Monday, April 12, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Big Data

Cloudera’s MLOps platform brings governance and management to data science pipelines

May 7, 2020
in Big Data
Cloudera’s MLOps platform brings governance and management to data science pipelines
585
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter

Cloudera took a big step forward with its Cloudera Machine Learning (CML) platform today. The company is introducing new operational management features for machine learning models and governance features for the data science pipelines that produce them. See ZDNet Editor in Chief Lawrence Dignan’s post for coverage of the news itself, and some really helpful analysis of how it positions Cloudera in the analytics market. To augment Dignan’s analysis, I’ll cover details on the machine learning operations (MLOps) features Cloudera is announcing today. And before doing so, I’ll explain why customers need them to begin with.

Wherefore MLOps?

To understand why MLOps is necessary, consider that machine learning models are actually software. Typically, the models are deployed as REST-based Web services and they go through a development process involving the authoring of code. In addition to the software development parallels, machine learning also involves the use and processing of data sets, just as BI and other descriptive analytics work does.

You might also like

Weaviate is an open-source search engine powered by ML, vectors, graphs, and GraphQL

MinIO simplifies onramps to do-it-yourself hybrid cloud object storage

Trifacta goes all in on the cloud

For these very reasons, machine learning work should be supported by the same kind of source code management, testing, versioning and automated deployment that other software has. Similarly, data science environments need data governance support, including cataloging and lineage tracking of machine learning models and their underlying data sets. Cloudera’s MLOps offering addresses both: model deployment and management features surface inside CML, while governance features show up in Cloudera’s Shared Data Experience (SDX) fabric.

Atlas embraced

The governance features come to SDX as enhancements announced by Cloudera in December to the open source Apache Atlas project. Though Atlas is an industry-wide standard, Cloudera is its chief backer and the project was founded by Hortonworks, which merged with Cloudera in a deal announced back in October, 2018. Cloudera Data Catalog also has a basis in Apache Atlas.

Also read:

Machine learning governance features in SDX include the aforementioned model cataloging and lineage capabilities. SDX also provides security infrastructure over the REST Web service interfaces erected around deployed models. 

Management and administration

Management features in CML include automated deployment support as well as a model monitoring service for tracking performance, accuracy and drift of the model overall. CML can also track individual predictions made by the model, and how well they correspond to “ground truth”, ensuring compliance and providing detailed context for assessing the model’s overall accuracy. To manage and ensure the interpretability of machine learning models, CML offers built-in functionality to generate SHAP and LIME-based model and prediction explanations.

On the development side, CML is based on template-based projects, which consist of associated source code files, development sessions (configurable Kubernetes containers), experiments, models and jobs. As those projects progress, developers can embed API calls to CML within their source code to log experiments and their associated metadata and metrics.

Open platform, hyper/multi cloud

In an advanced briefing with ZDNet, Cloudera explained that given the governance features’ basis in Apache Atlas, and CML being a component of the Cloudera Data Platform (CDP), Cloudera’s MLOps capabilities are in fact open standards which the company hopes will see adoption by other industry players. Moreover, since CDP supports, and SDX manages, deployments across private and (potentially multiple) public clouds, the CML environment is portable across target platforms too.

Also read: Cloudera Data Platform launches with multi/hybrid cloud savvy and mitigated Hadoop complexity

Cloudera explained to ZDNet that among its customers are organizations that have progressed well past the evaluation phase of machine learning work and have tens, hundreds or even thousands of models in production. Managing of these models on an ad hoc basis and lacking structured development tooling to produce them is simply unsustainable. Necessity being the proverbial mother of invention, Cloudera MLOps is the company’s concrete response to the needs of those customers. 

Let’s now see how Cloudera’s customer requirements-driven MLOps offering fares against pure play startup-produced MLOps  platforms from the likes of Datatron, Algorithmia and DotScience.

Cloudera is a customer of Brust’s advisory firm, Blue Badge Insights.

Credit: Zdnet

Previous Post

TIXnGO launches blockchain-based health certificates aiming to help ease lockdowns

Next Post

Facebook Launches 'Discover,' A Secure Proxy to Browse the Internet for Free

Related Posts

Weaviate is an open-source search engine powered by ML, vectors, graphs, and GraphQL
Big Data

Weaviate is an open-source search engine powered by ML, vectors, graphs, and GraphQL

April 8, 2021
MinIO simplifies onramps to do-it-yourself hybrid cloud object storage
Big Data

MinIO simplifies onramps to do-it-yourself hybrid cloud object storage

April 7, 2021
Trifacta goes all in on the cloud
Big Data

Trifacta goes all in on the cloud

April 6, 2021
Cloudera Data Platform hits Google Cloud
Big Data

Cloudera Data Platform hits Google Cloud

March 31, 2021
Cloudera fills gap in streaming platform with SQL
Big Data

Cloudera fills gap in streaming platform with SQL

March 31, 2021
Next Post
Facebook Launches ‘Discover,’ A Secure Proxy to Browse the Internet for Free

Facebook Launches 'Discover,' A Secure Proxy to Browse the Internet for Free

Leave a Reply Cancel reply

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

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

AI and Machine Learning Driven Contract Lifecycle Management for Government Contractors
Machine Learning

AI and Machine Learning Driven Contract Lifecycle Management for Government Contractors

April 12, 2021
Cambridge Quantum Computing Pioneers Quantum Machine Learning Methods for Reasoning
Machine Learning

Cambridge Quantum Computing Pioneers Quantum Machine Learning Methods for Reasoning

April 11, 2021
Why Machine Learning Over Artificial Intelligence?
Machine Learning

Why Machine Learning Over Artificial Intelligence?

April 11, 2021
27 million galaxy morphologies quantified and cataloged with the help of machine learning
Machine Learning

27 million galaxy morphologies quantified and cataloged with the help of machine learning

April 11, 2021
Machine learning and big data needed to learn the language of cancer and Alzheimer’s
Machine Learning

Machine learning and big data needed to learn the language of cancer and Alzheimer’s

April 11, 2021
Job Scope For MSBI In 2021
Data Science

Job Scope For MSBI In 2021

April 11, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • AI and Machine Learning Driven Contract Lifecycle Management for Government Contractors April 12, 2021
  • Cambridge Quantum Computing Pioneers Quantum Machine Learning Methods for Reasoning April 11, 2021
  • Why Machine Learning Over Artificial Intelligence? April 11, 2021
  • 27 million galaxy morphologies quantified and cataloged with the help of machine learning April 11, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates