https://princetonacm.acm.org/tcfpro/ 18th Annual IEEE IT Professional Conference (ITPC) Armstrong Hall at The College of New Jersey Friday, March 15th, 2024 | 10:00 AM to 5:00 PM IT Professional Conference at Trenton Computer Festival IEEE Information Technology Professional Conference on Friday, March 15th, 2024 TCFPro24 Building Real-Time Generative AI Pipelines Building Real-Time Generative AI Pipelines In this talk, Tim will delve into the exciting realm of building real-time generative AI pipelines with streaming capabilities. The discussion will revolve around the integration of cutting-edge technologies to create dynamic and responsive systems that harness the power of generative algorithms. From leveraging streaming data sources to implementing advanced machine learning models, the presentation will explore the key components necessary for constructing a robust real-time generative AI pipeline. Practical insights, use cases, and best practices will be shared, offering a comprehensive guide for developers and data scientists aspiring to design and implement dynamic AI systems in a streaming environment. Tim will show a live demo showing we can use Apache NiFi to provide a live chat between a person in Slack and several LLM models all orchestrated with Apache NiFi, Apache Kafka and Python. We will use RAG against Chroma and Pinecone vector data stores, Hugging Face and WatsonX.AI LLM, and add additional context with NiFi lookups of stocks, weather and other data streams in real-time. Timothy Spann Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.

1. © 2024 Cloudera, Inc. All rights reserved.
Building Real-Time Generative AI
Pipelines
Tim Spann
Principal Developer Advocate
March 15, 2024

2. © 2024 Cloudera, Inc. All rights reserved.

3. © 2024 Cloudera, Inc. All rights reserved. 3
This week in Apache NiFi, Apache Flink,
Apache Kafka, ML, AI, Apache Spark, Apache
Iceberg, Python, Java and Open Source
friends.
https://bit.ly/32dAJft
https://www.meetup.com/futureofdata-
princeton/
FLaNK Stack Weekly by Tim Spann

4. © 2024 Cloudera, Inc. All rights reserved. 4
Confidential—Restricted
@PaasDev
https://www.meetup.com/futureofdata-princeton/
From Big Data to AI to Streaming to Containers to
Cloud to Analytics to Cloud Storage to Fast Data to
Machine Learning to Microservices to ...
Future of Data - NYC + NJ + Philly + Virtual
https://linktr.ee/tspannhw

5. © 2024 Cloudera, Inc. All rights reserved. 5
Tim Spann
Twitter: @PaasDev Blog: datainmotion.dev
Principal Developer Advocate
Princeton Future of Data Meetup
ex-Pivotal, ex-Hortonworks,
ex-StreamNative, ex-HPE,
ex-PwC, ex-EY.
https://medium.com/@tspann
https://github.com/tspannhw

6. © 2024 Cloudera, Inc. All rights reserved. 6
Some common Vector DBs Open Community & Open Models
RAPID INNOVATION IN THE LLM SPACE
Too much to cover today.. but you should know the common LLMs, Frameworks, Tools
Notable LLMs
Closed Models Open Models
GPT3.5
GPT4
Llama2
Mistral7B
Mixtral8x7B
Claude2
++ 100s more… check out the HuggingFace LLM
Leaderboard (pretrained, domain fine-tuned, chat models, …)
Code Llama
Popular LLM Frameworks
When to use one over the other? Use Langchain if you need a general-purpose framework with
flexibility and extensibility. Consider LlamaIndex if you’re building a RAG only app (retrieval/search)
Langchain is a framework for
developing apps powered by LLMs
● Python and JavaScript Libraries
● Provides modules for LLM
Interface, Retrieval, & Agents
LLamaIndex is a framework designed
specifically for RAG apps
● Python and JavaScript Libraries
● Provides built in optimizations /
techniques for advanced RAG
HuggingFace is an ML community for hosting &
collaborating on models, datasets, and ML applications
● Latest open source LLMs are in HuggingFace
● + great learning resources / demos
https://huggingface.co/
Open Source vs Self Hosted vs SaaS option

