How does a car make split-second decisions such as switching between real-time traffic updates, adaptive breaking, or lane assist correctional steering? There is a growing need for such techniques that allow cars to operate autonomously while managing dynamic, control, and safety challenges. This becomes more paramount amidst the growing demand for safer, smarter, and more connected vehicles and the increasing adoption of autonomous driving features.
What is Arm Split, Lock, and Hybrid Mode?
Automotive systems like Advanced Driver Assistance Systems (ADAS), Automated Driving Systems (ADS), and In-vehicle Infotainment (IVI) need to process large volumes of data quickly all while maintaining multiple levels of safety integrity. Balancing these requirements is crucial for vehicles, particularly with ongoing computing challenges around performance, power, and area.
Designed to make tomorrow’s driving exhilarating, safe, and convenient, Arm’s Split, Lock, and Hybrid processing modes offer the versatility needed to support various automotive safety levels, further enabling automakers to develop vehicles that are safe, powerful, and adaptable.
Arm’s Split, Lock, and Hybrid modes offer a comprehensive solution by enabling a single silicon design to operate flexibly in different modes tailored to specific safety and performance needs. This versatility allows automakers and Tier 1 suppliers to deploy the same hardware across a wide range of safety-critical automotive use cases.
Split, Lock, and Hybrid Modes Use cases and examples
Split Mode: Maximizing Performance
Use case example: A vehicle’s IVI system handles many non-safety critical tasks, such as playing music, providing navigation directions, and managing cabin temperature, all while maintaining a seamless driver experience.
How Split Mode Delivers: In Split mode, processor cores operate independently, delivering maximum performance when handling demanding applications. This enables high throughput in applications where rapid response and high data processing are critical, such as running multimedia, navigation, communication features, high-end graphics, and fast processing. This mode is perfect for scenarios where speed and efficiency are crucial, and safety isn’t the primary concern.
Lock Mode: Uncompromising Safety: ASIL D
Use case example: Various ADAS features are used when driving through dense fog on a busy highway. The vehicle will actively scan the environment, anticipate potential hazards, deploy traction control, and assist with steering and even brakes.
The vehicle system processes obstruction data, calculates risks, and automatically steers the driver away from the hazard, or applies emergency braking, protecting them from a potential accident. It is imperative that these systems are fail-safe proof in life-threatening situations.
How Lock Mode Ensures Safety: Lock mode is engineered for the most stringent safety-critical applications, such as ADAS L2+ features, where system failure could have life-threatening consequences. In this mode, processor cores operate in pairs, with the Arm DynamIQ Shared Unit (DSU) logic and memory operating in lockstep, ensuring a redundant operation that enables fail-safe execution. This redundancy is essential for systems requiring the highest safety standards, like ASIL D/SIL 3, which govern critical functions such as automatic braking and collision avoidance and acts as a countermeasure for security threat.
Hybrid Mode: The Balanced Solution for ASIL B
Use case example: Hybrid mode works harmoniously to optimize power consumption while maintaining essential safety measures, ensuring a smooth driving experience without compromising reliability or control. In moderate-risk scenarios, such as maintaining a safe distance from other vehicles using adaptive cruise control or managing energy efficiently, hybrid mode ensures that critical functions operate harmoniously to enhance your driving experience without unnecessary power consumption or safety compromises.
How Hybrid Mode Balances: Hybrid mode is engineered with balanced performance and safety in mind. In this mode, cores operate independently, and the DSU logic operates in lockstep. This allows for some redundancy and safety features while maintaining a higher level of performance and efficiency compared to full lockstep. For mid-tier safety features such as lane departure warnings or energy management in electric vehicles (EVs) that only need ASIL B/SIL 2, Hybrid mode coupled with Software test libraries (STLs) provides a balance between Availability, safety, and performance.
Paving the Way for Automotive Excellence
Arm’s Split, Lock, and Hybrid modes are more than just a technical term—they are the key to unlocking the future of automotive innovation. By offering flexible, high-performance, and safety-conscious solutions, Arm is the best choice of foundational platform to build the future of automotive with safe and reliable vehicles.
