Tag: machine learning

Scaling Your Data Science Team

In an earlier post, we explored hiring your first Data Scientist. To ensure this individual is successful, you must have engaged executives, defined expectations, and a culture that accepts change while working in new ways to make decisions. Once you…

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Thinking About Organizational Change with ChatGPT

Recent weeks have brought the release of ChatGPT 4 with a host of new capabilities, including better creativity and longer post capability. These enable collaboration on technical writing, screenplays, and learning an author’s writing style. This capability builds on the…

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Fairness and Bias in Machine Learning

As machine learning becomes more widely used for automated decision-making, we must identify issues of fairness in ML outcomes. Ensuring fairness in ML is important for several reasons; lack of fairness in machine learning can perpetuate or amplify societal biases…

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After the migration: How to get the most out of your Google Cloud migration

All public clouds offer flexible computing, networking and storage. And you’re likely using at least one public cloud, if not two or more. But if you’re thinking about moving some of your mission-critical applications to the cloud—such as SAP or…

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How free funds can help you go further with Google Cloud

Blog 5: How free funds can help you go further with Google Cloud

Google Cloud is changing what’s possible in the cloud with advanced technologies like analytics and machine learning capabilities. That means cloud migrations are no longer just about lifting and shifting workloads to the cloud, but about fundamentally transforming the way…

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Don’t skip the most important part of your Google Cloud journey!

Organizations are turning to the cloud to reap the benefits of scalability, flexibility and cost savings as part of their data transformation efforts. But these environments are complex and require a strategic approach from the outset. Google Cloud is increasingly…

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Machine Learning: The Why, When and How

Why Machine Learning?

Why machine learning? At its simplest, machine learning (ML) uses mathematical models to analyze large volumes of data, identify patterns and make decisions. ML models can imitate human behavior to predict outcomes, such as those used for language translation, chatbot…

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Putting ML Prototypes Into Production Using TensorFlow Extended (TFX)

Introduction Machine learning projects start by building a proof-of-concept or a prototype. This entails choosing the right dataset (features), the appropriate ML algorithm/model and the hyper-parameters for that algorithm. As a result of a POC, we would have a trained…

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Five Ways Your Machine Learning Model Is like Cookie Monster

At the highest, simplest level, a machine learning (ML) model is an algorithm that ingests data and spits out insights, predictions or recommendations. It has two important phases—first you have to train your model (training) then you let the model…

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Heart Disease Prediction Using Keras Deep Learning

Heart disease covers a range of different conditions that could affect your heart. It is one of the most complex diseases to predict given the number of potential factors in your body that can lead to it. Identifying and predicting…

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