DevOps.com

MARCH 22, 2024

The rise of edge computing will drive a long-overdue convergence of DevOps, data engineering, security, networking, OT and MLOps best practices.

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Xebia

FEBRUARY 25, 2024

What is this thing called MLOps ? In this blog post, we’ll try to demystify MLOps and take you through the process of going from a notebook to your very own industry-grade ML application. The first part will be about the what and why of MLOps and the second part about technical aspects of MLOps.

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DevOps.com

OCTOBER 7, 2020

Recently, there has been a lot of talk about MLOps from the DevOps and software engineering perspective. The message often comes across as, “Hurry, DevOps community, let’s set some standards before MLOps reinvents the wheel,” or “The purpose of MLOps is to allow anyone who knows how to code to get ML into production.”

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DevOps.com

MARCH 31, 2022

As previously discussed, three distinct fields—DevOps, MLOps and DataOps—have evolved to govern each of these interconnected disciplines. Intelligent applications are (by their very nature) complex. While conventional software basically consists of one thing (code), intelligent software involves code, models and data.

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DevOps.com

NOVEMBER 5, 2020

Company CEO Diego Oppenheimer said Algorithmia Insights provides a level of observability into ML models that DevOps teams have come to expect from applications. […]. The post Algorithmia Allies With Datadog on MLOps Observability appeared first on DevOps.com.

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Xebia

NOVEMBER 7, 2022

Machine Learning Operations (MLOps) climbed in popularity over the past few years with the promise to apply DevOps to Machine Learning. You need data science product teams to do MLOps. Conclusion: A holistic view is the key to MLOps. MLOps revolves around people, processes, and the organization as a whole.

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Hacker Earth Developers Blog

JANUARY 18, 2023

Ashutosh: I have been a part of recruitment in the data science field for nearly 14 years of my career and have recruited for successful startups (seed to Series D) and MNCs across levels (entry, junior, mid and senior management) and profiles including data analysts, data scientist, ML engineers, full stack developers, and DevOps/MLOps.

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Dataiku

APRIL 28, 2022

Upon witnessing the success of DevOps, the analytics community quickly realized the opportunity for a similar application of practices into analytics work — enter MLOps, a way that data teams and analytics teams alike can benefit from the automation of model deployment and integration.

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DataRobot

JUNE 16, 2021

In several earlier blog posts, we have focused on what we at DataRobot call the AI production gap , which refers to the gap that makes it difficult to transition models from the data science teams who develop them to the IT and DevOps teams who are responsible for deploying and monitoring them in production. The MLOps Management Agent.

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Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

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OTS Solutions

MAY 2, 2023

In this blog, we will be discussing what the Digital Transformation process is in business and how Digital Transformation technologies can impact your business growth. This blog outlines several Digital Transformation Technologies that organizations are leveraging to drive digital transformation.

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Coveros

MARCH 20, 2024

If you’ve attended our STAREAST , STARWEST , and Agile + DevOps conferences , AI Con USA’s structure will feel familiar. MLOps: DevOps for Machine Learning : Learn how to apply DevOps knowledge to the machine learning and AI development, test, delivery, and operations process.

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Coveros

APRIL 3, 2024

We’ll also be providing critical pre-conference training focused on key topics in AI and machine learning: AI for Testers Getting Started with Machine Learning GitHub Copilot for Developers MLOps: DevOps for Machine Learning DISCOVER AI CON USA The post Predicting the Future of AI-Driven Software Testing appeared first on Coveros.

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Software Review Testing Software Machine Learning 52

OpenCredo

APRIL 20, 2021

Why this blog post? I find this a wonderfully powerful paradigm; the move towards DevOps has blurred the boundaries of application development and deployment, and this choice drives the same for machine learning. Model serving is one of the more well-populated areas of the MLops space, so there are plenty of options.

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Machine Learning Artificial Inteligence Software Review Open Source 140

Altexsoft

DECEMBER 15, 2021

What is AutoML and how it relates to MLOps. AutoML objectives and benefits overlap with those of MLOps — a broader discipline with focus not only on automation but also on cross-functional collaboration within machine learning projects. MLOps cycle. Google Vertex AI: a unified interface for the entire MLOps cycle.

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Coveros

MARCH 6, 2024

We’ll also be providing critical pre-conference training focused on key topics in AI and machine learning: AI for Testers Getting Started with Machine Learning GitHub Copilot for Developers MLOps: DevOps for Machine Learning LEARN MORE The post AI in Software Development: A Revolution for Professionals appeared first on Coveros.

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Software Development Artificial Inteligence Software Development 52

O'Reilly Media - Ideas

JANUARY 25, 2024

Rachel Stephens provides two fascinating pieces of the puzzle in a recent article on the RedMonk blog , but those pieces don’t fit together exactly. Interest in operations for machine learning (MLOps) grew 14% over the past year. As companies deploy the applications they’ve been building, MLOps will continue to see solid growth.

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DataRobot

FEBRUARY 11, 2022

In the first part of the “Ever-growing Importance of MLOps” blog, we covered influential trends in IT and infrastructure, and some key developments in ML Lifecycle Automation. DataRobot’s MLOps product offers a host of features designed to transform organizations’ user experience, firstly, through its model-monitoring agents.