Containers might seem like a relatively recent technological breakthrough, but their origins trace back to the 1970s when Unix systems first used container-like concepts to isolate applications. Fast-forward to 2013, and Docker revolutionized this idea by introducing a portable, user-friendly container platform, sparking widespread adoption. In 2015, Docker was instrumental in creating the Open Container Initiative (OCI) to promote open standards within the container ecosystem. With the stability provided by the OCI, container technology spread throughout the tech world.
Although Docker Desktop is the leading tool for creating containerized applications, Docker remains surrounded by numerous misconceptions. In this article, we’ll debunk the top Docker myths and explain the capabilities and benefits of this transformative technology.
Myth #1: Docker is no longer open source
Docker consists of multiple components, most of which are open source. The core Docker Engine is open source and licensed under the Apache 2.0 license, so developers can continue to use and contribute to it freely. Other vital parts of the Docker ecosystem, like the Docker CLI and Docker Compose, also remain open source. This allows the community to maintain transparency, contribute improvements, and customize their container solutions.
Docker’s commitment to open source is best illustrated by the Moby Project. In 2017, Moby was spun out of the then-monolithic Docker codebase to provide a set of “building blocks” to create containerized solutions and platforms. Docker uses the Moby project for the free Docker Engine project and our commercial Docker Desktop.
Docker is a founder and remains a crucial contributor to the OCI, which defines container standards. This initiative ensures that Docker and other container technologies remain interoperable and maintain a commitment to open source principles.
Myth #2: Docker containers are virtual machines
Docker containers are often mistaken for virtual machines (VMs), but the technologies operate quite differently. Unlike VMs, Docker containers don’t include an entire operating system (OS). Instead, they share the host operating system kernel, making them more lightweight and efficient. VMs require a hypervisor to create virtual hardware for the guest OS, which introduces significant overhead. Docker only packages the application and its dependencies, allowing for faster startup times and minimal performance overhead.
By utilizing the host operating system’s resources efficiently, Docker containers use fewer resources overall than VMs, which need substantial resources to run multiple operating systems concurrently. Docker’s architecture efficiently runs numerous isolated applications on a single host, optimizing infrastructure and development workflows. Understanding this distinction is crucial for maximizing Docker’s lightweight and scalable potential.
However, when running on non-Linux systems, Docker needs to emulate a Linux environment. For example, Docker Desktop uses a fully managed VM to provide a consistent experience across Windows, Mac, and Linux by running its Linux components inside this VM.
Myth #3: Docker Engine vs. Docker Desktop vs. Docker Enterprise Edition — They’re all the same
Considerable confusion surrounds the different Docker options that are available, which include:
Mirantis Container Runtime: Docker Enterprise Edition (Docker EE) was sold to Mirantis in 2019 and rebranded as Mirantis Container Runtime. This software, which is managed and sold by Mirantis, is designed for production container deployments and offers a lightweight alternative to existing orchestration tools.
Docker Engine: Docker Engine is the fully open source version built from the Moby Project, providing the Docker Engine and CLI.
Docker Desktop: Docker Desktop is a commercial offering sold by Docker that combines Docker Engine with additional features to enhance developer productivity. The Docker Business subscription includes advanced security and governance features for enterprises.
All of these variants are OCI-compliant, differing mainly in features and experiences. Docker Engine caters to the open source community, Docker Desktop elevates developer workflows with a comprehensive suite of tools for building and scaling applications, and Mirantis Container Runtime provides a specialized solution for enterprise production environments with advanced management and support. Understanding these distinctions is crucial for selecting the appropriate Docker variant to meet specific project requirements and organizational goals.
Myth #4: Docker is the same thing as Kubernetes
This myth arises from the fact that both Docker and Kubernetes are associated with containerized environments. Although they are both key players in the container ecosystem, they serve different roles.
Kubernetes (K8s) is an orchestration system for managing container instances at scale. This container orchestration tool automates the deployment, scaling, and operations of multiple containers across clusters of hosts. Other orchestration technologies include Nomad, serverless frameworks, Docker’s Swarm mode, and Apache Mesos. Each offers different features for managing containerized workloads.
