Cloudera Machine Learning

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Cloudera Machine Learning

Cloudera Machine Learning (CML) is the future of business. It’s a powerful AI platform that helps businesses grow and stay competitive by providing an end-to-end machine learning platform for enterprises.

CML is fully integrated with Cloudera Data Platform (CDP) to provide a consistent experience with secure, shared business data across hybrid and multi-cloud environments.

CML enables easy onboarding of a new tenant and provision of an ML workspace in a shared Red Hat OpenShift Container Platform environment.

It also allows data scientists to access shared data on CDP Private Cloud Base and Cloudera Data Warehouse. CML leverages Spark-on-K8s to spin up and down Spark clusters on demand.

With CML, you can accelerate machine learning projects from research to production and manage the complete lifecycle.

CML provides –

 

  • A collaborative environment for data scientists to work on their projects,
  • Supports popular programming languages like Python and R,
  • And integrates with popular machine learning libraries and frameworks.

It also provides –

  • Capabilities for model monitoring,
  • Versioning,
  • And management,

Allowing organizations to track the performance of deployed models and iterate on their models as needed.

But that’s not all!

CML also leverages the security and governance features of the Cloudera Shared Data Experience (SDX), which is a unified framework for data security and governance in CDP.

Cloudera-SDX

SDX ensures –

 

  • data protection,
  • compliance,
  • And access control across different data sources and workloads, including machine learning projects.

It helps organizations enforce policies, manage data access, and maintain data lineage and auditability for machine learning workflows.

By integrating with the other components of CDP, CML enables organizations to build end-to-end machine learning pipelines that leverage –

  • the data management,
  • analytics,
  • and governance capabilities of the platform,

Providing a unified and comprehensive environment for machine learning tasks.

So, let’s dive into the details of Cloudera Machine Learning and how it can help businesses grow and stay competitive in today’s world.

Cloudera Machine Learning

Machine learning has become one of the most critical capabilities for modern businesses to grow and stay competitive today.

From automating internal processes to optimizing the design, creation, and marketing processes behind virtually every product consumed, ML models have permeated almost every aspect of our work and personal lives.

Cloudera Machine Learning on Cloudera Data Platform accelerates time-to-value by enabling data scientists to collaborate in a single unified platform that is all inclusive for powering any AI use case.

Purpose-built for agile experimentation and production ML workflows, Cloudera Machine Learning manages everything from data preparation to MLOps, to predictive reporting.

Solve mission critical ML challenges along the entire lifecycle with greater speed and agility to discover opportunities which can mean the difference for your business.

Each ML workspace enables teams of data scientists to –

  • Develop,
  • Test,
  • Train,
  • And ultimately deploy machine learning models

For building predictive applications all on the data under management within the enterprise data cloud.

ML workspaces support fully-containerized execution of –

  • Python,
  • R,
  • Scala,
  • and Spark

workloads through flexible and extensible engines.

Key Features -

  • 1. Containerized ML workspaces

    Data scientists shouldn't have to switch between tools to discover, query, and visualize data sets. CML offers all of these capabilities via the Data Discovery and Visualization feature, a single UI for all your exploratory data science needs

  • 2. SDX for training data & ML models

    With CDP Machine Learning, administrators and data science teams have full visibility from data source to production environment - enabling transparent workflows and easy collaboration across teams securely.

  • 3. Exploratory Data Science

    Data scientists shouldn't have to switch between tools to discover, query, and visualize data sets. CML offers all of these capabilities via the Data Discovery and Visualization feature, a single UI for all your exploratory data science needs.

  • 4. Applied ML Prototypes (AMPs)

    AMPs are ML projects that can be deployed with one click directly from Cloudera Machine Learning. AMPs enable data scientists to go from an idea to a fully working ML use case in a fraction of the time. It provides an end-to-end framework for building, deploying, and monitoring business-ready ML applications instantly.

  • 5. Complete MLOps toolset

    Cloudera Machine Learning’s MLOps capability enables one-click model deployment, model cataloguing, and granular prediction monitoring to keep models secure and accurate across production environments.

  • 6. Data visualization

    Deliver insights with a consistent and easy-to-use experience, featuring intuitive and accessible drag-and-drop dashboards and custom application creation.

Cloudera Machine Learning covers the end-to-end machine learning workflow, enabling fully isolated and containerized workloads – including Python, R, and Spark-on-Kubernetes – for scale-out data engineering and machine learning with seamless distributed dependency management.

Sessions enable Data Scientists to directly leverage the CPU, memory, and GPU compute available across the workspace, while also being directly connected to the data in the data lake.

Experiments enable Data Scientists to run multiple variations of model training workloads, tracking the results of each Experiment in order to train the best possible Model.

Models can be deployed in a matter of clicks, removing any roadblocks to production.

They serve as REST endpoints in a high availability manner, with automated lineage building and metric tracking for MLOps purposes.

Jobs can be used to plan an entire end-to-end automated pipeline, including monitoring for model drift and automatically kicking off model retraining and redeployment as needed. Applications deliver interactive experiences for business users in a matter of clicks.

Frameworks such as Flask and Shiny can be used in development of these Applications, while Cloudera Data Visualization is also available as a point-and-click interface for building these experiences.

Cloudera Machine Learning provides benefits for each type of user :

Data Scientists

can collaborate and speed up model development and delivery with transparent, secure, and governed workflows. They can expand AI use cases with automated ML pipelines and an integrated and complete production ML toolkit.

IT

can increase DS productivity with visibility, security, and governance of the complete ML lifecycle.

Business Users

can access interactive Applications built and deployed by DS teams. 

So, Cloudera Machine Learning is a comprehensive platform that enables enterprises to collaboratively build and deploy machine learning capabilities at scale.

Check out Frequently Asked Question Below:

Cloudera Machine Learning (CML) makes it easy to bring Data Scientists to the data platform and makes it easier to overcome the challenges they face on a day to day basis.

The Cloudera Data Platform CDP is a platform that allows companies to analyses data with self-service analytics in hybrid and multi-cloud environments. It allows organizations to get more value out of all their data by creating data lakes in the cloud in a matter of hours.

Cloudera Machine Learning (CML) leverages Spark-on-K8s, enabling data scientists to directly manage the resources and dependencies necessary for the Spark cluster. Once the workload is completed, the Spark executors are spun down to free up the resources for other uses.

In Short -

Cloudera Machine Learning (CML) plays a crucial role for businesses seeking success in today’s data-centric environment.

Seamlessly integrated with the Cloudera Data Platform (CDP), CML offers a comprehensive solution to accelerate enterprise machine learning initiatives.

By providing an end-to-end platform, CML fosters collaboration among data scientists, supports popular programming languages and frameworks, and streamlines the entire machine learning lifecycle from research to production.

Additionally, leveraging the security and governance features of the Cloudera Shared Data Experience (SDX), CML ensures data protection, compliance, and access control across diverse data sources and workloads.

With features like containerized ML workspaces, exploratory data science capabilities, Applied ML Prototypes (AMPs), and a complete MLOps toolset, CML empowers users to efficiently build and deploy ML models.

It caters to the needs of data scientists, IT professionals, and business users, providing transparent, secure, and governed workflows that drive innovation and competitive advantage.

Ultimately, Cloudera Machine Learning emerges as a transformative platform, enabling enterprises to harness the power of AI and machine learning at scale.

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