Cloudera

AUGUST 24, 2022

Cloudera Machine Learning (CML) is a cloud-native and hybrid-friendly machine learning platform. CML empowers organizations to build and deploy machine learning and AI capabilities for business at scale, efficiently and securely, anywhere they want. A machine with Docker tools installed. docker.io).

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Knowledge Hut

NOVEMBER 19, 2023

Additionally, with the rise of machine learning models, programming robots to identify patterns and effectively apply what they learn has been a revolutionary breakthrough. This has given rise to machine learning for robotics, thus creating lucrative career options for candidates belonging to data science or computer science.

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Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI.

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Knowledge Hut

NOVEMBER 18, 2023

With the advent of Internet of Things (IoT), companies can easily gain access to large volumes of customer data on a regular basis. Thus, organizations are actively implementing machine learning for IoT models in order to fulfill this need. You will get a comprehensive idea of the use cases of ML models in IoT applications.

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Cloudera

DECEMBER 8, 2020

Two years ago we wrote a research report about Federated Learning. You can read it online here: Federated Learning. Federated Learning is a paradigm in which machine learning models are trained on decentralized data. However, it is an important tool in the private machine learning toolkit.

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Cloudera

NOVEMBER 24, 2020

With the complexity of data growing across the enterprise and emerging approaches to machine learning and AI use cases, data scientists and machine learning engineers have needed more versatile and efficient ways of enabling data access, faster processing, and better, more customizable resource management across their machine learning projects.

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Cloudera

APRIL 27, 2023

In the blog we will cover how Ray can be used in Cloudera Machine Learning’s open-by-design architecture to bring fast distributed AI compute to CDP. Now we can spin up a Ray cluster in Cloudera Machine Learning. This is enabled through a Ray Module in cmlextensions python package published by our team.

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Cloudera

OCTOBER 26, 2022

?. It’s no secret that advancements like AI and machine learning (ML) can have a major impact on business operations. Cloudera has seen a lot of opportunity to extend even more time saving benefits specifically to data scientists with the debut of Applied Machine Learning Prototypes (AMPs). The answer is a resounding no.

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Cloudera

APRIL 13, 2021

You’ve probably heard it more than once: Machine learning (ML) can take your digital transformation to another level. We recently published a Cloudera Special Edition of Production Machine Learning For Dummies eBook. The post 10 Steps to Achieve Enterprise Machine Learning Success appeared first on Cloudera Blog.

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Snowflake

JUNE 1, 2023

For more than 10 years, Snowflake has been dedicated to bringing the work—pipelines, machine learning (ML) models, and apps—to your governed data. You can get started now using Streamlit open source , and stay tuned for access to the native Snowflake-Streamlit integration. What’s next?

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U-Next

MARCH 14, 2023

According to The Times, “ The group of young cyberspace entrepreneurs celebrated what appears to have been the first transaction in retail on the Internet using a readily accessible version of strong data encryption software designed to ensure anonymity. Easy Automation Online shops are now accessible 24/7 on various channels.

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Cloudera

NOVEMBER 19, 2021

We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. Continuous Operations for Production Machine Learning (COPML) helps companies think about the entire life cycle of an ML model.

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ProjectPro

JANUARY 31, 2023

Ever wondered how insurance companies successfully implement machine learning to expand their businesses? With the introduction of advanced machine learning algorithms , underwriters are bringing in more data for better risk management and providing premium pricing targeted to the customer.

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ProjectPro

JANUARY 31, 2023

If you are a machine learning engineer or a data scientist, learning how to save a machine learning model is the one of the most crucial steps for you to reuse the model without having to train it from scratch. Let’s learn how to save deep learning models in the python programming language.

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Cloudera

JANUARY 6, 2021

Python is used extensively among Data Engineers and Data Scientists to solve all sorts of problems from ETL/ELT pipelines to building machine learning models. Apache HBase is an effective data storage system for many workflows but accessing this data specifically through Python can be a struggle. Example Operations .

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Knowledge Hut

JULY 28, 2023

These labels provide context and meaning to the data, enabling machine learning algorithms to learn and make predictions. If you are new to this domain and wanted to learn how to label data for machine learning problems, then you’ve landed on the right page. What is Data Labeling for Machine Learning?

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Striim

NOVEMBER 17, 2023

In today’s data-driven world, the ability to leverage real-time data for machine learning applications is a game-changer. Meanwhile, Google BigQuery ML is a machine learning service provided by Google Cloud, allowing you to create and deploy machine learning models using SQL-like syntax directly within the BigQuery environment.

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Cloudera

AUGUST 17, 2021

Dear Parents and Educators and Friends of Cloudera, If you are reading this blog, you know us at Cloudera as a group of self-described data geeks and data analysts. Our company mission is to make data and analytics easy and accessible, for everyone. However, data doesn’t just make a difference for enterprises. And we mean it.