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Machine Learning Artificial Inteligence Systems Review eBook 52

Cloudera

AUGUST 25, 2020

As you may remember, we recently announced a full set of MLOps capabilities in Cloudera Machine Learning , our cloud native machine learning tool for the cloud. The truth is that model serving and deployment workflows have repeatable, boilerplate aspects which enterprises can automate using modern DevOps techniques.

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Machine Learning Artificial Inteligence Technical Review Software Review 74

DevOps.com

FEBRUARY 28, 2024

JFrog's integration with Qwak's MLOps platform will advance collaboration between teams building and deploying multiple classes of software artifacts.

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Team Building Software Continuous Delivery Machine Learning 71

DevOps.com

APRIL 21, 2023

To assess a company’s machine learning readiness, we must consider several key factors, including: ● Vision/strategy ● Data availability ● Expertise, infrastructure and resources ● MLOps […] The post Preparing for Machine Learning Readiness to Unlock its Full Potential appeared first on DevOps.com.

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DataRobot

MARCH 16, 2023

Value at Production Scale – DataRobot’s ML Production is more than just basic MLOps tooling and now new features are making it even easier and faster to scale and maintain model performance. blog series and deep dive into the new 9.0 features over the next few weeks. We’re only just getting started.

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DevOps.com

FEBRUARY 19, 2024

The success of organizations in today’s digital economy demands speed — the ability to quickly respond to market trends with new applications and services, instant access to critical data, and resolution of issues even before they affect service.

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Trends Applications Technology Marketing 61

DevOps.com

MARCH 4, 2024

Looking for a great new DevOps job? Check out these available opportunities at New York City's MTA, Mayo Clinic and more!

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DevOps.com

AUGUST 25, 2022

In today’s data age, getting data analytics right is more essential than ever. A robust data analytics implementation enables businesses to hit key performance metrics, build data and AI-driven customer experiences (think ‘personalize my feed’) and capture operational issues before they spiral out of control.

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Analytics Metrics Data Performance 133

O'Reilly Media - Ideas

OCTOBER 13, 2020

In an incident management blog post , Atlassian defines SLOs as: “the individual promises you’re making to that customer… SLOs are what set customer expectations and tell IT and DevOps teams what goals they need to hit and measure themselves against. Deciding what investment to make in MLOps tooling is an inherently complex task.

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DevOps.com

MAY 13, 2022

As the machine learning market matures, new tools are evolving that better match data science and machine learning teams’ needs. Vendors, both open source and private, have been quick to introduce new products that better meet requirements making it easier and faster to develop models and enable collaboration.

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DevOps.com

DECEMBER 7, 2021

Iterative today added an experiment versioning capability to an open source platform for managing machine learning operations (MLOps) using GitOps workflows. The post Iterative Adds Experiment Versioning to MLOps Platform appeared first on DevOps.com.

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DevOps.com

NOVEMBER 29, 2022

Zilliz this week made generally available a managed cloud service that can now be spun up in less than five minutes. The service is based on the company’s open source Milvus vector database. Frank Liu, director of operations for Zilliz, said the managed Zilliz Cloud service running on the Amazon Web Services (AWS) cloud will […].

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DevOps.com

MARCH 28, 2023

Enfabrica Corp. today emerged from stealth at the MemCon conference to launch a class of processors for networking in cloud computing environments that are optimized for moving data between CPUs, graphical processor units (GPUs), accelerators and memory.

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DevOps.com

DECEMBER 16, 2022

With nearly 60% of businesses set to adopt MLOps by 2024, this function has quickly established itself as an indispensable part of the enterprise technology world. And with computing needs rapidly expanding as digital transformation efforts continue, the growth of the MLOps market is unlikely to stop anytime soon.

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DevOps.com

JANUARY 17, 2021

The post How MLOps Can Help Get AI Projects to Deployment appeared first on DevOps.com. In fact, a recent survey by NewVantage Partners revealed that only 15% of leading enterprises have gotten any AI into production at all.

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DevOps.com

DECEMBER 14, 2023

CloudBolt's Kyle Campos believes that, going forward, the cost to run an application will become the ultimate performance metric.

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DevOps.com

APRIL 6, 2021

The second wave of enterprise artificial intelligence and machine learning (AI/ML) adoption is building, and is expected to crest in 2021, as many organizations increased and accelerated their investments in tools and people.

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DevOps.com

NOVEMBER 3, 2021

Oracle today unfurled Oracle Cloud Infrastructure (OCI) AI services, a collection of services that make it easier for developers to use application programming interfaces (APIs) to invoke a wide range of services based on algorithms trained by Oracle on their behalf.

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DevOps.com

OCTOBER 20, 2021

Every once in a while, a trend becomes broad enough that I feel the need to offer career advice. This year it will be simple: Know enough about AI/ML to be dangerous. We are past the point where not having at least a basic understanding is a good idea. Looking around the data center, AI […].

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Data Center Trends Data DevOps 106

Cloudera

JULY 26, 2021

Consistency simplifies and accelerates DevOps activities related to data product development by delivering an singular development platform where different roles and development disciplines (e.g., The post Five Strategies to Accelerate Data Product Development appeared first on Cloudera Blog.

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