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In-House vs Outsourcing Data Annotation for ML: Pros & Cons

In-house data annotation can be expensive but can be helpful in early stages of ML production. Outsourcing data annotation is cheaper but security can be compromised.

Auto.AI Europe | September 28-30 @ 8:00am CEST | Berlin, Germany

Bosch Connected World 2022 | November 9, 2022 @ 10:00am GMT | Berlin, Germany

ADAS & AV Technology Expo | September 7-8, 2022 @ 8:00am PST | San Francisco, USA

AI in Retail Summit + Deep Learning Summit | September 14-15, 2022 @ 8:00am BST | London, UK

Transform by Venture Beat | July 19, 2022 @ 8:00am PST | San Francisco, USA

Ai4 2022 | August 16-18, 2022 @8:00 a.m. PST | Las Vegas, USA


Defining, Measuring, and Guaranteeing Quality for Autonomous Driving

Good annotation and testing practices are the foundations of building a great model. However, understanding what constitutes quality data is a tricky question.

4 Game-Changing Applications of LiDAR in Retail

Customers walk around stores, browse displays, walk down aisles, and stop to consider products. From the outside, it may not appear there is much to glean from these behaviors.

Transport et navigation

Des véhicules autonomes aux aides à la navigation, alimentez vos solutions de transport et de navigation avec les données les plus précises du secteur.

Commerces de détail et électronique

Les attentes des consommateurs étant en hausse, les solutions d’IA séduisantes et engageantes doivent être alimentées par des données de haute qualité sans compromis.

Robotique et fabrication

Aidez vos clients à réduire les coûts, à augmenter la productivité et à renforcer la sécurité grâce à des données d’entrainement très précises pour la robotique et la fabrication.

10 Frequently Asked Data Labeling Questions

Here are some of the most frequently asked data labeling questions, along with recommendations for approaching your data annotation strategy holistically.

Building a Robust Automotive LiDAR Annotation Quality Rubric

The purpose of a LiDAR annotation quality rubric is simple: it ensures that two people will score the same object in identical ways.

A Message From Our CEO

« Like so many immigrant children, I learned to believe in a dream that is as much American as it is universal: a dream of equal opportunity. » – Leila Janah

What TIME Got Wrong

Continuous improvement and excellence are at the heart of everything we do, and we invite anyone, in and outside of the company, to help us on our journey.

Sama by the Numbers

Sama is committed to providing our workforce with professional development and upskilling opportunities, benefits, and a living wage.

Autonomous Vehicles’ Impact on Cities with Lyft’s Sarah Barnes

A future filled with autonomous vehicles promises to be a driving utopia. Maximum efficiency navigation decreasing traffic and congestion, safety features that drastically reduce collisions with other cars, bikes, or pedestrians, and an electric-first approach that lowers greenhouse gas emissions. But as with all disruptive technologies, the journey to functional autonomous driving future isn’t a ... Read more

Orbisk is Using Accurate AI to Help Restaurants Reduce Food Waste Up to 70%

Orbisk is focused on limiting the amount of food waste produced in restaurants, hotels, and cafes with their AI-powered food waste monitoring solution.

Sama’s Experiment-Driven Approach to Solving for High-Quality Labels at Scale

A recent academic paper outlines how Sama uses Experiment-Driven Development to measure how improvements made to our platform increase annotation efficiency.

Accurate Data Labeling Powers the Volumental Shoe Recommendation App — Helping Retailers Convert Mobile Customers

Learn how Volumental partnered with Sama to accurately label the datasets that fuel the computer vision technology for their mobile foot scanning app.

Facebook’s Manohar Paluri Makes Machines See

Manohar Paluri has spent the bulk of his career developing methods to make machines see. Now, in his role as Director, Artificial Intelligence at Facebook (now Meta), computer vision is one building block in the massive undertaking of developing egocentric perception: making sense of data collected from a first-person perspective via wearable devices. Mano joined ... Read more

AI in Retail: Labeling Challenges and Success Stories

As AI technology continues to transform the world around us, consumer expectations are shifting. Advances in AI for retail are transforming shopping experiences both in store and online. Join Jerome Pasquero, Product Manager at Sama for this session covering labeling challenges for AI in Retail. In this session, we’ll have a look at AI use ... Read more

Moxie the Conversational AI Robot Teaches Children Kindness

When you were a child, do you remember learning what it means to be kind? What about reading sadness in someone’s face, understanding the anger you felt, or respecting personal space? If you don’t remember learning about any of these human moments, then Moxie, a conversational AI robot built by Embodied, is the android friend ... Read more

How More Accurate Data Labeling is Helping PolyPerception Advocate for Responsible Waste Management

Find out how accurately labeled data is helping PolyPerception provide material recovery facilities with better visibility into their waste streams.

Representation in AI with Walmart Global Tech Leaders Anshu Bhardwaj & Desirée Gosby

Walmart's SVP of Global Technology Anshu Bhardwaj and VP of Emerging Technology Desirée Gosby join Sama CEO Wendy Gonzalez for a roundtable discussion about representation in AI, explainable & ethical AI, and how representative teams are a key way to reduce biases in AI technology. Stream the full episode below, or head here to select ... Read more

ML Assisted Annotation Powered by MICROMODEL Technology

ML Assisted Annotation can help you generate high-quality pre-labeled and human-assisted annotations, for predictably higher quality data in half the time.

