How to organize machine learning teams

How to organize machine learning teams

Optimize ML team structure: Discover 2 common approaches, their pros and cons, and learn to choose the right one for your organization.

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Introduction to MLOps

Introduction to MLOps

An overview of MLOps field, its similarities & differences compared to DevOps, & how it can help organizations navigate common issues.

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Accelerated Annotation Supercharged by Meta’s Segment Anything Model

Accelerated Annotation Supercharged by Meta’s Segment Anything Model

We’ve integrated Meta AI’s Segment Anything Model (SAM) into our Accelerated Annotation product to deliver precise segmentation at unprecedented speeds.

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ICYMI: Key Insights from Smart Cities Expert Jonathan Reichental

ICYMI: Key Insights from Smart Cities Expert Jonathan Reichental

In our latest event, we talked with smart cities expert Jonathan Reichental about the future of urban development. Here are some key insights from our chat

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ICYMI: Key Insights from Data Science Icon Usama Fayyad

ICYMI: Key Insights from Data Science Icon Usama Fayyad

3 key takeaways from our latest LinkedIn Live event where we explored the state of AI & the importance of integrating human intervention with automation.

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Optimizing Decision Making by Combining Automation and People

Optimizing Decision Making by Combining Automation and People

An incremental design approach to automation and machine learning affords strategic opportunities for choosing to route exceptions to machines or people.

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3 Common Mistakes Automation and ML Modelers Make

3 Common Mistakes Automation and ML Modelers Make

Automation and AI hold great potential to innovate, improve, and make predictions. Here are three mistakes you’ll want to avoid.

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Maximizing ROI for ML, Decision Management, and RPA

Maximizing ROI for ML, Decision Management, and RPA

Could the secret to developing ML be more boring than we think? It’s time to give up the quest for the perfect model.

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6 AI Predictions for 2021: A View From the Trenches

6 AI Predictions for 2021: A View From the Trenches

What will 2021 bring to the world of AI and machine learning? CloudFactory CEO and founder Mark Sears shares our predictions.

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How Computer Vision Helps Us See the Future

How Computer Vision Helps Us See the Future

Can AI help us in predicting the future with computer vision? Here's how computer vision can use today’s data to model tomorrow’s outcomes.

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Why Using Data Scientists for Data Labeling is a Big Mistake [Infographic]

Why Using Data Scientists for Data Labeling is a Big Mistake [Infographic]

Your in-house data scientists shouldn't be doing tedious data labeling work for machine learning projects. They should be focusing on more important innovation.

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How to Keep Your Machine Learning Models Up-to-Date

How to Keep Your Machine Learning Models Up-to-Date

No matter how robust your initial training may be, keeping your machine learning models up-to-date is essential. Here are two retraining approaches.

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AI Bias And The 'People Factor' In AI Development

AI Bias And The 'People Factor' In AI Development

AI is only as good as the data it's trained to analyze. CloudFactory CEO Mark Sears shares in Forbes about how AI bias can arise from people, tools, algorithms, and human ...

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The Life of a Data Scientist [Infographic]

The Life of a Data Scientist [Infographic]

What is a data scientist and what do they do? Explore their roles, responsibilities, and contributions to AI and machine learning.

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How Humans Can Stay Competitive in An AI World

How Humans Can Stay Competitive in An AI World

The future of work has shifted in the last decade. Here are a few ways to stay competitive as our world becomes increasingly driven by artificial intelligence.

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