Docker is primarily a platform for developing, shipping, and running containerized applications. It focuses on packaging applications and their dependencies in a portable container and is often used for local development where scaling is not required. Docker Desktop includes Docker Compose, which is designed to orchestrate multi-container deployments locally
In many organizations, Docker is used to develop applications, and the resulting Docker images are then deployed to Kubernetes for production. To support this workflow, Docker Desktop includes an embedded Kubernetes installation and the Compose Bridge tool for translating Compose format into Kubernetes-friendly code.
Myth #5: Docker is not secure
The belief that Docker is not secure is often a result of misunderstandings around how security is implemented within Docker. To help reduce security vulnerabilities and minimize the attack surface, Docker offers the following measures:
Opt-in security configuration
Except for a few components, Docker operates on an opt-in basis for security. This approach removes friction for new users, but means Docker can still be configured to be more secure for enterprise considerations and for security-conscious users with sensitive data.
“Rootless” mode capabilities
Docker Engine can run in rootless mode, where the Docker daemon runs without root permissions. This capability reduces the potential blast radius of malicious code escaping a container and gaining root permissions on the host. Docker Desktop takes security further by offering Enhanced Container Isolation (ECI), which provides advanced isolation features beyond what rootless mode can offer.
Built-in security features
Additionally, Docker security includes built-in features such as namespaces, control groups (cgroups), and seccomp profiles that provide isolation and limit the capabilities of containers.
SOC 2 Type 2 Attestation and ISO 27001 Certification
It’s important to note that, as an open source tool, Docker Engine is not in scope for SOC 2 Type 2 Attestation or ISO 27001 Certification. These certifications pertain to Docker, Inc.’s paid products, which offer additional enterprise-grade security and compliance features. These paid features, outlined in a Docker security blog post, focus on enhancing security and simplifying compliance for SOC 2, ISO 27001, FedRAMP, and other standards.
Along with these security measures, Docker also provides best practices in the Docker documentation and training materials to help users learn how to secure their containers effectively. Recognizing and implementing these features reduces security risks and ensures that Docker can be a secure platform for containerized applications.
Myth #6: Docker is dead
This myth stems from the rapid growth and changes within the container ecosystem over the past decade. To keep pace with these changes, Docker is actively developed and is also widely adopted. In fact, the Stack Overflow community chose Docker as the most-used and most-desired developer tool in the 2024 Developer Survey for the second year in a row and recognized it as the most-admired developer tool.
Docker Hub is one of the world’s largest repositories of container images. According to the 2024 Docker State of Application Development Report, tools like Docker Desktop, Docker Scout, Docker Build Cloud, and Docker Debug are integral to more than two-thirds of container development workflows. And, as a founding member of the OCI and steward of the Moby project, Docker continues to play a guiding role in containerization.
In the automation space, Docker is crucial for building OCI images and creating lightweight runners for build queues. With the rise of data science and AI/ML, Docker images facilitate the exchange of models, notebooks, and applications, supported by GPU workload capabilities in Docker Desktop. Additionally, Docker is widely used for quickly and cost-effectively mocking up test scenarios as an alternative to deploying actual hardware or VMs.
Myth #7: Docker is hard to learn
The belief that Docker is difficult to learn often comes from the perceived complexity of container concepts and Docker’s many features. However, Docker is a foundational technology used by more than 20 million developers worldwide, and countless resources are available to make learning Docker accessible.
The thriving Docker community also contributes a wealth of content and resources, including video tutorials, how-tos, and in-person talks.
Myth #8: Docker and container technology are only for developers
The idea that Docker is only for developers is a common misconception. Docker and containers are used across various fields beyond development. Docker Desktop’s ability to run containerized workloads on Windows, macOS, or Linux requires minimal technical knowledge from users. Its integration features — synchronized host filesystems, network proxy support, air-gapped containers, and resource controls — ensure administrators can enforce governance and security.
Data science: Docker provides consistent environments, enabling data scientists to share models, datasets, and development setups seamlessly.
Healthcare: Docker deploys scalable applications for managing patient data and running simulations, such as medical imaging software across different hospital systems.
Education: Educators and students use Docker to create reproducible research environments, which facilitate collaboration and simplify coding project setups.
