Skip to content
Tiatra, LLCTiatra, LLC
Tiatra, LLC
Information Technology Solutions for Washington, DC Government Agencies
  • Home
  • About Us
  • Services
    • IT Engineering and Support
    • Software Development
    • Information Assurance and Testing
    • Project and Program Management
  • Clients & Partners
  • Careers
  • News
  • Contact
 
  • Home
  • About Us
  • Services
    • IT Engineering and Support
    • Software Development
    • Information Assurance and Testing
    • Project and Program Management
  • Clients & Partners
  • Careers
  • News
  • Contact

How MLOps Is Helping Overcome Machine Learning’s Biggest Challenges

Enterprises are betting big on machine learning (ML). According to IDC, 85% of the world’s largest organizations will be using artificial intelligence (AI) — including machine learning (ML), natural language processing (NLP) and pattern recognition — by 2026.

And a survey conducted by ESG found, “62% of organizations plan to increase their year-over-year spend on AI, including investments in people, process, and technology.”

But despite all the money flowing into ML projects, most organizations are struggling to get their ML models and applications working on production systems. 

The market researchers at Gartner say that “Only half of AI projects make it from pilot into production, and those that do take an average of nine months to do so.”

IDC’s numbers look even worse, with only 31% of enterprises surveyed saying that they have AI functioning in production. In addition, “Of the 31% with AI in production, only one third claim to have reached a mature state of adoption wherein the entire organization benefits from an enterprise-wide AI strategy.”

And another recent survey has the worst numbers of all, finding that 90% of ML models are not deployed to production.

So what’s the problem? Why are so many enterprises finding it difficult to realize their ML goals? 

The problem with ML

Industry watchers suggest that enterprise struggles with ML boil down to two key factors: processes and infrastructure.

On the process side, most ML projects require the integration of multiple teams and systems. An Omdia report notes, “Successful enterprise ML at scale demands the careful orchestration of a complex tapestry made up of people, processes, and platforms, an effort that does not end when an ML solution goes live but instead continues for the life of the solution.” 

Many enterprises do not yet have repeatable processes in place to address these needs. As a result, data scientists often spend too much time on IT operations tasks, like figuring out how to allocate computing resources, rather than actually creating and training data science models.

These problems are exacerbated by a lack of hardware designed for ML use cases. Gartner reports, “86% of organizations identified at least one of the following areas as a weak link in their AI infrastructure stack: GPU processing, CPU processing, data storage, networking, resource sharing, or integrated development environments.”

IDC agrees. “IDC research consistently shows that inadequate or lack of purpose-built infrastructure capabilities are often the cause of AI projects failing,” said Peter Rutten, IDC research vice president and global research lead on Performance Intensive Computing Solutions.

The promise of MLOps

So how can enterprises overcome these challenges? A partial solution lies in the adoption of MLOps.

At its simplest, MLOps is defined as applying the principles of the DevOps movement to machine learning. Cnvrg.io, which has built ready-to-use open source ML pipelines that can run on any infrastructure, explains that MLOps “reduces friction and bottlenecks between ML development teams and engineering teams in order to operationalize models.” It adds, “It is a discipline that seeks to systematize the entire ML lifecycle.”

The approach works. Organizations that have implemented MLOps report up to a 10x increase in productivity, 5x faster model training, and up to a 50% increase in compute utilization according to cnvrg.io research.

It should be no wonder then that IDC predicts, “By 2024, 60% of enterprises will have operationalized their ML workflows through MLOps/ModelOps capabilities and AI-infused their IT Infrastructure operations through AIOps capabilities.”

Infrastructure designed for MLOps

But MLOps is only part of the answer. Enterprises also need infrastructure designed to meet ML needs and, more specifically, to meet the needs MLOps. With that in mind, Dell Technologies recently rolled out its Dell Validated Design for AI, built in collaboration with cnvrg.io. 

It addresses the need for fast compute with VxRail HCI V670 or PowerEdge R750a servers. The Dell design augments the CPUs with industry-leading NVIDIA A100 or A30 GPUs. PowerSwitch 25GbE S5248F‑ON or NVIDIA® Spectrum® SN3700 and out‑of‑band PowerSwitch S4148T‑ON — provide the speed and bandwidth necessary for MLOps. And PowerScale F600 or H600 provides highly scalable storage. Tying it all together is cnrg.io’s MLOps stack, VMware Tanzu, and NVIDIA AI Enterprise software.

