Operations | Monitoring | ITSM | DevOps | Cloud

Machine Learning

Azure Machine Learning Pricing - 2024 Guide to ML Costs

Undoubtedly, AI is our future—which means it’s past time to integrate machine learning models into your FinOps multi-cloud tech stack. AI turns simple tasks into something that can be executed at the click of a button. With well-trained models, FinOps, MSPs, and Enterprises can automate cost detection, forecasting, and anomaly identification, streamlining complex financial operations without increasing their workforce. The good news?

Machine Learning and AI Explained

There is no escaping the discussion about how machine learning (ML) and AI systems will revolutionize how people and industries work. Most of this discussion needs to be revised, as companies are still evaluating how AI systems (typically Large Language Model (LLM) systems like OpenAI ChatGPT, Google Gemini, Anthropic Claude and others) enhance worker productivity and deliver business benefits. Cybersecurity is one sector where extensive use of AI-enhanced solutions is common.

How Machine Learning and AI are Transforming Telecom's Future

The telecommunications industry is no stranger to rapid technological advancements, but the integration of machine learning (ML) and generative AI is taking it to new heights. AI and ML are not just about technological transformation; they’re also revolutionizing people, processes, and the entire telco landscape. For tech enthusiasts and business leaders, understanding how these AI-driven innovations are shaping the future is crucial.

Optimise your ML workloads on Kubernetes

Kubernetes has proven to be a vital tool for developing and running ML models. It enhances experimentation, workflow management, and ensures high availability while handling the resource-intensive nature of AI workloads. With optimizations, Kubernetes can further improve resource utilization, making AI/ML projects more efficient.

The Future is Now: How AI is Revolutionizing App Development

Apps are become a necessary component of our everyday existence. We use them for everything from communication to entertainment, shopping, banking, health and fitness tracking, and more. As consumer demand grows for more intelligent and personalized apps, developers are turning to artificial intelligence (AI) to build the apps of the future. Here, we explore the current and future impact AI will have on app development.

Machine Learning Inference Model at Scale with Graviton

Sharpen your machine learning knowledge with our upcoming virtual event, "Machine Learning Model Inference at Scale with Graviton." In collaboration between 2bcloud and Amazon Web Services (AWS), this event will provide you with a comprehensive, step-by-step tutorial on setting up a high-performance and cost-optimized machine learning inference environment, using Graviton processors.

The Role of Machine Learning in Cybersecurity

Machine learning (ML) in cybersecurity dates back to the early 2000s and has become a key tool today in fighting cyber threats. According to Cybersecurity Ventures, global spending on cybersecurity products and services is expected to exceed $1.75 trillion cumulatively from 2021 to 2025, highlighting the increasing reliance on advanced technologies to combat cyber threats.

How Technology Advances Indoor Location Tracking Capabilities

The rapid evolution of technology has transformed numerous aspects of our lives, and indoor location tracking is no exception. Once limited to rudimentary applications, this innovative field has blossomed into a sophisticated system integral to various industries. From retail to healthcare, the advances in indoor location tracking have revolutionized how businesses operate and enhance user experiences. This article explores the latest technological developments in indoor location tracking, highlighting the role of Bluetooth beacons, Wi-Fi positioning, and Ultra-Wideband (UWB) technology.

Expanding Artifactory's Hugging Face Support with Datasets

When working with ML models, it’s fair to say that a model is only as good as the data it was trained on. Training and testing models on quality datasets of an appropriate size is essential for model performance. Because of the intricate link between a model and the data it was trained on, it’s also important to be able to store datasets and versioned models together.