An SRE's guide to optimizing ML systems with MLOps pipelines
As AI and ML become more prevalent, administrators can use Site Reliability Engineering (SRE) techniques to manage the ML infrastructure and software.
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Machine Learning
7 considerations when building your ML architecture
As the number of organizations moving their ML projects to production is growing, the need to build reliable, scalable architecture has become a more pressing concern. According to BCG (Boston Consulting Group), only 6% of organizations are investing in upskilling their workforce in AI skills. For any organization seeking to reach AI maturity, this skills gap is likely to cause disruption.
Learn to Forecast Time Series Data Using ML & InfluxDB
Forecasting is all about predicting the future—in data science, it is one of the key skills in dealing with time series data, such as stock price prediction, sales forecasting, logistics planning, etc. In this tutorial, we’ll learn how to forecast the notorious weather pattern of London, UK, using the following free and open source technologies.
MLOps Your Way with the JFrog Platform
Just like in traditional software development, creating AI applications isn’t a one size fits all approach. However, many of the challenges and concerns facing AI/ML development teams share common threads – difficulties getting models to production, tangled infrastructure, data quality, security issues, and so on.
Beginner's guide to getting started in machine learning
Machine learning (ML) has shifted from being a niche research field to a powerhouse behind many technologies we use daily. From personalized recommendations on streaming platforms to chatbots and image recognition, ML’s influence is everywhere. But what exactly is machine learning, and why should you invest time in learning about it? This blog will walk you through ML’s fundamentals, explain what you need to know, and outline a practical step-by-step plan to start your ML journey.
Five AI Programming Languages to Consider Using in 2025
This is a closer look at the top five good programming languages that AI developers should consider using in their upcoming projects.
Your 2025 Tech Toolkit: How to Keep Your Business on Top
Technology is advancing faster than ever, redefining the way businesses operate, interact with customers, and remain competitive. In 2025, embracing innovation isn't just an advantage-it's a necessity for survival and growth. Studies show that businesses leveraging advanced technologies like AI, IoT, and real-time data are better equipped to streamline operations and enhance customer satisfaction. From automating workflows to creating personalized experiences, the right tech tools unlock endless opportunities.
Breaking Silos: Unifying DevOps and MLOps into a Cohesive Software Supply Chain - Part 3
The synergy between DevOps and MLOps is more crucial now than ever. However, merging these two paradigms into a coherent software supply chain poses a unique set of challenges that can leave teams feeling overwhelmed. From the complexities of managing model dependencies to adapting conventional CI/CD tools for advanced machine learning (ML) workflows, the path to integration isn’t without its twists and turns.
Why Kubernetes Is Becoming the Platform of Choice for Running AI/MLOps Workloads
Artificial intelligence (AI) and machine learning operations (MLOps) have become crucial across a wide swath of industries, with the two technologies working in tandem to provide value. AI enables data-driven insights and automation, while MLOps ensures efficient management of AI models throughout their lifecycle. With AI’s growing complexity and scale, organizations need robust infrastructure to manage intensive computational tasks, giving rise to platforms like Kubernetes.
Solving E-Commerce's Cold Start Problem with Azure ML
Imagine visiting an e-commerce site that instantly understands your preferences, offering tailored product recommendations from the first click. For our client, this vision was about creating a seamless, engaging experience for new users by providing immediate, personalized suggestions. Using Azure ML Studio, we turned this vision into reality by solving key challenges like the “cold start problem” and building a robust recommendation system. Here’s how we made it happen.