Why would a data scientist use **Kafka Jupyter Python KSQL TensorFlow** all together in a single notebook?

There is an **impedance mismatch between model development using Python and its Machine Learning tool stack and a scalable, reliable data platform**. The former is what you need for quick and easy prototyping to build analytic models. The latter is what you need to use for data ingestion, preprocessing, model deployment and monitoring at scale. It…

Added by Kai Waehner on January 22, 2019 at 10:00am — No Comments

I built a scenario for a hybrid machine learning infrastructure leveraging Apache Kafka as scalable central nervous system. The public cloud is used for training analytic models at extreme scale (e.g. using TensorFlow and TPUs on Google Cloud Platform (GCP) via Google ML Engine. The predictions (i.e.…

ContinueAdded by Kai Waehner on August 1, 2018 at 11:00pm — 1 Comment

Machine Learning / Deep Learning models can be used in different ways to do predictions. My preferred way is to deploy an analytic model directly into a stream processing application (like Kafka Streams or KSQL). You could e.g. use the …

ContinueAdded by Kai Waehner on July 8, 2018 at 4:26pm — No Comments

I had a new talk presented at "Codemotion Amsterdam 2018" this week. I discussed the relation of Apache Kafka and Machine Learning to build a Machine Learning infrastructure for extreme scale.

Long version of the title:

"**Deep Learning at Extreme Scale (in the Cloud)
with the Apache Kafka Open Source Ecosystem - How to Build a Machine Learning Infrastructure with Kafka, Connect, Streams, KSQL, etc.**"

As always, I want to share the slide deck. The talk was…

ContinueAdded by Kai Waehner on May 8, 2018 at 9:30pm — No Comments

Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.

Deep Learning is the modern buzzword for artificial neural networks, one of many concepts…

ContinueAdded by Kai Waehner on April 23, 2017 at 9:00am — 1 Comment

**Visual Analytics and Data Discovery** allow analysis of big data sets to **find insights and valuable information**. This is much more than just classical Business Intelligence (BI). See this article for more details and motivation: "Using Visual Analytics to Make Better Decisions: the Death Pill Example". Let's take a look at important characteristics to choose the right tool for…

Added by Kai Waehner on July 27, 2016 at 10:00pm — No Comments

- Apache Kafka + KSQL + TensorFlow for Data Scientists via Python + Jupyter Notebook
- Scalable IoT ML Platform with Apache Kafka + Deep Learning + MQTT
- Model Serving: Stream Processing vs. RPC / REST - A Deep Learning Example with TensorFlow and Kafka
- Deep Learning Infrastructure for Extreme Scale with the Apache Kafka Open Source Ecosystem
- Open Source Deep Learning Frameworks and Visual Analytics
- Characteristics of Good Visual Analytics and Data Discovery Tools

- Open Source Deep Learning Frameworks and Visual Analytics
- Scalable IoT ML Platform with Apache Kafka + Deep Learning + MQTT
- Apache Kafka + KSQL + TensorFlow for Data Scientists via Python + Jupyter Notebook
- Model Serving: Stream Processing vs. RPC / REST - A Deep Learning Example with TensorFlow and Kafka
- Characteristics of Good Visual Analytics and Data Discovery Tools
- Deep Learning Infrastructure for Extreme Scale with the Apache Kafka Open Source Ecosystem

- TensorFlow (5)
- Apache (5)
- Kafka (4)
- Learning (4)
- Machine (4)
- Open (4)
- Source (4)
- MXNet (3)
- Python (3)
- AWS (3)
- Cloud (3)
- Confluent (3)
- Data (3)
- Deep (3)
- GCP (3)
- KSQL (2)
- Keras (2)
- Processing (2)
- R (2)
- Stream (2)
- Streaming (2)
- Streams (2)
- Amazon (2)
- Analytics (2)
- Azure (2)
- Big (2)
- Google (2)
- H2O (2)
- H2Oai (2)
- Java (1)
- Jupyter (1)
- KNIME (1)
- Kubernetes (1)
- MATLAB (1)
- MQTT (1)
- Mesos (1)
- Microservice (1)
- Microservices (1)
- Microsoft (1)
- Migration (1)
- Multi (1)
- Network (1)
- Neural (1)
- Proxy (1)
- Qlik (1)
- RCP (1)
- REST (1)
- Real (1)
- Recovery (1)
- Registry (1)
- Replication (1)
- Request (1)
- Response (1)
- SAS (1)
- SaaS (1)
- Scalability (1)
- Schema (1)
- Serving (1)
- Spark (1)
- Spotfire (1)
- TIBCO (1)
- Tableau (1)
- Theano (1)
- Things (1)
- Time (1)
- Torch (1)
- Advanced (1)
- Visual (1)
- analytics (1)
- big data (1)
- data preparation (1)
- Architecture (1)
- data science (1)
- AutoML (1)
- data wrangling (1)
- Autoencoder (1)
- deep learning (1)
- gRPC (1)
- machine learning (1)
- CEP (1)
- of (1)
- Caffee (1)
- open (1)
- Center (1)
- open source (1)
- source (1)
- visual analytics (1)
- Connect (1)
- DCOS (1)
- DL4J (1)
- DeepLearning4j (1)
- Deeplearning4j (1)
- Disaster (1)
- Discovery (1)
- Event (1)
- Github (1)
- H2O.ai (1)
- HTTP (1)
- Hybrid (1)
- Internet (1)

© 2020 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions