What Companies Need to Know to Advance AI Adoption with MLOps - Part 1
June 29, 2022

Lucas Bonatto
Elemeno

Artificial intelligence (AI) will likely have more impacton our society than the Industrial Revolution. It should come as no surprise that AI has overpassed the terms big data, cloud, and machine learning (ML) combined in corporate earning calls.

AI will be the key differentiator between companies that strive for high margins versus those that can barely stay afloat — AI adoption goes hand in hand with cost decreases. Amidst the rapid rate of AI advancement, there are countless benefits for companies who leverage it internally or to improve customer experience.

But assuming that most companies already using AI are observing a cost reduction of 20%, why are others not following the lead?

Companies' adoption rate is still very shy due to a lack of available tools to streamline AI development, with an average change of +2 percentage points across industries from 2020 to 2021. However, machine learning operations (MLOps), combining ML and software engineering, are becoming more mainstream and intend to improve the quality and speed of delivering ML models to production.

AI professionals, such as data scientists and ML experts, often spend too much time on the nitty-gritty of software engineering and should be focusing their energy on teaching machines to learn instead. MLOps allows for collaboration and communication between data scientists and operations professionals to simplify management processes. These AI professionals can focus on their domain of expertise and automate the burden of creating AI from zero in large-scale production environments.

Business leaders are missing out on many opportunities without proper MLOps platforms in place. So, how can companies get started?

Look at What to Consider Before Implementing a MLOps Platform

A MLOps platform can benefit companies of all sizes. Still, it is important to think about how much data your company generates and whether you have the resources to manage and process this data. This is where business leaders should make sure the technology they want to adopt aligns with their business models.

Since implementing a MLOps platform internally can be costly, it is important to consider your company's budget and whether the platform's benefits justify the costs. Some companies may not be willing to allocate a budget to have an entire team of MLOps engineers to support their data teams.

Business leaders need to weigh up whether to build a ML platform internally or employ a MLOps platform from a third-party provider. For most scenarios, using a SaaS offering is the best way, especially a pay-per-use model with no fixed fees. Unless you're a technology company wanting to offer MLOps as a service, there should be no need to build your own MLOps platform.

Then, look at your objectives and goals: What do you want to achieve with a MLOps platform?

Do you want to automate the ML lifecycle to help increase team efficiency?

Are you looking to improve collaboration across your organization by tracking each step in the ML lifecycle?

Do you want to deploy infrastructure as code (IaC) or continuous integration/ continuous delivery (CI/CD) tools to automate building and testing?

Before reaching out to SaaS providers to implement a MLOps platform, assess your company's current infrastructure, technical capabilities, team experience, and where you could benefit the most from AI. You could first look for low-hanging fruit, such as tasks that have a heavy financial burden but don't require creative intelligence.

These steps will help to ensure that any platform can be seamlessly integrated into your company's existing systems. Some companies with pre-defined models and senior professionals with data science experience often choose to embed AI and custom models into existing tools and processes. Others, like startups, decide to have AI at their core from the beginning but also look for solutions with a pre-trained model so the team can integrate it quickly and fine-tune where necessary.

Go to: What Companies Need to Know to Advance AI Adoption with MLOps - Part 2

Lucas Bonatto is CEO and Founder of Elemeno
Share this

Industry News

August 16, 2022

Canonical welcomes the .NET development platform, one of Microsoft’s earliest contributions to open source projects, as a native experience on Ubuntu hosts and container images, starting in Ubuntu 22.04 LTS.

August 16, 2022

Veracode announced the launch of the Veracode Velocity Partner Program.

August 16, 2022

Render announced a new monorepository feature that enables its customers to keep all of their code in one super repository instead of managing multiple smaller repositories.

August 15, 2022

Gadget announced Connections, a major new feature that gives app developers access to building blocks that enable them to build and scale ecommerce apps in a fraction of the time, at a fraction of the cost.

August 15, 2022

Opsera is on the Salesforce AppExchange to help enterprise customers shorten software delivery cycles, improve pipeline quality and security, lower operations costs and better align software delivery to business outcomes.

August 15, 2022

Virtusa Corporation earned the DevOps with GitHub on Microsoft Azure advanced specialization, a validation of a services partner's deep knowledge, extensive experience and proven success in implementing secure software development practices applying DevOps principles and using Azure and GitHub solutions.

August 15, 2022

Companies looking to reduce their cloud costs with automated optimization can now easily procure CAST AI via Google Cloud Marketplace using their existing committed spend.

August 11, 2022

Granulate, an Intel Company, announced the upcoming launch of its latest free cost-reduction solution, gMaestro, a continuous workload and pod rightsizing tool for Kubernetes cost optimization.

August 11, 2022

Rezilion announced the availability of MI-X, a newly created open-source tool developed by Rezilion's vulnerability research team.

August 11, 2022

Contrast Security announced its enhanced application programming interface (API) security capabilities within the Contrast Secure Code Platform.

August 10, 2022

Mirantis made it even easier to integrate Mirantis Container Cloud into developer workflows and provide developers and operators with easy access and visibility into the Kubernetes clusters with the Mirantis Container Cloud Lens Extension announced today.

August 10, 2022

ArmorCode announced an integration with Traceable AI which will bring its data into the ArmorCode platform and improve Application Security Posture from code to cloud.

August 10, 2022

Quali unveiled enhanced features for its Torque platform to unify infrastructure orchestration and governance.

August 09, 2022

Veracode announced the enhancement of its Continuous Software Security Platform with substantial improvements to its integrated developer experience.

August 09, 2022

Normalyze announced General Availability for its Freemium offering, a self-serve, free platform that democratizes data discovery and classification in all three public clouds, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).