How Sama’s Accurate AI is Helping Blind Runners Run Independently

Project Guideline by Google partnered with Sama to help people who are blind run without a guide, using only a smartphone, headphones, and a yellow guideline.

Innovation Week: How Sama Builds a Culture of Experimentation

Sama’s third annual Innovation Week is coming to a close, and once more, our teams have given us plenty to be excited about.

RCT Results from MIT: Evaluating the Impact of Sama’s Training and Job Programs

This week, researchers at MIT released a white paper evaluating Sama’s impact through a three-year Randomized Controlled Trial study. Here are their findings.

Challenges & Solutions for 3D LiDAR Annotation & 3D Data Sets

In this webinar, discover the challenges & solutions to 3D LiDAR annotation & 3D data sets for solving autonomous driving or driver assistance.

Experts Explain: How to Think About Human-Centered Machine Learning

We asked experts working in the field about their thoughts on the role of humans in Machine Learning, and humans and the future of ML. 

How to Define and Measure Your Training Data Quality

How do you define training data quality and measure it? How do you improve it? We go into defining, measuring, and reviewing your training data quality.

10 Experts on the Biggest Roadblocks to Bringing ML Models to Production

87% of AI projects will never make it into production. Why? We asked ML experts.

Sama’s Gold Tasks: ML Training Data with Gold-Standard Quality

Sama is an expert in efficiently designing annotation guidelines that enhance data quality. Gold Tasks refer to tasks that have been annotated perfectly.

Supercharge Your Data Quality with Automated Quality Accelerators

Automated quality accelerators are technology innovations that are focused on reducing the amount of manual quality assurance time spent in QA processes.

10 Experts Give Reasons Why High-Quality Training Data is so Important

We reached out to various ML experts, asking them the questions: Why is high-quality training data so important? Why do so many projects fail in ML?

What’s next? 17 Machine Learning Predictions for 2021

2021 Predictions: We asked a range of ML experts about what they believe will be the next big thing in AI and Machine Learning.

10 Must-Read Machine Learning Books

There’s no shortage of literature about ML, but we’ve compiled a list of 10 must-read books to add to your list!

Custom Keypoint Shapes for Vector Image & Video Annotation

Announcing our support for custom keypoint shapes in our training data platform trusted by the world’s leading AI teams, for vector image and video annotation.

12 Women in Machine Learning to Watch

Here’s a celebratory list of some of the women we look up to and have spearheaded development in AI and Machine Learning in 2020.

What Are the Types of Machine Learning?

We explain the various types of machine learning algorithms including supervised, unsupervised and reinforcement learning, as well as business use cases.

The Traffic Light Problem for Autonomous Vehicles

The traffic light problem for autonomous vehicles is critical for all vehicle safety, and unlike human-drivers, AVs rely solely on computer vision systems to navigate the world around us.

Object Tracking with Frame Level-Labeling

Sama video and 3D object tracking with frame-level labeling assists companies in quickly building models that better reflect real-world behavior.

8 Answers to Your Questions About AI and Machine Learning

In this interview, we chat with Head of AI at Sama about AI trends to expect in 2020, as well as frequently asked questions about AI and machine learning.

Computer Vision Insights From Around the Web

This list of computer vision insights shares how artificial intelligence is learning to understand and relate to the intensely visual world around us.

What’s Holding Back Artificial Intelligence?

Data isn’t the only thing holding back artificial intelligence. Read more about some of the challenges and trends in AI.

Revamped 2D Vector Segmentation

Sama’s revamped toolset for 2D image vector segmentation is ideal for computer vision projects using vector shapes to structure training data.

13 Open Source Datasets for Machine Learning

13 open source datasets for machine learning, including one dataset featured in the Fine-Grained Visual Categorization (FGVC) workshop at CVPR 2019.

Moving Toward Level 4 Autonomous Driving

Kirk Boydston, Training Data Specialist at Sama shares five considerations to move your machine learning model toward level 4 autonomous driving.

Training Your AI in 3D

Today, we’re announcing the production availability of our new 3D annotation engine for the Sama.

Introducing Object Tracking with Video Annotation

Today, Sama announces the availability of our latest image annotation toolset for advanced video object tracking.

Machine Learning 101

In this post, we’ll present a simple overview of machine learning and how it helps computers solve complex problems.

What is Data Collection and Why Do You Care?

Data collection is a systematic strategy for gathering and measuring information from a variety of sources to get an accurate picture about a specific area of interest.

Takeaways from AutoAI Conference 2018

Last week, Sama visited the Auto.AI event, which bills itself as the platform bringing together the stakeholders who play an active role in the deep driving, computer vision, and sensor fusion.

The Advantages and Limitations of Synthetic Data

With increased buzz around synthetic data, it is important to understand the advantages and limitations of this solution, and the overall affect on the application.

What is Synthetic Data?

Synthetic data is system-generated data that mimics real data in terms of essential parameters set by the user.

What is Training Data?

The best way for a computer to gain knowledge is to start by showing it exactly what it is you want it to do. For this, we use training data.

Winning Customers with Algorithms: How Teams in Nairobi Help Shape Your Shopping Experience

Termed “planogramming,” visual merchandising is key in retail stores. The best stores find a balance between exciting customers without overwhelming them with deals shouting from every corner.

Des données de formation de haute qualité, du début à la fin.