Improving Data Labeling Efficiency with Auto-Labeling, Uncertainty Esimates

In this post, we will be diving into the machine learning theory and techniques that were developed to evaluate our auto-labeling AI at Superb AI. More specifically, how our data platform estimates the uncertainty of auto-labeled annotations and applies it to active learning.

Before jumping right in, it would be useful to have some mental buckets into which the most popular approaches can be categorized. In our experience, most works in deep learning uncertainty estimation fall under two buckets. The first belongs to the category of Monte-Carlo sampling, having multiple model inferences on each raw data and using the discrepancy between those to estimate the uncertainty. The second method models the probability distribution of model outputs by having a neural network learn the parameters of the distribution. The main intention here is to give breadth to the kind of techniques we explored and hope to offer some clarity on how and why we arrived at our unique position on the subject. We also hope to effectively demonstrate the scalability of our particular uncertainty estimation method.

1. A quick review of the efficacy of Auto-labeling

Before we dive into the various approaches to evaluate the performance of auto labeling, there is a note of caution to be exercised. Auto-label AI, although extremely powerful, cannot always be 100% accurate. As such, we need to measure and evaluate how much we can trust the output when utilizing auto labeling. And once we can do it, the most efficient way to use auto-labeling is then to have a human user prioritize which auto-labeled annotation to review and edit based on this measure.

Measuring the “confidence” of model output is one popular method to do this. However, one well-known downside to this method is that confidence levels can be erroneously high even when the prediction turns out to be wrong if the model is overfitted to the given training data. Therefore, confidence levels cannot be used to measure how much we can “trust” auto-labeled annotations.

In contrast, estimating the “uncertainty” of model output is a more grounded approach in the sense that this method statistically measures how much we can trust a model output. Using this, we can obtain an uncertainty measure that is proportional to the probability of model prediction error regardless of model confidence scores and model overfitting. This is why we believe that an effective auto-label technique needs to be coupled with a robust method to estimate prediction uncertainty.

2. Method 1: Monte-Carlo Sampling

One possible approach to uncertainty estimation proposed by the research community is obtaining multiple model outputs for each input data (i.e. images) and calculating the uncertainty using these outputs. This method can be viewed as a Monte-Carlo sampling-based method.

Let’s take a look at an example 3-class classification output below.

y1 = [0.9, 0.1, 0]

y2 = [0.01, 0.99, 0]

y3 = [0, 0, 1]

y4 = [0, 0, 1]

Here, each of the y1 ~ y4 is the model output from four different models on the same input data (i.e. the first model gave the highest probability to class #1, etc.). The most naive approach would be using four different models to obtain these four outputs, but using Bayesian deep learning or dropout layers can give randomness to a single model and allow us to obtain multiple outputs from a single model.

#machine-learning #artificial-intelligence #deep-learning #active-learning #how-to

What is GEEK

Buddha Community

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

---

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

• Industrial metrology for quality assurance.
• 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data

What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

• ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
• ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data