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Why teach MLOps to your Data Science Teams?

DareData

In today's data-driven world, machine learning has emerged as a transformative force, empowering organizations to extract valuable insights from vast amounts of data. As the scope of the models and the data continues to scale, the role of a Data Scientist has evolved accordingly in the last years. But that is not all!

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ML Training and Deployment Pipeline Using Databricks

Ripple Engineering

This blog outlines Ripple’s general design and approach for machine learning model lifecycle management using Databricks. Tracking models and all associated data is helpful in tracking performance over time, backtesting experiments and A/B testing. Each cluster has a service account that has access to the requisite data.

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Cloudera Streaming Analytics 1.4: the unification of SQL batch and streaming

Cloudera

The team’s focus turned to bringing Flink Data Definition Language ( DDL) and the batch interface into SSB with that completed. For customers, this opens up massive new opportunities within the Cloudera stack to incorporate existing data footprints with streaming sources. makes building these data products a snap.

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Change The Way You Do ML With Applied ML Prototypes

Cloudera

Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. With almost all of the Fortune 500 and a majority of the Global 2000 relying on Cloudera for their most important data assets, Cloudera’s Machine Learning product (CML) is the way enterprises do ML.

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Data Engineering Annotated Monthly – June 2022

Big Data Tools

I’ll offer my impressions of recent developments in the data engineering space and highlight new ideas from the wider community. By the way, if you would prefer to get this monthly source of data engineering information delivered straight to your inbox each month, you can subscribe to the newsletter here.

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Data Engineering Annotated Monthly – June 2022

Big Data Tools

I’ll offer my impressions of recent developments in the data engineering space and highlight new ideas from the wider community. By the way, if you would prefer to get this monthly source of data engineering information delivered straight to your inbox each month, you can subscribe to the newsletter here.

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BERT NLP Model Explained for Complete Beginners

ProjectPro

Let’s break that statement down: Models are the output of an algorithm run on data, including the procedures used to make predictions on data. Self-attention ensures that as the model goes through each of the words sequentially in the training data, it looks at the input text for hints that can assist in encoding the word better.