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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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Can Artificial Intelligence Replace Human Intelligence?

The Crazy Programmer

Artificial Intelligence is a science of making intelligent and smarter human-like machines that have sparked a debate on Human Intelligence Vs Artificial Intelligence. Will Human Intelligence face an existential crisis? Impacts of Artificial Intelligence on Future Jobs and Economy.

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Difference between Artificial Intelligence and Machine Learning

The Crazy Programmer

We are talking about machine learning and artificial intelligence. Artificial Intelligence does not the system to be pre programmed however they are given algorithms which are able to learn on their own intelligence. . Machine learning is a subset of Artificial Intelligence.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.

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Building a vision for real-time artificial intelligence

CIO

Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificial intelligence. The underpinning architecture needs to include event-streaming technology, high-performing databases, and machine learning feature stores.

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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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MLOps 101: The Foundation for Your AI Strategy

Many organizations are dipping their toes into machine learning and artificial intelligence (AI). Download this comprehensive guide to learn: What is MLOps? How can MLOps tools deliver trusted, scalable, and secure infrastructure for machine learning projects? Why do AI-driven organizations need it?

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5 Things a Data Scientist Can Do to Stay Current

With the number of available data science roles increasing by a staggering 650% since 2012, organizations are clearly looking for professionals who have the right combination of computer science, modeling, mathematics, and business skills. Fostering collaboration between DevOps and machine learning operations (MLOps) teams.

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How to Choose an AI Vendor

You know you want to invest in artificial intelligence (AI) and machine learning to take full advantage of the wealth of available data at your fingertips. But rapid change, vendor churn, hype and jargon make it increasingly difficult to choose an AI vendor.

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Trusted AI 102: A Guide to Building Fair and Unbiased AI Systems

The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. How to choose the appropriate fairness and bias metrics to prioritize for your machine learning models. How to successfully navigate the bias versus accuracy trade-off for final model selection and much more.

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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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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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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.

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How AI and ML Can Accelerate and Optimize Software Development and Testing

Speaker: Eran Kinsbruner, Best-Selling Author, TechBeacon Top 30 Test Automation Leader & the Chief Evangelist and Senior Director at Perforce Software

In this session, Eran Kinsbruner will cover recommended areas where artificial intelligence and machine learning can be leveraged. With new AI and ML algorithms spanning development, code reviews, unit testing, test authoring, and AIOps, teams can boost their productivity and deliver better software faster.