Docker’s versatility extends beyond development, providing consistent, scalable, and secure environments for various applications.
Myth #9: Docker Desktop is just a GUI
The myth that Docker Desktop is merely a graphical user interface (GUI) overlooks its extensive features designed to enhance developer experience, streamline container management, and accelerate productivity, such as:
Cross-platform support
Docker is Linux-based, but most developer workstations run Windows or macOS. Docker Desktop enables these platforms to run Docker tooling inside a fully managed VM integrated with the host system’s networking, filesystem, and resources.
Developer tools
Docker Desktop includes built-in Kubernetes, Docker Scout for supply chain management, Docker Build Cloud for faster builds, and Docker Debug for container debugging.
Myth #10: Docker containers are for microservices only
Although Docker containers are popular for microservices architectures, they can be used for any type of application. For example, monolithic applications can be containerized, allowing them and their dependencies to be isolated into a versioned image that can run across different environments. This approach enables gradual refactoring into microservices if desired.
Additionally, Docker is excellent for rapid prototyping, allowing quick deployment of minimum viable products (MVPs). Containerized prototypes are easier to manage and refactor compared to those deployed on VMs or bare metal.
Now you know
Now that you have the facts, it’s clear that adopting Docker can significantly enhance productivity, scalability, and security for a variety of use cases. Docker’s versatility, combined with extensive learning resources and robust security features, makes it an indispensable tool in modern software development and deployment. Adopting Docker and its true capabilities can significantly enhance productivity, scalability, and security for your use case.
Intel engineers today released OpenVINO 2024.4 as the newest version of their open-source AI toolkit. OpenVINO 2024.4 prepares for upcoming Intel Core Ultra Series 2 "Lunar Lake" processors, supports newer Gen AI models, now supports Python 3.12, and finally adds official support for Red Hat Enterprise Linux 9...
SparkFun has released a new air quality multi-sensor board, the Indoor Air Quality Combo Sensor, which integrates the SCD41 and SEN55 sensors from Sensirion for measuring carbon dioxide, volatile organic compounds (VOCs), particulate matter, relative humidity, and temperature. The air quality multi-sensor board simplifies power management for the two sensors via onboard DC voltage conversion and allows a single Qwiic connection for power and communication. It features two Qwiic connectors and a 0.1”-space through-hole header for I2C and power. The board is not a complete solution for indoor air quality monitoring. It has to be connected to a Qwiic-enabled microcontroller such as SparkFun Thing Plus Matter, DataLogger IoT, and the ESP32 Qwiic Pro Mini. Users can install the required Arduino libraries — the Arduino Core library, Sensirion I2C SEN5x, and SparkFun SCD4x — either via the Arduino library manager or directly from SparkFun. The device is open-source, with hardware files, [...]
Team Ikaro is a vibrant group of high school students from the Pacinotti Archimede Institute in Rome, sharing a strong passion for electronics and turning heads in the world of robotics! Specializing in Soccer Lightweight games (where robot-soccer players compete to score goals on a miniature field), they clinched the first place at the Romecup 2024 and won Italy’s national Robocup in Verbania earlier this year – earning the right to compete in the world championships in Eindhoven, where they placed third in the SuperTeam competition.
The brains behind the bots
Utilizing the versatile Arduino Nano RP2040 Connect, the team has crafted highly efficient robots that feature ultrasound sensors, PCB boards, a camera, four motors, a solenoid kicker and omni-directional wheels, all meticulously assembled in the school’s FabLab.
Mentored by professor Paolo Torda, Team Ikaro exemplifies the spirit of innovation and teamwork bringing together three talented students: Francesco D’Angelo, the team leader, focuses on system design and mechanics; Flavio Crocicchia, the software developer, ensures the robots’ brains are as sharp as possible; Lorenzo Addario specializes in camera software, making sure the robots can “see” and react swiftly on the field. Their combined efforts have led to a seamless integration of hardware and software, and established a foundation of passion and ambition for future success in their careers.
Future goals
After their first taste of global competition, Team Ikaro is determined to continue refining their robots, leveraging every bit of knowledge and experience they gain – whether in the classroom, lab, or live challenges. At Arduino, we are proud to sponsor such brilliant young minds and look forward to seeing what they will accomplish next!