Dell infrastructure is also part of Intel’s cnvrg.io Metacloud, giving AI developers the flexibility to run, test and deploy AI and ML workloads on mixed hardware within the same AI/ML workflow or pipeline. Metacloud leverages cloud-native technologies such as containers and Kubernetes, which enables developers to quickly and easily select infrastructure located on-premises, co-located and in any public cloud and run the workload.

With the right processes and infrastructure, enterprises can overcome the challenges inherent in ML at scale and begin to accomplish the goals of their machine learning projects.

***

Intel® Technologies Move Analytics Forward

Data analytics is the key to unlocking the most value you can extract from data across your organization. To create a productive, cost-effective analytics strategy that gets results, you need high performance hardware that’s optimized to work with the software you use.

Modern data analytics spans a range of technologies, from dedicated analytics platforms and databases to deep learning and artificial intelligence (AI). Just starting out with analytics? Ready to evolve your analytics strategy or improve your data quality? There’s always room to grow, and Intel is ready to help. With a deep ecosystem of analytics technologies and partners, Intel accelerates the efforts of data scientists, analysts, and developers in every industry. Find out more about Intel advanced analytics.


Read More from This Article: How MLOps Is Helping Overcome Machine Learning’s Biggest Challenges
Source: News

Category: NewsApril 25, 2022
Tags: art

Post navigation

PreviousPrevious post:Cloud momentum sees SAP start the year strongNextNext post:6 smart practices for better business-IT alignment

Related posts

Barb Wixom and MIT CISR on managing data like a product
May 30, 2025
Avery Dennison takes culture-first approach to AI transformation
May 30, 2025
The agentic AI assist Stanford University cancer care staff needed
May 30, 2025
Los desafíos de la era de la ‘IA en todas partes’, a fondo en Data & AI Summit 2025
May 30, 2025
“AI 비서가 팀 단위로 지원하는 효과”···퍼플렉시티, AI 프로젝트 10분 완성 도구 ‘랩스’ 출시
May 30, 2025
“ROI는 어디에?” AI 도입을 재고하게 만드는 실패 사례
May 30, 2025
Recent Posts
  • Barb Wixom and MIT CISR on managing data like a product
  • Avery Dennison takes culture-first approach to AI transformation
  • The agentic AI assist Stanford University cancer care staff needed
  • Los desafíos de la era de la ‘IA en todas partes’, a fondo en Data & AI Summit 2025
  • “AI 비서가 팀 단위로 지원하는 효과”···퍼플렉시티, AI 프로젝트 10분 완성 도구 ‘랩스’ 출시
Recent Comments
    Archives
    • May 2025
    • April 2025
    • March 2025
    • February 2025
    • January 2025
    • December 2024
    • November 2024
    • October 2024
    • September 2024
    • August 2024
    • July 2024
    • June 2024
    • May 2024
    • April 2024
    • March 2024
    • February 2024
    • January 2024
    • December 2023
    • November 2023
    • October 2023
    • September 2023
    • August 2023
    • July 2023
    • June 2023
    • May 2023
    • April 2023
    • March 2023
    • February 2023
    • January 2023
    • December 2022
    • November 2022
    • October 2022
    • September 2022
    • August 2022
    • July 2022
    • June 2022
    • May 2022
    • April 2022
    • March 2022
    • February 2022
    • January 2022
    • December 2021
    • November 2021
    • October 2021
    • September 2021
    • August 2021
    • July 2021
    • June 2021
    • May 2021
    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    Categories
    • News
    Meta
    • Log in
    • Entries feed
    • Comments feed
    • WordPress.org
    Tiatra LLC.

    Tiatra, LLC, based in the Washington, DC metropolitan area, proudly serves federal government agencies, organizations that work with the government and other commercial businesses and organizations. Tiatra specializes in a broad range of information technology (IT) development and management services incorporating solid engineering, attention to client needs, and meeting or exceeding any security parameters required. Our small yet innovative company is structured with a full complement of the necessary technical experts, working with hands-on management, to provide a high level of service and competitive pricing for your systems and engineering requirements.

    Find us on:

    FacebookTwitterLinkedin

    Submitclear

    Tiatra, LLC
    Copyright 2016. All rights reserved.