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Cloudera

APRIL 19, 2021

In our previous blog post in this series , we explored the benefits of using GPUs for data science workflows, and demonstrated how to set up sessions in Cloudera Machine Learning (CML) to access NVIDIA GPUs for accelerating Machine Learning Projects. Introduction.

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ProjectPro

FEBRUARY 17, 2022

Similarly, in Machine learning, selecting the correct number of features for fitting a predictive model to a given dataset is essential. Table of Contents Feature Selection Methods in Machine Learning What is Feature Selection in Machine Learning? Use various feature selection techniques in machine learning.

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Confluent

FEBRUARY 6, 2019

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. Therefore, the majority of machine learning/deep learning frameworks focus on Python APIs.

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DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps is a term that refers to the set of practices and tools that organizations use to improve the quality and speed of data analytics and machine learning. This can help organizations to build trust in their data-related workflows, and to drive better outcomes from their data analytics and machine learning initiatives.

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Cloudera

SEPTEMBER 10, 2020

Cloudera has been named a Leader in The Forrester Wave : Notebook-Based Predictive Analytics and Machine Learning, Q3 2020. For enterprise machine learning teams, this means having the right platform, tools, and processes that streamline end-to-end ML to tackle once-impossible business challenges effectively and at scale.

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Precisely

DECEMBER 20, 2022

Let’s further explore the impact of data in this industry as we count down the top 5 financial services blog posts of 2022. #5 Many are turning to machine learning as an alternative to manual workloads and a long-term solution to anti-money laundering compliance. But what does that look like in practice?

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Cloudera

APRIL 10, 2021

When working on complex, or rigorous enterprise machine learning projects, Data Scientists and Machine Learning Engineers experience various degrees of processing lag training models at scale. To overcome this, practitioners often turn to NVIDIA GPUs to accelerate machine learning and deep learning workloads. .

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Rockset

APRIL 18, 2023

Latency matters in machine learning applications. Real-time machine learning (ML) involves deploying and maintaining machine learning models to perform on-demand predictions for use cases like product recommendations, ETA forecasting, fraud detection and more.

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ProjectPro

JUNE 22, 2021

This blog will help you master the fundamentals of classification machine learning algorithms with their pros and cons. You will also explore some exciting machine learning project ideas that implement different types of classification algorithms. So, without much ado, let's dive in.

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Cloudera

JANUARY 20, 2021

For more context, this demo is based on concepts discussed in this blog post How to deploy ML models to production. Machine learning is now being used to solve many real-time problems. For data already existing in HBase, PySpark allows for easy access and processing with any use-case. . Background / Overview.

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ProjectPro

AUGUST 25, 2021

This blog introduces the critical differences that one encounters when anyone performs an analysis of logistic regression vs linear regression. Firstly, we introduce the two machine learning algorithms in detail and then move on to their practical applications to answer questions like when to use linear regression vs logistic regression.

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ProjectPro

SEPTEMBER 29, 2021

The term artificial intelligence is always synonymously used Awith complex terms like Machine learning, Natural Language Processing, and Deep Learning that are intricately woven with each other. One of the trending debates is that of the differences between natural language processing and machine learning.

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ProjectPro

OCTOBER 28, 2021

In this blog, we have mentioned all the topics that are considered as prerequisites for learning machine learning. We have covered all the subjects and the best resources that will help you learn them thoroughly. Machine learning is no exception to that. Why should you learn Machine learning?

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Data Engineering Podcast

APRIL 21, 2024

Contact Info LinkedIn Blog Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? The Machine Learning Podcast helps you go from idea to production with machine learning. Closing Announcements Thank you for listening!

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ProjectPro

JANUARY 11, 2022

“Machine Learning” is one of the most trending buzzwords. They are built using Machine Learning algorithms. Table of Contents Machine Learning Classification vs. Regression What is Classification? Yes, you are right. What is Classification? Let’s understand each of them with examples.

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ProjectPro

OCTOBER 16, 2021

TensorFlow and Scikit-learn, two of the most popular words from the jargon of the Machine Learning world! If you are wondering what is the reason behind their popularity, continue reading as we answer that question in this blog by exploring hands-on machine learning with Scikit-learn and TensorFlow.

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ProjectPro

SEPTEMBER 29, 2021

Probability and Statistics are two intertwined topics that smoothen one’s path to becoming a Machine Learning pro. In this blog, you will find a detailed description of all you need to learn about probability and statistics for machine learning. How to become good at Statistics for Machine Learning?

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ProjectPro

NOVEMBER 25, 2021

To mitigate this challenge, organizations are now leveraging natural language processing and machine learning techniques to extract meaningful insights from unstructured text data. Read this blog to learn about text classification, one of the core topics of natural language processing.

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ProjectPro

SEPTEMBER 6, 2021

The Machine Learning market is anticipated to be worth $30.6 Machine Learning plays a vital role in the design and development of such solutions. Machine learning is everywhere. Machine Learning has a wide range of use cases and applications in this area. Billion in 2024.

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