Originally published September 2021 | Updated December 2024 According to Forrester, over 25% of global data and analytics employees report annual losses exceeding $5 million due to poor data quality, with 7% estimating losses of $25 million or more. This number is staggering and considering machine learning optimization (MLOps) can significantly reduce these losses by […]
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In the rapidly evolving field of artificial intelligence (AI), keeping up to date on the advancements and trends is essential for businesses aiming to harness AI’s full potential. We believe, the Gartner Hype Cycle for Artificial Intelligence is a crucial resource for understanding the maturity and adoption of AI technologies. “This research helps AI
In the realm of natural language processing (NLP), the advent of Large Language Models (LLMs) has revolutionized the way we approach various tasks, including automated data extraction. LLMs, such as GPT, showcase remarkable capabilities in understanding and generating human-like text. Entity or Data extraction, a crucial component of information retrieval, involves identifying and classifying entities
Harnessing the Power of LLMs for Automated Data Extraction Read More »
In November 2022, the launch of ChatGPT marked a groundbreaking milestone in AI, highlighting the need for the evolution of global AI regulations. In fact, demand for this evolving technology is so substantial that Gartner estimates more than 80% of enterprise companies will have utilized Generative AI APIs or deployed Generative AI-enabled applications by 2026.
Global AI Regulations: Navigating the Next Frontier Read More »
Over the last two years, Generative AI has proven to be more than just a fad. As enterprise organizations move from simple, initial experimentation to extensive Large Language Model (LLM) rollouts, the use cases for adoption have created new strategic opportunities from optimizing internal machine learning deployment lifecycles. Let’s delve into the various LLM use
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Generative AI has quickly become known outside of the IT landscape in the last year. But what exactly is the difference between generative AI and machine learning? By clearly understanding the differences between the two terms, you can learn how they can add value to your organization in distinct ways. What is Machine Learning? Machine
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The 2023 Gartner report, A CTO’s Guide to the Generative AI Technology Landscape, recognizes Seldon Technologies as a Sample Vendor. Seldon is listed as a Sample Vendor in the GenAI market. What is Generative AI? According to Gartner, “Generative AI (GenAI) learns from previous existing training data like images, videos, or text to generate new
Seldon Technologies has been recognized in the 2023 Gartner Market Guide for Artificial Intelligence (AI) Trust, Risk and Security Management (TRiSM). Seldon is listed as a Representative Explainability/Model Monitoring Vendor in the AI TRiSM market for its product Seldon Deploy Advanced. What is AI TRiSM? According to the Gartner report, “the AI TRiSM market comprises
Many companies are using data to drive their decisions. The aim is to remove uncertainties, guesswork, and gut feeling. A/B testing is a methodology that can be applied to validate a hypothesis and steer the decisions in the right direction. In this blog post, I want to show how to create a containerized micro-service architecture
A practical guide to A/B Testing in MLOps with Kubernetes and Seldon Core Read More »
Machine learning model inference is the use of a machine learning model to process live input data to produce an output. It occurs during the machine learning deployment phase of the machine learning model pipeline, after the model has been successfully trained. Machine learning model inference can be understood as making a model operational, or
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