7. © 2024 Cloudera, Inc. All rights reserved. 7
Enterprise Knowledge Base / Chatbot / Q&A
- Customer Support & Troubleshooting
- Enable open ended conversations with user
provided prompts
Code assistant:
- Provide relevant snippets of code as a
response to a request written in natural
language.
- Assist with creating test cases and
synthetic test data.
- Reference other relevant data such as a
company’s documentation to help provide
more accurate responses.
Social and emotional sensing
- Gauge emotions and opinions based on a
piece of text.
- Understand and deliver a more nuanced
message back based on sentiment.
ENTERPRISE WIDE USE CASES FOR AN LLM
Classification and Clustering
- Categorize and sort large volumes of data
into common themes and trends to support
more informed decision making.
Language Translation
- Globalize your content by feeding web
pages through LLMs for translation.
- Combine with chatbots to provide
multilingual support to your customer base.
Document Summarization
- Distill large amounts of text down to the
most relevant points.
Content Generation
- Provide detailed and contextually relevant
prompts to develop outlines, brainstorm
ideas and approaches for content.
L
Adoption dependent upon an Enterprise’s risk tolerance, restrictions, decision rights and disclosure obligations.

8. © 2024 Cloudera, Inc. All rights reserved.
NLP / AI / LLM
Generative AI

9. 9
Which Model and When?
Use the right model for right job: closed or open-source
Closed
Source
Usage can
easily scale
but so can
your costs
Rapidly
improving
AI models
Most
advanced
AI models
Excel at more
specialized
tasks
Great for a
wide range
of tasks
Open
Source
Better cost
planning
Compliance,
privacy, and
security risks
More control
over where &
how models
are deployed

10. 10
Adoption of Generative AI is a Journey
Identifying AI challenges in the enterprise
Data integration
barriers
● Streamlined access to
enterprise data
Rigid model
infrastructure
● Modularity
● Flexibility
● AI Ops
Lack of security
and transparency
● Model control
● Built-in security
● Visibility & governance
What’s missing
Challenges

11. 11
Data = Organization Context
Your data enables contextually accurate responses from LLMs
Large Language
Model
User Query
Contextually
Inaccurate
Response
Data
Organization
Context
User Query
Large Language
Model
Contextually
Accurate
Response

12. © 2024 Cloudera, Inc. All rights reserved. 12
CLOSED-SOURCE
FOUNDATION MODELS
MODEL HUBS
OPEN SOURCE
FOUNDATION MODELS
FINE-TUNED MODELS
PRIVATE
VECTOR STORE
MANAGED
VECTOR STORE
CLOUD INFRASTRUCTURE
Milvus, Solr*
Meta (Llama 2)
Applied Machine Learning Prototypes (AMPs)
Hugging Face
Pinecone
SPECIALIZED HARDWARE
APIs: OpenAI (GPT-4 Turbo)
Amazon Bedrock: Anthropic (Claude 2), Cohere…
DATA
WRANGLING
REAL-TIME
DATA INGEST
& ROUTING
AI MODEL
TRAINING &
INFERENCE
DATA STORE &
VISUALIZATION
Open Data Lakehouse
DATA
WRANGLING
REAL-TIME
DATA INGEST
& ROUTING
AI MODEL
TRAINING &
SERVING
DATA STORE &
VISUALIZATION
AI APPLICATIONS

13. Live Q&A
Travel Advisories
Weather Reports
Documents
Social Media
Databases
Transactions
Public Data Feeds
S3 / Files
Logs
ATM Data
Live Chat
…
ARCHITECTURE
INTERACT
COLLECT STORE
ENRICH, REPORT
Distribute
Collect
Report
REPORT
Visualize
Report, Automate
AI BASED ENHANCEMENTS
Predict, Automate
VECTOR DATABASE
LLM
Machine
Learning
Data
Visualization
Data Flow
Data
Warehouse
SQL
Stream Builder
Data
Visualization
Input Sentences
Generated Text
Timestamp
Input Sentence
Timestamps
Enrichments
Messaging
Broker
Real-time alerting
Real-time alerting
Aggregations

14. © 2024 Cloudera, Inc. All rights reserved.
DATAFLOW / STREAMING

15. LLM USE CASE
Vector DB
AI Model
Unstructured file types
Data in Motion
on Cloudera Data
Platform (CDP)
Capture, process &
distribute any data,
anywhere
Other enterprise data Open Data Lakehouse
Materialized Views
Structured Sources
Applications/API’s
Streams

