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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. And this blog will focus on Predictive Analytics. Data Collection – streaming data. Data Enrichment – data engineering. The ML Solution.

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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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Happy Birthday, CDP Public Cloud

Cloudera

In the beginning, CDP ran only on AWS with a set of services that supported a handful of use cases and workload types: CDP Data Warehouse: a kubernetes-based service that allows business analysts to deploy data warehouses with secure, self-service access to enterprise data. Predict – Data Engineering (Apache Spark).

Cloud 94
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DataOps Uncovered: A Bold New Approach to Telemetry and Network Visibility

Kentik

Data scientists play a critical role in the DataOps ecosystem, leveraging advanced analytics and machine learning techniques to gain insights from large and complex data sets. DataOps team roles In a DataOps team, several key roles work together to ensure the data pipeline is efficient, reliable, and scalable.

Network 52
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The Good and the Bad of Databricks Lakehouse Platform

Altexsoft

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. Besides that, it’s fully compatible with various data ingestion and ETL tools. How data engineering works in 14 minutes.

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Forget the Rules, Listen to the Data

Hu's Place - HitachiVantara

Rule-based fraud detection software is being replaced or augmented by machine-learning algorithms that do a better job of recognizing fraud patterns that can be correlated across several data sources. DataOps is required to engineer and prepare the data so that the machine learning algorithms can be efficient and effective.

Data 90
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Data Innovation Summit with Gema Parreño – lead data scientist at Apiumhub

Apiumhub

We are super excited to participate in the biggest and the most influential Data, AI and Advanced Analytics event in the Nordics! Data Innovation Summit ! There our Gema Parreño – Data Science expert at Apiumhub gives a talk about Alignment of Language Agents for serious video games.