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Bench Talk for Design Engineers

Bench Talk

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Bench Talk for Design Engineers | The Official Blog of Mouser Electronics


Becks SimpsonBecks Simpson is a Machine Learning Lead at AlleyCorp Nord where developers, product designers and ML specialists work alongside clients to bring their AI product dreams to life. She has worked across the spectrum in deep learning and machine learning from investigating novel deep learning methods and applying research directly for solving real world problems to architecting pipelines and platforms to train and deploy AI models in the wild and advising startups on their AI and data strategies.


From Pixels to Predictions Becks Simpson
Satellite imagery offers an unparalleled view of our planet, but making sense of it at scale is a major challenge. Explore how computer vision turns raw data into actionable insights for many environmental applications.

POC Post-Production: Tracking for ML Observability Becks Simpson
Explore essential techniques for monitoring and updating machine learning models after deployment to ensure that they continue to perform at their best.

Extending the ML Proof of Concept for Production Becks Simpson
Our series on building a machine learning proof of concept continues with a focus on software development and deployment. Transfer the model into production by using robust software development techniques and available deployment solutions.

Open Source for ML Modeling Becks Simpson
With the help of existing resources, implementing the machine learning models required for a proof of concept isn’t as difficult as it seems.

A Robust Experimentation Environment for Success Becks Simpson
Ensuring that an experimentation environment is structured to allow quick iteration on ML models and data is crucial for successful projects that produce robust and trustworthy results.

Data Criteria for ML POC Success Becks Simpson
Selecting data inputs and labels, evaluating data quality, and determining the needed quantity of data are critical steps for a successful machine learning project.

ML Proof of Concept to Production Part One Becks Simpson
Starting on the right foot by defining business goals and relevant metrics is key to developing a successful machine learning proof of concept and taking it to production.

GPT-3.5 vs. GPT-4: What's the Difference? Becks Simpson
GPT-3.5 and GPT-4 push the boundaries of what is possible with language models. Depending on their specific use cases and product requirements, users could leverage GPT-3.5 for accurate and nuanced language generation or GPT-4 for more complex language generation.

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