The Microsoft Hyper-V updates have been submitted for the Linux 6.12 kernel. They are mostly small changes but does bring work to optimize the boot time for large Hyper-V VMs...
The XFS file-system changes have been merged for the Linux 6.12 kernel and introduce new ioctls for being able to exchange the contents of two files...
AAEON GENE-ASL6 is an Intel Atom x7000RE-series Amston Lake powered 3.5-inch single board computer (SBC) featuring three 2.5GbE RJ45 ports and three independent display outputs via HDMI, LVDS, and VGA. The GENE-ASL6 can be configured with up to 16GB of LPDDR5 memory and supports a variety of storage options including SATA, mSATA, and M.2 NVMe SSD options. There is also an M.2 2230 E-Key slot for Wi-Fi and Bluetooth connectivity and the other M.2 3052 B-Key slot can be used for storage or to connect 4G or 5G modules. Other than that, this board features a variety of connectivity options including USB 3.2 Gen 2 ports, serial ports, GPIO, SMBus/I2C, optional audio header, and much more. AAEON GENE-ASL6 3.5-inch industrial SBC specification Amston Lake SoC (one or the other) Intel Atom x7213RE dual-core processor @ 2.0 to 3.4 GHz with 6MB cache, 16EU Intel UHD graphics; 9W TDP Intel Atom x7433RE [...]
For anyone still relying upon virtual reality (VR) applications written for the OpenGL API rather than the Vulkan API that has been dominant among VR apps (and other modern games / software) for years, the Mesa code and in particular the Zink OpenGL-on-Vulkan driver now supports the OpenGL VR (OVR) extensions...
An interesting merge request opened this week and already merged for Mesa 24.3 comes from an Autodesk engineer adding Vulkan Windowing System Integration (WSI) around Apple's Metal API for use on macOS...
With roots in Africa, the kalimba is a type of hand piano featuring an array of keys that are each tuned for a specific note, and upon plucking or striking one, a pleasant xylophone-like sound can be heard. Taking inspiration from his mini kalimba, Axel from the YouTube channel AxelMadeIt sought to automate how its keys are struck and produce classical melodies with precision.
The design process started out with Axel determining the best mechanism for interacting with the small keys, and after hitting/plucking them using a range of objects, he settled on plucking individual keys with a small plastic actuator. Two servo motors were utilized to perform the action, with one motor sliding a gantry left-and-right, and the other moving a small plastic pick across the keys. Axel’s design underwent several iterations to get the sound correct since material thickness, the lack of a resonant backing, and a loud servo motor all contributed to reduced quality initially.
After perfecting the physical layout, Axel assembled the electronic components into a custom 3D-printed case, which includes spaces for the Arduino Nano, battery, charging circuit, and pushbuttons. The first two buttons cause the kalimba to play preprogrammed melodies, while the last one plays random notes with a random amount of delay in between.
The FFmpeg multimedia library continues to enhance its support around the Vulkan Video APIs with the latest commits seeing H.264 and H.265/HEVC Vulkan encode support merged...
Seeed Studio has launched a Kickstarter campaign for the SenseCAP Watcher, a physical AI agent capable of monitoring a space and taking actions based on events within that area. Described as the “world’s first Physical LLM Agent for Smarter Spaces,” the SenseCAP Watcher leverages onboard and cloud-based technologies to “bridge the gap between digital intelligence and physical applications.” The SenseCAP Watcher is powered by an ESP32-S3 microcontroller coupled with a Himax WiseEye2 HX6538 chip (Cortex-M55 and Ethos-U55 microNPU) for image and vector data processing. It builds on the Grove Vision AI V2 module and comes in a form factor about one-third the size of an iPhone. Onboard features include a camera, touchscreen, microphone, and speaker, supporting voice command recognition and multimodal sensor expansion. It runs the SenseCraft software suite which integrates on-device tinyML models with powerful large language models, either running on a remote cloud server or a local computer [...]
