Webinars & Videos

Gen AI's Future Among ADAS & AV Data Labeling Best Practices

Advancements in Gen AI have companies from all industries considering how to incorporate it into products and services. But is it in a place to revolutionize ADAS and AV? Watch to understand Gen AI in the context of automotive machine learning use cases, including pros and cons of three common data labeling techniques; the current state of Gen AI and its theoretical role in the future of ADAS; and how LLMs and HITL annotation together can advance automotive ML quickly & safely.

ML Model Pitfall: Scooter vs Skateboard Edge Case Example

upcoming

Webinar

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ML Model Pitfall: Scooter vs Skateboard Edge Case Example

It's impossible to predict the future. That's why your ML model needs to be resilient. This example from our webinar, 3 Pitfalls for ML Model Failure (and what to do about them) explains how ADAS and AV models were at risk of failing when trying to categorize people on scooters. From a computer vision standpoint, they looked like someone on a skateboard. But they behaved very differently; a model predicting certain movements could result in a collision.

Solving Complex HITL Challenges Using Insights-Driven Approaches | Auto AI USA 2024

upcoming

Presentation

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Solving Complex HITL Challenges Using Insights-Driven Approaches | Auto AI USA 2024

As we gain more efficiency from ever-better ML models, we are left with increasingly complex ML use cases that, in turn, pose greater HITL challenges. In this talk, Ryan Tavakolfar, Strategic Solutions Engineer at Sama, shares examples of addressing complex HITL challenges, such as annotating long sequences and using pre-annotations for efficiency, through insights-driven approaches and solutions. The presentation will also touch on some key considerations for data labeling in the world of Generative AI.

Live Podcast with Voxel 51: Gen AI, Measuring ROI, and What's Next for ML

upcoming

Webinar

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Live Podcast with Voxel 51: Gen AI, Measuring ROI, and What's Next for ML

Jason Corso (Voxel51), Duncan Curtis (Sama), and Rob Stevenson (How AI Happens podcast) discuss Sama’s 2023 ML Pulse Report on Generative AI, model production confidence, and overcoming the biggest challenges the industry faces as we move into 2024.

3 Pitfalls For ML Model Failure (And How to Avoid Them)

upcoming

Webinar

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3 Pitfalls For ML Model Failure (And How to Avoid Them)

The majority of data scientists say over 80% of their models fail to be deployed. What can ML practitioners do to get ahead of the biggest pitfalls — before they derail your projects, cause expensive delays, or erode trust in your product? Duncan Curtis (SVP, AI Product and Technology) reviews three of the most impactful pitfalls when building ML models—and what you can do to avoid them.

upcoming

Webinar

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2023 Trends in Autonomous Driving

As 2023 kicks into gear, the promises and challenges of autonomous vehicle development are evermore present. What trends and major predictions can we expect for this year?

How to Optimize Data Quality for Better ML Model Performance

upcoming

Webinar

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How to Optimize Data Quality for Better ML Model Performance

Your ML model’s success requires more than data. It needs a comprehensive approach to quality control that engages with an annotation partner who’s well-versed in the latest research-driven practices, a smart quality strategy that balances annotation precision with practical needs, and the agility to recognize and propose process enhancements in real-time.

Concerned About Safety? Your Training Data Could Be Veering You Off Course

upcoming

Presentation

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Concerned About Safety? Your Training Data Could Be Veering You Off Course

There are many challenges on the way to building AV solutions and high quality labeled data is one of them. During this informative session, Renata will highlight the importance of building a robust automotive annotation quality strategy.

How to Pick the Best Data Labeling Solution, and What You Should Consider When Choosing a Partner

upcoming

Webinar

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How to Pick the Best Data Labeling Solution, and What You Should Consider When Choosing a Partner

For companies striving to unlock the full potential of machine learning, access to accurate and scalable datasets often represents a significant bottleneck. This is in part because many common approaches to data labeling come with tradeoffs between accuracy, cost, and time investment.

Frequently Asked Data Labeling Questions – Sama at Ai4 2022

upcoming

Presentation

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Frequently Asked Data Labeling Questions – Sama at Ai4 2022

A day in the life of an ML engineer or a data scientist is not as glamorous as you might think; data-related tasks — from aggregating to labeling and augmenting data — can take up to 80% of their time. At Sama, we’ve helped hundreds of organizations overcome data challenges at every stage of the AI model lifecycle.

Recognizing and Overcoming Data Annotation Challenges for Enterprise Machine Learning

upcoming

Webinar

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Recognizing and Overcoming Data Annotation Challenges for Enterprise Machine Learning

Access to timely, adequate volumes of high-quality labeled data is one of the biggest barriers to optimizing model performance and effectively productizing enterprise ML solutions. The good news is that as an increasing number of computer vision models make it into production, best practices are crystallizing.

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Webinars & Videos

Gen AI's Future Among ADAS & AV Data Labeling Best Practices

ML Model Pitfall: Scooter vs Skateboard Edge Case Example

Solving Complex HITL Challenges Using Insights-Driven Approaches | Auto AI USA 2024

Live Podcast with Voxel 51: Gen AI, Measuring ROI, and What's Next for ML

3 Pitfalls For ML Model Failure (And How to Avoid Them)

2023 Trends in Autonomous Driving

How to Optimize Data Quality for Better ML Model Performance

Concerned About Safety? Your Training Data Could Be Veering You Off Course

How to Pick the Best Data Labeling Solution, and What You Should Consider When Choosing a Partner

Frequently Asked Data Labeling Questions – Sama at Ai4 2022

Recognizing and Overcoming Data Annotation Challenges for Enterprise Machine Learning

With Sama, Go Beyond Your Data