16. https://medium.com/cloudera-inc/getting-ready-for-apache-nifi-2-0-5a5e6a67f450
NiFi 2.0.0 Features
● Python Integration
● Parameters
● JDK 21+
● JSON Flow Serialization
● Rules Engine for Development
Assistance
● Run Process Group as Stateless
● flow.json.gz
https://cwiki.apache.org/confluence/display/NIFI/NiFi+2.0+Release+Goals

17. 17
DataFlow Pipelines Can Help
External Context Ingest
Ingesting, routing, clean, enrich, transforming,
parsing, chunking and vectorizing structured,
unstructured, semistructured, binary data and
documents
Prompt engineering
Crafting and structuring queries to optimize
LLM responses
Context Retrieval
Enhancing LLM with external context such as
Retrieval Augmented Generation (RAG)
Roundtrip Interface
Act as a Discord, REST, Kafka, SQL, Slack bot to
roundtrip discussions

18. 18
UNSTRUCTURED DATA WITH NIFI
• Archives - tar, gzipped, zipped, …
• Images - PNG, JPG, GIF, BMP, …
• Documents - HTML, Markdown, RSS, PDF, Doc, RTF, Plain Text, …
• Videos - MP4, Clips, Mov, Youtube URL…
• Sound - MP3, …
• Social / Chat - Slack, Discord, Twitter, REST, Email, …
• Identify Mime Types, Chunk Documents, Store to Vector Database
• Parse Documents - HTML, Markdown, PDF, Word, Excel, Powerpoint

19. 19
CLOUD ML/DL/AI/Vector Database Services
• Cloudera ML
• Amazon Polly, Translate, Textract, Transcribe, Bedrock, …
• Hugging Face
• IBM Watson X.AI
• Vector Stores Anywhere: Weaviate, Pinecone, Milvus,
Chroma DB, SOLR, …

20. 20
https://medium.com/cloudera-inc/google-gemma-for-real-time-lightweight-open-llm-infe
rence-88efe98e580f

21. © 2024 Cloudera, Inc. All rights reserved.

22. © 2024 Cloudera, Inc. All rights reserved.
Python Processors

23. © 2024 Cloudera, Inc. All rights reserved.
Extract Company Names
● Python 3.10+
● Hugging Face, NLP, SpaCY, PyTorch
https://github.com/tspannhw/FLaNK-python-ExtractCompanyName-processor

24. © 2024 Cloudera, Inc. All rights reserved.
WatsonX SDK To Foundation
● Python 3.10+
● LLM
● WatsonX.AI Foundation Models
● Inference
● Secure
● Official SDK from IBM
https://github.com/tspannhw/FLaNK-python-watsonx-processor

25. © 2024 Cloudera, Inc. All rights reserved.
CaptionImage
● Python 3.10+
● Hugging Face
● Salesforce/blip-image-captioning-large
● Generate Captions for Images
● Adds captions to FlowFile Attributes
● Does not require download or copies of
your images
https://github.com/tspannhw/FLaNK-python-processors

26. © 2024 Cloudera, Inc. All rights reserved.
RESNetImageClassification
● Python 3.10+
● Hugging Face
● Transformers
● Pytorch
● Datasets
● microsoft/resnet-50
● Adds classification label to FlowFile
Attributes
● Does not require download or copies of
your images
https://github.com/tspannhw/FLaNK-python-processors

27. © 2024 Cloudera, Inc. All rights reserved.
NSFWImageDetection
● Python 3.10+
● Hugging Face
● Transformers
● Falconsai/nsfw_image_detection
● Adds normal and nsfw to FlowFile
Attributes
● Gives score on safety of image
● Does not require download or copies of
your images
https://github.com/tspannhw/FLaNK-python-processors

28. © 2024 Cloudera, Inc. All rights reserved.
FacialEmotionsImageDetection
● Python 3.10+
● Hugging Face
● Transformers
● facial_emotions_image_detection
● Image Classification
● Adds labels/scores to FlowFile Attributes
● Does not require download or copies of
your images
https://github.com/tspannhw/FLaNK-python-processors

29. © 2024 Cloudera, Inc. All rights reserved.
Other Python Processors
● Chunk Document, Parse Document
● Prompt Chat GPT
● Put Chroma, Query Chroma
● Put Pinecone, Query Pinecone

30. 30
DEMO

31. Startup Grind
AI Max
Summit -
April 12 NJ

32. 32
TH N Y U

33. CLOUDERA STREAM PROCESSING
Two Major Capabilities: Enterprise Messaging and Powerful Stream Processing
Enterprise grade messaging products for Apache
Kafka. Streams Messaging Manager to
monitor/operate clusters, Streams Replication
Manager for HA/DR deployments, Schema Registry for
centralized schema management, and support for
Kafka Connect and Cruise Control
Cloudera Streaming Analytics (CSA)
Powered By Apache Flink
Cloudera Streams Messaging (CSM)
Powered by Apache Kafka
Powered by Apache Flink with SQL StreamBuilder, it
provides low-latency stream processing capabilities
with advanced windowing & state management made
simple with SQL

34. ENTERPRISE MANAGEMENT CAPABILITIES FOR APACHE KAFKA
Extend streams messaging services for Schema Mgmt, Replication & Monitoring
Schema Registry
Kafka Schema Governance
Streams Replication Manager
Kafka Replication Service
for Disaster Recovery
Streams Messaging Manager
Management & Monitoring Service
for all of your Kafka clusters

35. Kafka Data Movement, Operations and Security Made Easier
ENTERPRISE MANAGEMENT CAPABILITIES FOR APACHE KAFKA
Kafka Connect Support
Simple Data Movement
Change Data Capture Connectors
Build Custom Connectors with NiFi
Ranger Security
Improved ACL and Audit for
Kafka, KConnect and Schema
Registry
Cruise Control Support
Intelligent Rebalancing
& Self-Healing of your
Kafka Clusters

36. CLOUDERA STREAM PROCESSING
Two Major Capabilities: Enterprise Messaging and Powerful Stream Processing
Enterprise grade messaging products for Apache
Kafka. Streams Messaging Manager to
monitor/operate clusters, Streams Replication
Manager for HA/DR deployments, Schema Registry for
centralized schema management, and support for
Kafka Connect and Cruise Control
Cloudera Streaming Analytics (CSA)
Powered By Apache Flink
Cloudera Streams Messaging (CSM)
Powered by Apache Kafka
Powered by Apache Flink with SQL StreamBuilder, it
provides low-latency stream processing capabilities
with advanced windowing & state management made
simple with SQL

37. NEXT GENERATION STREAMING ANALYTICS WITH APACHE
FLINK
Low latency stateful stream processing
● Flink is a distributed data processing
systems ideally suited for real-time, event
driven applications.
● Unifies stream and batch processing
● Advanced features - late arriving data,
checkpointing, event time processing,
Exactly Once Processing
Real-Time
Insights
Event
Processing
Low
Latency

38. SQL STREAM BUILDER (SSB)
SQL STREAM BUILDER allows
developers, analysts, and data
scientists to write streaming
applications with industry
standard SQL.
No Java or Scala code
development required.
Simplifies access to data in Kafka
& Flink. Connectors to batch data in
HDFS, Kudu, Hive, S3, JDBC, CDC
and more
Enrich streaming data with batch
data in a single tool
Democratize access to real-time data with just SQL

39. © 2024 Cloudera, Inc. All rights reserved. 39
LLMs ARE FOUNDATION MODELS
Base models that can be adapted for a wide range of use cases
Terabytes of Data
(Multiple Formats)
Foundation Models
(Billions of Parameters)
Train Adapt
Question/Answering
Sentiment Analysis
Doc summarization
… ++ more
➔ Historically, data scientists trained specialized models against narrow datasets to solve specific tasks.
➔ LLMs are Foundation models that can be adapted to perform a variety of tasks.
◆ It is faster to “adapt” a foundation model than it is to train a specialized model from scratch
◆ Decouples “knowledge” from “intelligence”
◆ Opens up AI use cases to software developers (instead of just specialised data scientists)