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New Applied ML Prototypes Now Available in Cloudera Machine Learning

Cloudera

You know the one, the mathematician / statistician / computer scientist / data engineer / industry expert. Some companies are starting to segregate the responsibilities of the unicorn data scientist into multiple roles (data engineer, ML engineer, ML architect, visualization developer, etc.),

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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Impedance mismatch between data scientists, data engineers and production engineers.

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Next Stop – Predicting on Data with Cloudera Machine Learning

Cloudera

The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. Data Collection – streaming data.

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Building a Machine Learning Application With Cloudera Data Science Workbench And Operational Database, Part 3: Productionization of ML models

Cloudera

In this last installment, we’ll discuss a demo application that uses PySpark.ML to make a classification model based off of training data stored in both Cloudera’s Operational Database (powered by Apache HBase) and Apache HDFS. Machine learning is now being used to solve many real-time problems. Background / Overview.

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Simplify your workflow deployment with Databricks Asset Bundles: Part I

Xebia

Databricks is now a top choice for data teams. Its user-friendly, collaborative platform simplifies building data pipelines and machine learning models. Many data practitioners, myself included, have faced various deployment and resource management strategies. You must build a data ingestion app.

Resources 130
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What you need to know about product management for AI

O'Reilly Media - Ideas

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. Machine learning adds uncertainty.

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AWS App Studio introduces a prebuilt solutions catalog and cross-instance Import and Export

AWS Machine Learning - AI

With App Studio, technical professionals such as IT project managers, data engineers, enterprise architects, and solution architects can quickly develop applications tailored to their organizations needswithout requiring deep software development skills.

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