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Machine Learning

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.

Kubeflow vs MLFlow

Learn the main differences between the MLOps tools of choice: Kubeflow and MLFlow Started by Google a couple of years ago, Kubeflow is an end-to-end MLOps platform for AI at scale. Canonical has its own distribution, Charmed Kubeflow, which addresses the entire machine-learning lifecycle. Charmed Kubeflow is a suite of tools, such as Notebooks for training, Pipeline for automation, Katib for hyperparameter tuning or KServe for model serving and more. Charmed Kubeflow benefits from a wide range of integrations with other tools such as MLFlow, Spark, Grafana or Prometheus.

Monitor machine learning models with Fiddler's offering in the Datadog Marketplace

With the growing utilization of AI, modern business applications rely more and more on machine learning (ML) models. But the complexity of these models poses significant challenges to data scientists, engineers, and MLOps teams seeking to maintain and optimize performance.