Operations | Monitoring | ITSM | DevOps | Cloud

Machine Learning

Netdata & Ansible example: ML demo room

We are always trying to lower the barrier to entry when it comes to monitoring and observability and one place we have consistently witnessed some pain from users is around adopting and approaching configuration management tools and practices as your infrastructure grows and becomes more complex. To that end, we have begun recently publishing our own little example ansible project used to maintain and manage the servers used in our public Machine Learning Demo room.

8 Important Things You Should Know About The Tech Used In The Food Industry

Are you curious about the technology that powers the food industry? From farm to fork, the use of cutting-edge tech has revolutionized how we produce, process, and consume our favorite meals. Whether you're a food enthusiast, a health-conscious individual, or simply intrigued by the latest innovations, understanding the role of technology in the food industry is vital. In this blog post, we'll explore eight important things you should know about the tech used in the food industry.

How Technological Advancements Transform Hiring Processes and Decisions

Technology has forever changed the landscape of businesses, especially in terms of HR practices. From hiring to onboarding and beyond, technological advancements have drastically altered the way companies develop their teams and make decisions. Automated technologies can help streamline processes, improve accuracy around evaluating job candidates, simplify communication and collaboration between colleagues globally-the list goes on! To better understand how technology influences human resources today and into the future, let's take a closer look at how far it has come already in transforming different stages within the typical hiring process.

ML Observability: what, why, how

Note: This post is co-authored by Simon Aronsson, Senior Engineering Manager for Canonical Observability Stack. AI/ML is moving beyond the experimentation phase. This involves a shift in the way of operating because productising AI involves many sophisticated processes. Machine learning operations (MLOps) is a new practice that ensures ML workflow automation in a scalable and efficient manner. But how do you make MLOps observable?

Top 8 Emerging Technology Trends for 2023

The state of technology is always changing. We are seeing new technological developments every day as a result of several things, such as improvements in research and development, the introduction of new goods and services, and modifications in customer preferences and requirements. The rapid development of artificial intelligence (AI) and machine learning is only one instance of how technology is evolving daily. These technologies are being utilized to increase productivity and decision-making across a range of sectors, including healthcare, banking, and retail.

Safeguarding Cryptocurrency Exchanges: The Power of Machine Learning Monitoring

Bitcoin and Coinbase have been in some hot water lately. How they handle cryptocurrency might not be legal or safe. The lack of regulations is causing concern from the government about potential criminal activity, fraud, and money laundering. The good news? Rules are being implemented for crypto exchanges to stop corrupt events from happening. Regulations like Know Your Customer (KYC) are an absolute must for exchanges to keep operating legally.

Solving the top 7 challenges of ML model development with CircleCI

Amid an AI boom and developing research, machine learning (ML) models such as OpenAI’s ChatGPT and Midjourney’s generative text-to-image model have radically shifted the natural language processing (NLP) and image processing landscape. Due to this new and powerful technology, developing and deploying ML models has quickly become the new frontier for software development.

Machine Learning Made Simple - Civo Navigate NA 2022

Josh Mesout explores the complexities and challenges of adopting machine learning and artificial intelligence (AI). He discusses the struggle to embrace and understand these technologies, leading to a high failure rate. Mesout highlights the significant time spent on infrastructure engineering and the need for expertise across various disciplines. He addresses the difficulty in justifying ROI and the risks associated with machine learning. Civo introduces KubeFlow as a service to simplify machine learning, including lower pricing points, GPU Edge box, and partnerships.

Kubeflow vs MLFlow: which one to choose?

Data scientists and machine learning engineers are often looking for tools that could ease their work. Kubeflow and MLFlow are two of the most popular open-source tools in the machine learning operations (MLOps) space. They are often considered when kickstarting a new AI/ML initiative, so comparisons between them are not surprising. This blog covers a very controversial topic, answering a question that many people from the industry have: Kubeflow vs MLFlow: Which one is better?

Transforming Data Analysis: Exploring The Latest Advancements in Extraction Capabilities

The digital age has ushered in a massive influx of data from various sources. As data continues to grow in volume and complexity, the need for effective data extraction tools that can help us glean actionable insights from this information is more pressing than ever. In fact, the process of data extraction has evolved significantly over the years, moving from rudimentary manual procedures to sophisticated automated systems.