AI & Machine Learning

5 Developments In AI In 2021

Artificial intelligence (AI) has come a long way in recent years, and the advancements show no signs of slowing. Here are five recent examples of progress in AI that could positively impact the future.

Sustaining The Fast Pace Of Digital Business Transformation

The acceleration of digital business initiatives triggered changes in the way work gets done and decisions are made, but as hybrid workforce models continue, leaders are finding it critical to keep up digital momentum.

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How Is Artificial Intelligence Evolving Mobile Technology?

AI presents many possibilities for innovation in the mobile app industry. AI is the future of mobile app development. Artificial intelligence is changing how users interact with app services and products.

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  • Data Science Governance – Don’t Reinvent The Wheel

    As data science processes continue to become operationalized and embedded within business processes, the importance of governing those processes continues to rise. This blog will discuss a couple of distinct areas of governance that organizations should consider.

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    Avoiding The 4 Major Pitfalls Of Data Science Projects

    Working on a data science project, especially with a new stakeholder, can be challenging. Learn how to avoid the main pitfalls.

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    AI Can Help Companies Tap New Sources Of Data For Analytics

    AI is making it possible for analytics to automatically incorporate and process important context from a broad array of sources — many of which would have previously required analysts to navigate silos and poorly maintained catalogues.

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    How To Learn AI Programming From Scratch

    AI programming is an important virtue of a professional employee. Indeed, today many companies need such a specialist.

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    6 Useful Probability Distributions With Applications To Data Science Problems

    Probability distributions are mathematical functions describing probabilities of things happening. Let’s take a look at six useful probability distributions!

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    How To Deploy Machine Learning Models

    A step-by-step overview Jupyter notebooks are where machine learning models go to die. Wait— what? Unlike what you probably learned in University, building models in a Jupyter notebook or R Studio script is just the very beginning of the process. If your process ends with a model sitting in a notebook, those models almost certainly

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    Results Are Not The Biggest Factor In Data Science Success

    The most important factor in determining if a given data science project will succeed or fail in a business environment is not the quality of the results.

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    Professionalize AI To Maximize Investment

    As AI becomes increasingly prevalent in organizations’ operations, why should it be treated differently than other roles? Proper training and guidelines will maximize your investments, create long-term growth opportunities for AI in your organization and power your business to outperform peers.

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    How RPA And IPA Differ And Why You Need Them

    IPA is the next stage of automation which builds upon RPA’s capabilities with the addition of advanced technologies such as NLP, machine learning, and computer vision.

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