If you want to make PCBs at home and you don’t happen to own a CNC mill, then you’ll probably need to turn to chemical etching. Use one of several different techniques to mask the blank PCB’s copper that you want to keep, then toss the whole thing into a bath to dissolve away the unmasked copper. Unfortunately, the last step can be slow, which is why Chris Borge built this PCB agitator.
Alton Brown’s philosophy on “unitaskers” is wise when it comes to the kitchen, but things are different in the workshop. Sometimes a tool or machine is so useful that it is worth keeping around — even if it only does one job. That’s the case here, because Borge’s machine only does one thing: tilts back and forth. If a container with a PCB in an etchant bath is sitting on top of the machine, that action will slosh the chemicals around and the agitation will dramatically speed up the process.
On a mechanical level, this is extremely simple. It only requires a handful of 3D-printed parts, some fasteners, and a couple of bearings. The bearings provide a rotational interface between the stationary base (weighed down with poured concrete) and the pivoting platform. The electronics are even simpler and consist of an Arduino Nano board and a small hobby servo motor. The Arduino just tells the servo motor to move back and forth endlessly, tilting the platform and providing constant agitation.
For those wondering about the performance of the NVIDIA RTX 6000 Ada Generation workstation performance on Ubuntu 24.04 LTS with the up-to-date NVIDIA Linux graphics drivers now relying on the open-source kernel modules, this article is for you in looking at the performance of this high-end workstation graphics card on the up-to-date Linux software stack. The NVIDIA RTX 6000 Ada Generation is tested alongside the RTX 2000 / 4000 Ada Generation graphics cards and also the AMD Radeon PRO W7000 series competition atop Ubuntu 24.04 LTS.
Changing careers can be a major decision, especially in today’s fast-paced tech world. Professionals are spoilt for choices like for example a computer vision career. If you’re considering a shift and want to apply your current skills in a new and exciting direction, this guide is here to help.
This article is a step-by-step resource to show you how to make the transition, from understanding the basics of computer vision to building the necessary skills and portfolio to get started. With this guide, you’ll know exactly what steps to take to make the change successfully.
STEP1⃣: Identifying Transferable Skills for a Smooth Transition into Computer Vision
When transitioning to a career in computer vision, one of the most reassuring aspects is that many of the skills you’ve already developed can be useful. Let’s break down some key transferable skills:
Programming Skills: If you’re already familiar with languages like Python or C++, you’re on the right track. These languages are widely used in computer vision, particularly Python, due to libraries like OpenCV and TensorFlow. Even a basic understanding of coding can be a great starting point since many tutorials and projects will build on what you already know.
According to a survey from TealHQ, 60% of computer vision professionals come from a software engineering background, highlighting the demand for strong programming abilities.
Mathematical Foundation: Understanding concepts in linear algebra, calculus, and probability is vital in computer vision. These fields form the backbone of algorithms used in image recognition, object detection, and machine learning models. If you’ve ever worked with data analysis, finance, or engineering, you’ve likely applied these concepts already.
Don’t worry if you’re not an expert—there are plenty of beginner-friendly resources to help you brush up on the essentials.
Analytical Thinking: Problem-solving is at the core of computer vision. If you’ve worked in roles that required you to analyze data, troubleshoot, or think critically, you already have a valuable mindset. Computer vision often requires breaking down complex problems into smaller steps, which is very similar to tasks in other technical fields.
Domain Knowledge: One of the overlooked but important areas is domain-specific expertise. For example, if you have experience in healthcare, manufacturing, or transportation, your knowledge can help you apply computer vision solutions in those industries. Many employers look for candidates who can bring both technical skills and industry experience to the table.
STEP2⃣: Learning Resources and Courses for Beginners
Transitioning into computer vision requires learning new concepts and tools, but fortunately, there are numerous accessible resources to help you get started. Here are some beginner-friendly options:
Fast.ai: Practical Deep Learning for Coders – If you’re interested in the deep learning side of computer vision, this course offers real-world applications in a straightforward manner.
Books:
Deep Learning by Ian Goodfellow – A comprehensive resource for understanding the theory behind machine learning and computer vision.
Learning OpenCV by Gary Bradski and Adrian Kaehler – A practical guide focused on one of the most important libraries in computer vision, ideal for hands-on learners.
