Kong announced the launch of the latest version of Kong AI Gateway, which introduces new features to provide the AI security and governance guardrails needed to make GenAI and Agentic AI production-ready.
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. Business leaders are missing out on many opportunities without proper MLOps platforms in place. So, how can companies get started?
Start with: What Companies Need to Know to Advance AI Adoption with MLOps - Part 1
What Worked and Didn't for Other Contexts
As the adoption of AI goes mainstream, it's time for small and medium-sized enterprises (SMEs) to be inspired by the recipe that big tech companies have followed. However, SMEs shouldn't directly imitate big tech as they do not have near-infinite funds or extremely qualified professionals. As was the case with the inception of the internet, it took the industry a good chunk of time to learn how the web could revolutionize business. There is no reason why we shouldn't expect a similar scenario with AI.
The first learning from big tech is: Most large companies — wanting to adopt AI — hire teams to build internal platforms for ML practitioners. But these data scientists or ML engineers are often not familiar with enterprise software engineering. Expecting them to learn is feasible but inefficient, which is where MLOps platforms come in.
If you know enough people working at scale-ups and big corporations, you will have heard about companies trying to develop their own ML platforms internally. After all, it worked for Uber(link is external) with "Michelangelo." Unfortunately, that route doesn't work for companies that haven't managed to secure large amounts of funding. Even Uber stated(link is external): "As we evolve Uber's machine learning infrastructure and platform and support new machine learning use cases, we see new MLOps challenges emerge." Be wary of this route, and don't rush down that path unless completely necessary.
Companies could also look to Tesla, where a big part of their success can be attributed to their mastery of AI and high rate of innovation(link is external), which has been part of Tesla since the company's early stage. Tesla is tackling the data collection and model improvement feedback problem relevant to real-world ML applications. They use data feedback loops to collect the best data on self-driving cars to develop their existing models and solve ML challenges(link is external).
For years, Amazon has also been using ML to enhance product recommendations, create personalized experiences, and even allocate products in distribution centers to minimize transport costs and maximize delivery efficiency. And in return, they created Amazon SageMaker to help streamline the ML lifecycle by automating and standardizing MLOps practices for organizations.
Choosing a MLOps Platform
The number of choices for MLOps platforms can be overwhelming. Here are some factors companies should keep in mind when choosing one:
■ Ease of use: The platform should be easy to use and shouldn't require extensive training to get started. The idea is that companies can immediately use ML platforms without needing a full-on expert in engineering. Unfortunately, few platforms offer good user experience (UX).
■ Flexibility: The platform should be flexible enough to accommodate different types of ML models(link is external) and workflows, such as supervised, unsupervised, and reinforcement.
■ Scalability: MLOps platforms should be able to handle large-scale data and computations. Ideally, you should delegate scaling to a tool, and it shouldn't require you to manually ask it to increase scalability as the demand for models can likely be dynamic.
■ Security: Look to see if the platform provides security features such as authentication and authorization. ML security is important because ML systems often contain confidential information that organizations would not want the public or competitors to have access to.
■ Pricing: Affordability is a must and the platform should offer varying pricing models depending on your company's budget. Try to avoid any products that require a fixed fee per month. That usually means the SaaS company doesn't have an optimal architecture behind to support on-demand, volume-based charges.
■ MLOps tools are sometimes not enough: Companies and business leaders at the very beginning of their AI adoption journey want to see direct results from using AI quickly. Look out for platforms that offer pre-trained models and ready-to-use software infrastructure to complement the MLOps tools.
Various industries have taken a keen interest in AI to accelerate the new industrial revolution, but most companies will not be able to reach their AI goals working alone. Those trying to adopt AI without the proper tools will suffer slow turnover time for projects.
MLOps is quickly becoming mainstream and sought after by data scientist professionals wanting to accelerate and automate ML lifecycles and build highly scalable software infrastructure at companies of varying sizes. But business leaders must ensure these platforms align with their business objectives and goals, offering flexibility, scalability, and security for successful deployment.
Industry News
Traefik Labs announced significant enhancements to its AI Gateway platform along with new developer tools designed to streamline enterprise AI adoption and API development.
Zencoder released its next-generation AI coding and unit testing agents, designed to accelerate software development for professional engineers.
Windsurf (formerly Codeium) and Netlify announced a new technology partnership that brings seamless, one-click deployment directly into the developer's integrated development environment (IDE.)
The Cloud Native Computing Foundation® (CNCF®), which builds sustainable ecosystems for cloud native software, is making significant updates to its certification offerings.
The Cloud Native Computing Foundation® (CNCF®), which builds sustainable ecosystems for cloud native software, announced the Golden Kubestronaut program, a distinguished recognition for professionals who have demonstrated the highest level of expertise in Kubernetes, cloud native technologies, and Linux administration.
Red Hat announced new capabilities and enhancements for Red Hat Developer Hub, Red Hat’s enterprise-grade internal developer portal based on the Backstage project.
Platform9 announced that Private Cloud Director Community Edition is generally available.
Sonatype expanded support for software development in Rust via the Cargo registry to the entire Sonatype product suite.
CloudBolt Software announced its acquisition of StormForge, a provider of machine learning-powered Kubernetes resource optimization.
Mirantis announced the k0rdent Application Catalog – with 19 validated infrastructure and software integrations that empower platform engineers to accelerate the delivery of cloud-native and AI workloads wherever the\y need to be deployed.
Traefik Labs announced its Kubernetes-native API Management product suite is now available on the Oracle Cloud Marketplace.
webAI and MacStadium(link is external) announced a strategic partnership that will revolutionize the deployment of large-scale artificial intelligence models using Apple's cutting-edge silicon technology.
Development work on the Linux kernel — the core software that underpins the open source Linux operating system — has a new infrastructure partner in Akamai. The company's cloud computing service and content delivery network (CDN) will support kernel.org, the main distribution system for Linux kernel source code and the primary coordination vehicle for its global developer network.