DevOps.com

  • Latest
    • Articles
    • Features
    • Most Read
    • News
    • News Releases
  • Topics
    • AI
    • Continuous Delivery
    • Continuous Testing
    • Cloud
    • Culture
    • DevSecOps
    • Enterprise DevOps
    • Leadership Suite
    • DevOps Practice
    • ROELBOB
    • DevOps Toolbox
    • IT as Code
  • Videos/Podcasts
    • DevOps Chats
    • DevOps Unbound
  • Webinars
    • Upcoming
    • On-Demand Webinars
  • Library
  • Events
    • Upcoming Events
    • On-Demand Events
  • Sponsored Communities
    • AWS Community Hub
    • CloudBees
    • IT as Code
    • Rocket on DevOps.com
    • Traceable on DevOps.com
    • Quali on DevOps.com
  • Related Sites
    • Techstrong Group
    • Container Journal
    • Security Boulevard
    • Techstrong Research
    • DevOps Chat
    • DevOps Dozen
    • DevOps TV
    • Digital Anarchist
  • Media Kit
  • About
  • AI
  • Cloud
  • Continuous Delivery
  • Continuous Testing
  • DevSecOps
  • Leadership Suite
  • Practices
  • ROELBOB
  • Low-Code/No-Code
  • IT as Code
  • More
    • Application Performance Management/Monitoring
    • Culture
    • Enterprise DevOps

Home » Blogs » AI » With Platform, Dotscience Applies DevOps to AI

With Platform, Dotscience Applies DevOps to AI

By: Mike Vizard on July 30, 2019 Leave a Comment

Dotscience today launched a namesake platform for building and deploying artificial intelligence (AI) models based on a set of best DevOps practices.

Recent Posts By Mike Vizard
  • Survey Reveals High Cost of Application Modernization
  • Salesforce Adds RPA Bots to MuleSoft Integration Platform
  • GitLab To Embed Observability in CI/CD Platform
More from Mike Vizard
Related Posts
  • With Platform, Dotscience Applies DevOps to AI
  • Accenture Aims to Infuse AI into App Testing
  • Cloudera Calls for MLOps Standards Initiative
    Related Categories
  • AI
  • Blogs
    Related Topics
  • ai
  • AI models
  • artificial intelligence
  • devops
  • Dotscience
Show more
Show less

Company CEO Luke Marsden said that as organizations realize that AI models need to be trained and updated continuously, the need for a DevOps platform that accelerates that process will become more apparent. Today most AI models are trained over an extended amount of time and then deployed within an application environment. Over time, however, either more data becomes available or organizations determine the machine and deep learning algorithms originally used to create the AI model need to be updated or replaced. Whatever the underlying reason for replacing an AI model, a platform that addresses all the aspects of the AI model life cycle, including testing, reproducibility, accountability, collaboration and continuous delivery, is required, he said.

DevOps Connect:DevSecOps @ RSAC 2022

To address those requirements, the Dotscience platform facilitates concurrent collaboration across developer and operations teams, version control of the model creation process, tracking of provenance records in real-time, exploring and optimizing hyper-parameters when training a model and tracking workflows across multiple open source tools.

Most of the teams that build AI models have backgrounds in data science versus application development. As such, Marsden noted most of them have had little to no exposure to DevOps practices. In that absence of those processes, teams building AI models now routinely encounter issues such as having to navigate siloed data and technical debt, which all conspire to extend the time required to build AI model, Marsden said. In addition, teams building AI models need to keep track of not only versions, but also runs of their code that tie together input data with models and corresponding hyperparameters and metrics, he added.

Dotscience DevOps AI platform

In the absence of a DevOps platform such as Dotscience, it becomes challenging for organizations to document what changes were made to an AI model when, Marsden said. That governance issue has become especially problematic when it comes to AI models because organizations are coming under increased regulatory pressure to document how the AI models they are employing are built and updated.

Dotscience’s “The State of Development and Operations of AI Applications” report also published today identifies the top three challenges with AI workloads are duplicating work (33%), rewriting a model after a team member leaves (27.6%) and justifying its value (27%). Based on a survey of 500 industry professionals, the report also finds 52% of respondents track provenance manually using tools such as spreadsheets, while 27% don’t track provenance at all but think it is important. In total, the survey finds 63% of businesses report they are spending between $500,000 and $10 million on their AI efforts.

As more organizations rely on DevOps processes to build and deploy applications, there’s no doubt that teams building AI models that need to be inserted into those applications will have to fall in line with best DevOps practices. That challenge now is finding a way to extend those DevOps processes all the way back to the building of the AI models themselves. Only then is the AI model building and deployment process likely to become agile enough to stay relevant to the pace of change now occurring across digital business processes.

— Mike Vizard

Filed Under: AI, Blogs Tagged With: ai, AI models, artificial intelligence, devops, Dotscience

Sponsored Content
Featured eBook
The 101 of Continuous Software Delivery

The 101 of Continuous Software Delivery

Now, more than ever, companies who rapidly react to changing market conditions and customer behavior will have a competitive edge.  Innovation-driven response is successful not only when a company has new ideas, but also when the software needed to implement them is delivered quickly. Companies who have weathered recent events ... Read More
« 6 Reasons to Convince Your Manager to Send You to DevOps World |Jenkins World
Aqua Security Introduces Native Runtime Protection for Pivotal Cloud Foundry »

TechStrong TV – Live

Click full-screen to enable volume control
Watch latest episodes and shows

Upcoming Webinars

Boost Your Java/JavaScript Skills With a Multi-Experience Platform
Wednesday, June 29, 2022 - 3:30 pm EDT
Closing the Gap: Reducing Enterprise AppSec Risks Without Disrupting Deadlines
Thursday, June 30, 2022 - 11:00 am EDT
Automating the Observer: Lessons From 1,000+ Incidents
Thursday, June 30, 2022 - 1:00 pm EDT

Latest from DevOps.com

Common RDS Misconfigurations DevSecOps Teams Should Know
June 29, 2022 | Gad Rosenthal
Quick! Define DevSecOps: Let’s Call it Development Security
June 29, 2022 | Don Macvittie
Chip-to-Cloud IoT: A Step Toward Web3
June 28, 2022 | Nahla Davies
DevOps Connect: DevSecOps — Building a Modern Cybersecurity Practice
June 27, 2022 | Veronica Haggar
What Is User Acceptance Testing and Why Is it so Important?
June 27, 2022 | Ron Stefanski

Get The Top Stories of the Week

  • View DevOps.com Privacy Policy
  • This field is for validation purposes and should be left unchanged.

Download Free eBook

The 101 of Continuous Software Delivery
New call-to-action

Most Read on DevOps.com

The Age of Software Supply Chain Disruption
June 23, 2022 | Bill Doerrfeld
Cloudflare Outage Outrage | Yet More FAA 5G Stupidity
June 23, 2022 | Richi Jennings
Developer’s Guide to Web Application Security
June 24, 2022 | Anas Baig
What Is User Acceptance Testing and Why Is it so Important?
June 27, 2022 | Ron Stefanski
DevOps Connect: DevSecOps — Building a Modern Cybersecurity ...
June 27, 2022 | Veronica Haggar

On-Demand Webinars

DevOps.com Webinar ReplaysDevOps.com Webinar Replays
  • Home
  • About DevOps.com
  • Meet our Authors
  • Write for DevOps.com
  • Media Kit
  • Sponsor Info
  • Copyright
  • TOS
  • Privacy Policy

Powered by Techstrong Group, Inc.

© 2022 ·Techstrong Group, Inc.All rights reserved.