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Exploring the pros and cons of cloud-based large language models

CIO

In light of this, developer teams are beginning to turn to AI-enabled tools like large language models (LLMs) to simplify and automate tasks. Many developers are beginning to leverage LLMs to accelerate the application coding process, so they can meet deadlines more efficiently without the need for additional resources.

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20 Machine Learning/Artificial Intelligence Influencers To Follow In 2020

Hacker Earth Developers Blog

Machine Learning (ML) is emerging as one of the hottest fields today. The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% billion by the end of 2025. billion by the end of 2025.

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20 Machine Learning/Artificial Intelligence Influencers To Follow In 2020

Hacker Earth Developers Blog

Machine Learning (ML) is emerging as one of the hottest fields today. The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% billion by the end of 2025. billion by the end of 2025.

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5 ways to deploy your own large language model

CIO

A large language model (LLM) is a type of gen AI that focuses on text and code instead of images or audio, although some have begun to integrate different modalities. That question isn’t set to the LLM right away. And it’s more effective than using simple documents to provide context for LLM queries, she says.

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How Banks Are Winning with AI and Automated Machine Learning

Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations.

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Article: Software Testing, Artificial Intelligence and Machine Learning Trends in 2023

InfoQ Culture Methods

Technology has taken significant leaps within the last few years, introducing advancements that have taken us further into the digital age — impacting the software testing industry and we're seeing advances in machine learning, artificial intelligence, and the neural networks making them possible.

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Snorkel AI scores $35M Series B to automate data labeling in machine learning

TechCrunch

One of the more tedious aspects of machine learning is providing a set of labels to teach the machine learning model what it needs to know. It also announced a new tool called Application Studio that provides a way to build common machine learning applications using templates and predefined components.

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How Banks Are Winning with AI and Automated Machine Learning

Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations.

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Data Science Fails: Building AI You Can Trust

The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. Any organization that is considering adopting AI at their organization must first be willing to trust in AI technology.

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The Business Value of MLOps

As machine learning models are put into production and used to make critical business decisions, the primary challenge becomes operation and management of multiple models. Download the report to find out: How enterprises in various industries are using MLOps capabilities.

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Humility in AI: Building Trustworthy and Ethical AI Systems

More and more critical decisions are automated through machine learning models, determining the future of a business or making life-altering decisions for real people. These failures can also significantly erode human trust in AI, rendering it ineffective for real-world applications in many industries.

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Realizing the Benefits of Automated Machine Learning

While everyone is talking about machine learning and artificial intelligence (AI), how are organizations actually using this technology to derive business value? This white paper covers: What’s new in machine learning and AI.

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Machine Learning for Builders: Tools, Trends, and Truths

Speaker: Rob De Feo, Startup Advocate at Amazon Web Services

Machine learning techniques are being applied to every industry, leveraging an increasing amount of data and ever faster compute. But that doesn’t mean machine learning techniques are a perfect fit for every situation (yet). In what new directions machine learning’s most advanced practitioners are taking it now.

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The New Tech Experience: Innovation, Optimization, and Collaboration

Speaker: Paul Weald, Contact Center Innovator

Learn how to streamline productivity and efficiency across your organization with machine learning and artificial intelligence! How you can leverage innovations in technology and machine learning to improve your customer experience and bottom line.