scalable data preprocessing for training and predictions, a combination of different deep learning frameworks, data replication between data centers, intelligent real-time microservices running on Kubernetes, and local deployment of analytic models for offline predictions. Learn how the public cloud allows extreme scale for building analytic models and how the Apache Kafka open source ecosystem enables building a cloud-independent infrastructure for preprocessing and ingestion of data and inference and monitoring of analytic models in real time Understand why hybrid architectures and local model deployment are key for success in many scenarios and why you need a flexible machine learning architecture that supports different technologies and frameworks

Unleashing Apache Kafka and TensorFlow in Hybrid Cloud Architectures

Kai Wähner

Don’t underestimate the Hidden Technical Debt in Machine Learning Systems. Leverage Apache Kafka’s open ecosystem as a scalable and flexible Event Streaming Platform to build one pipeline for real-time and batch use cases. Use Streaming Machine Learning with Apache Kafka, Tiered Storage, and TensorFlow IO to simplify your big data architecture. Tiered Storage for Kafka provides: - one platform for all data processing - an event-based source of truth for materialized views - no need for a pipeline between Kafka and a Data Lake like Hadoop Benefits: - cost reduction - long-term backup - performance isolation (real-time and historical analysis in the same cluster) Use Cases for Reprocessing Historical Events: - New consumer application - Error-handling - Compliance / regulatory processing - Query and analyze existing events - Model training

Apache Kafka, Tiered Storage and TensorFlow for Streaming Machine Learning wi...

Kai Wähner

Machine Learning (ML) is separated into model training and model inference. ML frameworks typically use a data lake like HDFS or S3 to process historical data and train analytic models. But it’s possible to completely avoid such a data store, using a modern streaming architecture. This talk compares a modern streaming architecture to traditional batch and big data alternatives and explains benefits like the simplified architecture, the ability of reprocessing events in the same order for training different models, and the possibility to build a scalable, mission-critical ML architecture for real time predictions with muss less headaches and problems. The talk explains how this can be achieved leveraging Apache Kafka, Tiered Storage and TensorFlow.

Apache Kafka, Tiered Storage and TensorFlow for Streaming Machine Learning wi...

confluent

Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka. Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.

Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka

Kai Wähner

Overview of Apache Flink: the 4G of Big Data Analytics Frameworks

DataWorks Summit/Hadoop Summit

Slides of my talk at the Hadoop Summit Europe in Dublin, Ireland on April 13th, 2016. The talk introduces Apache Flink as both a multi-purpose Big Data analytics framework and real-world streaming analytics framework. It is focusing on Flink's key differentiators and suitability for streaming analytics use cases. It also shows how Flink enables novel use cases such as distributed CEP (Complex Event Processing) and querying the state by behaving like a key value data store.

Overview of Apache Fink: the 4 G of Big Data Analytics Frameworks

Slim Baltagi

Overview of Apache Fink: The 4G of Big Data Analytics Frameworks

Slim Baltagi

Event-based stream processing is a modern paradigm to continuously process incoming data feeds, e.g. for IoT sensor analytics, payment and fraud detection, or logistics. Machine Learning / Deep Learning models can be leveraged in different ways to do predictions and improve the business processes. Either analytic models are deployed natively in the application or they are hosted in a remote model server. In the latter you combine stream processing with RPC / Request-Response paradigm instead of direct doing direct inference within the application. This talk discusses the pros and cons of both approaches and shows examples of stream processing vs. RPC model serving using Kubernetes, Apache Kafka, Kafka Streams, gRPC and TensorFlow Serving. The trade-offs of using a public cloud service like AWS or GCP for model deployment are also discussed and compared to local hosting for offline predictions directly "at the edge".

Event-Driven Model Serving: Stream Processing vs. RPC with Kafka and TensorFl...

confluent

Talk from Kafka Summit San Francisco 2019 (https://kafka-summit.org/sessions/event-driven-model-serving-stream-processing-vs-rpc-kafka-tensorflow/). Video recording will be available for free on the Summit website. Event-based stream processing is a modern paradigm to continuously process incoming data feeds, e.g. for IoT sensor analytics, payment and fraud detection, or logistics. Machine Learning / Deep Learning models can be leveraged in different ways to do predictions and improve the business processes. Either analytic models are deployed natively in the application or they are hosted in a remote model server. In the latter you combine stream processing with RPC / Request-Response paradigm instead of direct doing direct inference within the application. This talk discusses the pros and cons of both approaches and shows examples of stream processing vs. RPC model serving using Kubernetes, Apache Kafka, Kafka Streams, gRPC and TensorFlow Serving. The trade-offs of using a public cloud service like AWS or GCP for model deployment are also discussed and compared to local hosting for offline predictions directly “at the edge”. Key takeaways • Machine Learning / Deep Learning models can be used in different ways to do predictions. Scalability and loose coupling are important success factors • Stream processing vs. RPC / Request-Response for model serving has many trade-offs – learn about alternatives and best practices for your different scenarios • Understand the alternatives and trade-offs of model deployment in modern infrastructures like Kubernetes or Cloud Services like AWS or GCP • See live demos with Java, gRPC, Apache Kafka, KSQL and TensorFlow Serving to understand the trade-offs

Event-Driven Stream Processing and Model Deployment with Apache Kafka, Kafka ...

Kai Wähner

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