Websites and Blogs:
OpenCV.org – The official website for OpenCV is a treasure trove of tutorials, documentation, and community support.
Learnopencv.com – A blog filled with tutorials and practical guides on computer vision topics.
Towards Data Science – A popular platform where professionals share insights, tutorials, and cutting-edge research in the field.
STEP3⃣: Building a Computer Vision Portfolio from Scratch
One of the most important steps in your career transition is building a portfolio that demonstrates your skills. A strong portfolio shows potential employers that you can apply what you’ve learned to real-world problems. Here’s how to get started:
Start Small: Begin with basic projects, such as image classification or object detection. These foundational tasks are relatively simple but show your ability to work with computer vision tools and datasets. You can find plenty of tutorials and datasets online to guide you through your first projects.
Use Open Datasets: Data is key in computer vision, and thankfully, there are plenty of publicly available datasets. Websites like Kaggle and university repositories provide access to datasets ranging from simple images to complex 3D data. These datasets allow you to work on interesting problems while honing your skills.
Document Your Work: It’s crucial to showcase not only the results of your projects but also how you arrived at them. Platforms like GitHub or Hugging Face are excellent for sharing your code with the world. Write clear README files, explaining your approach, the tools you used, and the results you achieved. This documentation shows employers that you can explain and communicate your work, which is a highly valuable skill in any tech field.
Participate in Competitions: Getting involved in Kaggle competitions is another way to stand out. Competitions often present real-world challenges and give you the opportunity to apply your skills in a competitive environment. Many hiring managers look for candidates who have practical experience, and 70% of them prefer to see personal project portfolios when reviewing candidates, according to a LinkedIn survey.
Step4⃣: Networking and Job Search Strategies for Career Changers
Join Professional Networks: IEEE, ACM, and local AI meetups.
Attend Conferences: CVPR, ICCV, NeurIPS.
Leverage LinkedIn: Connect with professionals in the field and follow relevant groups and companies.
Job Search Tips:
Tailor your resume to highlight relevant skills.
Prepare for technical interviews with online platforms like LeetCode.
Consider internships or freelance projects to gain experience.
Summary and Next Steps
Transitioning into a computer vision career doesn’t have to be overwhelming. By focusing on your existing skills and leveraging the right resources, you can make this journey smoother and more manageable.
Here’s a quick recap of the steps to guide you forward:
Review Your Transferable Skills: Reflect on the programming, analytical, mathematical, and domain-specific knowledge you already possess. These can form a solid foundation as you move into computer vision.
Invest in Learning: Use beginner-friendly online courses, books, and other resources to build your expertise. Start with the basics and gradually explore more complex topics as you gain confidence.
Build a Portfolio: Start working on small, manageable projects, document your process, and share your work on platforms like GitHub or Hugging Face. A well-rounded portfolio will be critical when applying for jobs.
Network Effectively: Get involved in professional networks, attend industry conferences, and connect with professionals in the field. Building relationships and staying visible in the community will help open doors to job opportunities.
Accelerate Your Transition with Our Master Bundle – Make it Smoother!
If you’re looking for a structured and comprehensive way to fast-track your transition into computer vision, our Computer Vision + Deep Learning Master Bundle is the perfect choice. Tailored specifically for career changers, this bundle offers everything you need to build practical, industry-relevant skills in computer vision and deep learning.
Why Choose This Program?
Designed for Career Changers: The curriculum focuses on real-world applications, bridging the gap between your existing knowledge and the demands of computer vision roles.
Hands-On Learning: With projects and expert-led sessions, you’ll gain the practical experience that employers are looking for.
Supportive Community: Join a network of fellow learners and professionals who can provide guidance and support throughout your career transition.
Enroll Today: OpenCV University – CVDL Master Bundle. Start your journey toward a rewarding career in computer vision.
Not to be confused with the proposal a few days ago by an AMD engineer for Attack Vector Controls for broader control over CPU security mitigation handling, the in-development Linux 6.12 kernel is adding new Kconfig options to allow for more build-time control over what CPU security mitigation code is compiled for the kernel...
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