Containers and Microservices have become the most popular way for new software deployments, and for application re-factoring, such as twelve factor apps.

Up until recently, we accepted the “black box” narrative surrounding AI as a necessary evil that could not be extrapolated away from AI as a concept.

There are several narratives that permeate around AI today. It has great promise, but it is not without pitfalls.

In simple terms, transfer learning is a machine learning approach where a model that is already trained on a specific data set and developed for a specific task

Continuous Integration (CI) and Continuous Delivery (CD) are staples of a modern software development workflow that enable developers to release their code rapi

Decisions organizations make today about building their AI pipelines will have major impacts on the future of AI maintenance for their organizations.

A combination of “deep learning” and “fake media,” a deepfake is manipulated video content that is nearly indistinguishable from authentic video.

Artificial Intelligence (AI) is all the buzz with everyone looking for ways to leverage AI in some capacity within their organization.

AI is brittle. It can be fooled. Threats to accuracy and performance of your models are lurking in unsuspected parts of your pipeline.

Developing and deploying AI-powered systems and applications is a complex business, especially in our extended remote reality.

No one should be doing AI for the sake of doing AI—it must be tied to a clear business objective. All AI applications stem from mining data, whatever the aim.

Welcome to the second installment of our ModelOps blog series, where we dive deep into the next step in the ModelOps pipeline, Model Training. During Model Trai

The research and development (R&D) phase of building an AI model to address a business problem is characterized by rapid exploration and iteration.

When an organization commits to DevSecOps, a fundamental shift takes place across teams. Security becomes everyone’s responsibility, which requires some rules.

In this installment of the ModelOps Blog Series, we will transition from what it takes to build AI models to the process of deploying into production.

Software and systems are the backbone of most organizations. When an application fails or a system is breached, it can lead to dire loss and harm.

Containerization is important for models to function properly once production begins. Leveraging continuous integration/deployment is will improve efficiency

With two common buzzwords in AI being Graphics Processing Unit (GPU) and Batch Processing, there is widespread need to run AI efficiently in production.

Modzy is an enterprise software platform equipped to manage and host machine learning models built in any programming language or framework – at scale.

Organizations using Artificial Intelligence (AI) and Machine Learning (ML) solutions face a challenging problem.