19Aug

Impact Outsourcing Limited Reinforces Commitment to Information Security with New ISO/IEC 27001-Aligned Policy

Impact OutsourcingConsultation, Leadership
Press Release — Impact Outsourcing Limited, a leading provider of AI-driven data annotation and machine learning services, has announced the launch of its robust Information Security Policy, aligned with the globally recognized ISO/IEC 27001 standard. This move underscores the company’s unwavering dedication to safeguarding the confidentiality, integrity, and availability of information for all its stakeholders.

A Comprehensive Approach to Information Security

At Impact Outsourcing Limited, information security is not just a priority—it’s a cornerstone of the company’s operational strategy. The newly introduced policy is designed to meet the stringent requirements of ISO/IEC 27001, a standard that sets the framework for an effective Information Security Management System (ISMS). This policy is crafted to ensure that all legal, regulatory, and contractual obligations are met, thereby fortifying the trust placed in the organization by its diverse clientele, suppliers, and governmental bodies.

Strategic Implementation of Security Objectives

In adherence to the new policy, Impact Outsourcing Limited has implemented a set of Information Security Objectives tailored to address specific security needs as determined by rigorous risk assessments. These objectives are not static; they are regularly evaluated and refined to reflect the evolving landscape of information security threats and challenges. The organization actively communicates these objectives and its performance in achieving them to all interested parties, ensuring transparency and accountability at every level.

Collaboration with Stakeholders to Elevate Security Standards

Understanding that information security extends beyond internal protocols, Impact Outsourcing Limited places significant emphasis on collaborating with customers, business partners, and suppliers. The company works closely with these external stakeholders to establish and uphold appropriate information security standards, fostering a secure ecosystem where data is protected across the entire supply chain.

Proactive Risk Management and Continuous Improvement

Impact Outsourcing Limited adopts a forward-thinking approach to business decisions, particularly in relation to risk evaluation and management. The company’s policy includes a commitment to continual improvement initiatives, which encompass ongoing risk assessments and the implementation of risk treatment strategies. This proactive stance not only ensures compliance with current security requirements but also positions the organization to anticipate and mitigate future risks effectively.

Empowering Employees through Security Awareness

The success of any Information Security Management System hinges on the active participation of all employees. Recognizing this, Impact Outsourcing Limited has made it a priority to instruct every member of its staff on the importance and responsibilities of information security management. Through regular training sessions and internal communications, the company fosters a culture of security awareness, where each employee is equipped to contribute to the organization’s overall security posture.

Leadership Commitment to Upholding Security Standards

The responsibility for upholding the Information Security Policy is a company-wide endeavor, spearheaded by Evanson Waweru, the Chief Technology Officer at Impact Outsourcing Limited. Under his leadership, the company has cultivated an environment where information security is integrated into every aspect of the business. Mr. Waweru’s personal commitment to this cause inspires all employees to take an active role in maintaining the highest standards of security in their daily operations.

About Impact Outsourcing Limited

Impact Outsourcing Limited is a premier provider of AI-driven data annotation and machine learning services, catering to a global clientele across various industries. The company specializes in delivering high-quality, scalable solutions that empower businesses to harness the full potential of artificial intelligence and machine learning technologies. With a strong focus on information security, Impact Outsourcing Limited is committed to maintaining the highest standards of confidentiality, integrity, and availability of data, ensuring that its clients’ sensitive information is always protected.

10Jun

Image Annotation for Machine Learning?

adminLeadership

Training of drones, autonomous vehicles, and other computer-vision based models needs annotated images and videos so that the machines can identify and interpret the object without much human intervention. The data which is fed in these machine algorithms to understand images and videos, text or audio created the need for the annotations.

Majorly, image and video annotation are widely used. However, the process of annotating is almost the same but video annotation needs more precision and accuracy and it is a bit difficult because of the movement of the target object i.e. the target object continuously moves in a video so it is slightly difficult to annotate the videos as it needs specialization and experience.

Image Annotation

Image annotation is one of the basic tasks to train the machines or computers to interpret and identify the visual world. Images annotated by the annotators are used to train machine learning algorithms which helps them to identify the objects that are given in the image. This gives computers the ability to see and identify the things as humans do.

Image annotation means selecting the given objects in the image and labelling them by their names. It helps machines to recognize things/objects so that they can make correct decision without any human intervention. For example, if a cat needs to be annotated then, that cat in the image will be marked and labelled as a cat and this data is fed into an algorithm to train the machine so that next time the machines can automatically recognize the object.

Pixel accurate image annotations

Based on algorithms there are several types of annotations. Few are:

  • Bounding box annotation
  • Polygon annotation
  • Semantic annotation
  • Key point annotation
  • 3D point cloud annotation
  • Landmark annotation

The most commonly used image annotation is the bounding box in which rectangle boxed are placed or marked around the target object. However, this has some major issues:

1. One needs a huge number of bounding boxes to reach over 95% detection accuracies.

2. This technique does not allow perfect detection regardless of how much data you use.

3. The detection becomes extremely complicated for obstructive objects.

The future

All these issues which are mentioned above can be solved with a pixel-accurate annotation. For example, pixel level accuracy is of utmost importance is the medical field where machine learning models requires high level of precision and accuracy for the model to make sound judgment and deliver accurate results. Machine Learning Projects in Medical space are highly sensitive and depends significantly upon accuracy of the data being fed into them. Even minor inaccuracies in the medical machine learning data could be detrimental for the entire operations and could lead to disastrous results. Hence, this is where pixel-accurate annotations plays a huge part in keeping it together. And a lot of it depends upon the quality of the images and datasets.

Yet, the most commonly used tools are majorly dependent on point-by-point object selection, which is time-consuming and costly too. Pixel-accurate annotations have a huge advantage to aerial imagery as well. However, the tools for such annotations depend on the slow point-by-point annotation. As a result the time taken to complete the task is way too much and the results are also sensitive to human errors. To train an algorithm to identify the roof types in the satellite images, annotator needs to annotate thousands to millions of images of roofs in different cities, weather conditions, etc and when the image is not accurate and gets there timely then the technology and the output will suffer because the quality of image plays a crucial role in the annotation.

However, there are researches that have helped in reducing the impact of image quality. Addressing this problem, the research community have made efforts towards creating more efficient pixel-accurate annotation methods. The community is developing many exciting pre-processing algorithms that we can use to improve image quality and ensure better quality segmentation.

A company whose competitive advantage depends on accurate image annotation can reach Analytics as we are delivering best-in-class image annotation services with several others. The professionals in analytics have several years of technical experience in using machine learning and artificial intelligence technologies to develop projects in healthcare, retail, autonomous flying, self-driving, agriculture, robotics and among others. Here one will get the utmost satisfaction to meet your requirements at affordable pricing.