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7 Libraries for Machine Learning

Analytics Vidhya

Introduction Machine learning has revolutionized the field of data analysis and predictive modelling. With the help of machine learning libraries, developers and data scientists can easily implement complex algorithms and models without writing extensive code from scratch.

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Machine Learning Experiment Tracking Using MLflow

Analytics Vidhya

Introduction The area of machine learning (ML) is rapidly expanding and has applications across many different sectors. Keeping track of machine learning experiments using MLflow and managing the trials required to construct them gets harder as they get more complicated.

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15+ Github Machine Learning Repositories for Data Scientists

Analytics Vidhya

Introduction If I had to pick one platform that has single-handedly kept me up-to-date with the latest developments in data science and machine learning – it would be GitHub.

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Implementing Query2Model: Simplifying Machine Learning

Analytics Vidhya

Introduction Embark on an exciting journey into the world of effortless machine learning with “Query2Model”! Join us as we delve into the […] The post Implementing Query2Model: Simplifying Machine Learning appeared first on Analytics Vidhya.

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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. Holding onto old BI technology while everything else moves forward is holding back organizations.

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No Code Machine Learning for Non-CS Background

Analytics Vidhya

Introduction The recent decade has witnessed a massive surge in the application of Machine learning techniques. Adding machine learning techniques to […] The post No Code Machine Learning for Non-CS Background appeared first on Analytics Vidhya.

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6 Free University Courses to Learn Machine Learning

Analytics Vidhya

Introduction Machine learning (ML) is rapidly transforming various industries. Companies leverage machine learning to analyze data, predict trends, and make informed decisions. Learning ML has become crucial for anyone interested in a data career. From healthcare to finance, its impact is profound.

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How Banks Are Winning with AI and Automated Machine Learning

By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the whitepaper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

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How Banks Are Winning with AI and Automated Machine Learning

By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

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Intelligent Process Automation: Boosting Bots with AI and Machine Learning

In Data Robot's new ebook, Intelligent Process Automation: Boosting Bots with AI and Machine Learning, we cover important issues related to IPA, including: What is RPA? But in order to reap the rewards of Intelligent Process Automation, organizations must first educate themselves and prepare for the adoption of IPA. What is AI?

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Resilient Machine Learning with MLOps

Today’s economy is under pressure from inflation, rising interest rates, and disruptions in the global supply chain. As a result, many organizations are seeking new ways to overcome challenges — to be agile and rapidly respond to constant change. We do not know what the future holds.

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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). Machine Learning Operations (MLOps) allows organizations to alleviate many of the issues on the path to AI with ROI by providing a technological backbone for managing the machine learning lifecycle through automation and scalability.

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5 Things a Data Scientist Can Do to Stay Current

And more is being asked of data scientists as companies look to implement artificial intelligence (AI) and machine learning technologies into key operations. Fostering collaboration between DevOps and machine learning operations (MLOps) teams. Sharing data with trusted partners and suppliers to ensure top value.

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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.

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Build Trustworthy AI With MLOps

In our eBook, Building Trustworthy AI with MLOps, we look at how machine learning operations (MLOps) helps companies deliver machine learning applications in production at scale. For businesses that are AI-driven, this trust hinges on the confidence that their AI solution can help them make their most critical decisions.