Operations | Monitoring | ITSM | DevOps | Cloud

April 2024

Best practices for monitoring managed ML platforms

Machine learning (ML) platforms such as Amazon Sagemaker, Azure Machine Learning, and Google Vertex AI are fully managed services that enable data scientists and engineers to easily build, train, and deploy ML models. Common use cases for ML platforms include natural language processing (NLP) models for text analysis and chatbots, personalized recommendation systems for e-commerce web applications and streaming services, and predictive business analytics.

Best practices for monitoring ML models in production

Regardless of how much effort teams put into developing, training, and evaluating ML models before they deploy, their functionality inevitably degrades over time due to several factors. Unlike with conventional applications, even subtle trends in the production environment a model operates in can radically alter its behavior. This is especially true of more advanced models that use deep learning and other non-deterministic techniques.

Role of AI in Predictive Security Camera Monitoring

As the modern secure world faces several advanced technologies and ever-evolving security threats, the roles of artificial intelligence in predictive security camera monitoring systems have all the opportunities to be a stimulating force to protect people, properties, and public places. Thanks to the ability of AI to quickly predict threats and rapidly respond with much more accuracy than ever before, security systems can now be more tailored to surveillance and risk management needs, turning the traditional notion of this vertical upside down.

Crossing the machine learning pilot to product chasm through MLOps

Numerous companies keep launching AI/ML features, specifically “ChatGPT for XYZ” type productization. Given the buzz around Large Language Models (LLMs), consumers and executives alike are growing to assume that building AI/ML-based products and features is easy. LLMs can appear to be magical as users experiment with them.

How AI and Machine Learning are Revolutionizing the Research Process

The advent of Artificial Intelligence (AI) and Machine Learning (ML) has been nothing short of transformative. Scholars across disciplines are leveraging these technologies to analyze data quickly and accurately, opening new frontiers in knowledge and understanding. The traditional research process, often laborious and time-consuming, is evolving into a more efficient and dynamic practice thanks to the computational power of AI and ML. For example, students who turn to specialized services with requests such as "Write my paper for me" can receive academic papers much faster, thanks to AI.

How to overcome common challenges in machine learning deployments

🚨 To read the full findings from this research, visit The Machine Learning State of Play 2024 white paper. Are the challenges of deploying machine learning (ML) overshadowing its true potential in the modern workplace? Through our recent white paper , we spoke to 500+ developers who have experience working with ML systems to gain an understanding of the pain points faced by developers when using ML solutions.