Cloudera Data Platform: Machine Learning

Cloudera Data Platform

Today, machine learning is one of the most important capabilities for modern firms to grow & stay competitive. ML models have pervaded practically every part of our work and personal life, from automating internal procedures to optimizing the design, creation, & marketing procedures behind almost every Cloudera Hortonworks product consumed.

Cloudera Hortonworks

Machine Learning is a web service that allows teams of data scientists to create self-service machine learning workstations and the underlying computing clusters.

Cloudera Hortonworks

Enterprise data sciences teams require access to enterprise data and the tools and computational resources needed to complete end-to-end computer vision workflows. In contrast, IT and the business must maintain governance and keep infrastructure costs under control. Cloudera Machine Learning combines the agility & economics of the Cloud service with the governed business tools that data science teams require, allowing them to run self-service machine learning processes from anywhere.

Iterative and sophisticated machine learning development is made even more difficult because most Cloudera Hortonworks machine learning tools aren’t meant to support the whole machine learning lifecycle. Cloudera Computer Vision on Cloudera Data Base reduces time-to-value by allowing data scientists to work in a single, all-encompassing platform for enabling any AI use case. Cloudera Machine Learning is purpose-built for rapid experimentation or production ML processes, and it handles it all from collected data to MLOps to predictive reporting. Solve mission-critical ML difficulties faster and more agilely across the full lifecycle to uncover opportunities that can make or break your company.

Each Cloudera Hortonworks ML workspace allows teams of computer scientists to create, test, train, and eventually deploy machine learning to improve the amount of applications using data from the enterprise data cloud. Through flexible and extendable engines, ML workspaces offer container-based processing of Python, R, Scala, or Spark workloads.

Core Competencies

Cloudera Hortonworks Machine Learning enables fully segregated and containerized workloads for scale-out data architecture for learning techniques with a seamlessly distributed problem-solving situation, supporting Python, R, and Spark-on-Kubernetes.

Data Scientists can use sessions to immediately access the workspace’s CPU, RAM, and GPU processing, although directly linked to the stored data lake.

Experiments allow Data Researchers to run several versions of training data workloads while keeping track of the outcomes required to train the right design feasible.

Modeling can be published in a few clicks, reducing any production barriers. They are provided as Restful endpoints with high availability and automatic lineage construction and metric tracking for map purposes.

Jobs can be used to coordinate a whole end-to-end automatic pipeline, including watching for model drift and triggering model retraining and re-deployment as appropriate.

In just a few clicks, applications provide interactive experiences for corporate users. These Apps may be built using frameworks like Flask and Shiny, and Cloudera Visualization Tool is also accessible as a point-and-click tool for creating these experiences.


Cloudera Artificial Learning is designed for the flexibility & power underlying cloud services but is not restricted to a single data source or provider. It’s a comprehensive platform for cooperatively building and deploying machine learning technologies at scale.

·       Each sort of user can profit from Cloudera Machine Learning.

·       Scientists that work with data

·       Transparent, secure, and controlled procedures enable DS teams to communicate and accelerate model creation and delivery.

·       Enhance AI use cases through automated machine learning pipelines and a fully integrated production machine learning toolbox.

End-to-end visibility & auditability of facts, processes, modeling, and dashboards enable speedier decision making and confidence.

Increase DS productivity by providing total visibility, safety, and governance over the ML lifecycle.

With a completely connected platform across the data lifecycle, you can eliminate bottlenecks and blindspots with the need to start moving data.

Self-service access with containerized ML workspaces let you accelerate AI by removing the heavy lifting and getting models into production faster.

·       Users in the Workplace

·       DS teams have created and launched interactive applications.

·       Predictive insights will enable you to make more informed business decisions.

With quick access to business data pipelines, scalable computational resources, and preferred tools, CDP Machine Learning provides enterprise data research teams to cooperate across the whole data lifecycle. Streamline the deployment of analytic workloads and intelligently manage deep learning use cases at scale across the organization.

With native or robust tools for creating, serving, and monitoring models, CDP Machine Learning optimizes ML workflows throughout your business. Govern and manage model cataloging using expanded SDX for models, and then seamlessly transport results to cooperate across CDP touchpoints, including Data Store and Operations Database.

Cloudera Machine Learning, Cloudera’s machine learning and artificial intelligence technology is now accessible on CDP Private Cloud. It combines big self-service data and data science in a single, portable package for inter-analytics on data everywhere as part of a business data cloud.

Computer science and Ai applications for business may now be built and deployed at scale, efficiently, and safely. Cloudera Deep Learning on Cloud Infrastructure is designed for cloud computing’s agility and power, yet it runs in your own secure and private data center.

Leave a Reply

Your email address will not be published. Required fields are marked *