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Containers and virtual machines (VMs) are virtualization technologies that enable the execution of applications in isolated environments. In other words, they are computing environments that combine various IT components and keep them separate from the rest of the system.

However, as an IT professional, you must be aware that there are significant differences between these two technologies. Therefore, it’s important to compare the pros and cons of containers vs. VMs to understand how to orchestrate these resources in your organization.

For instance, while virtual machines virtualize an entire machine, reaching down to the hardware layers, containers virtualize only the software layers above the operating system level.

Continue reading to better understand the differences between these two technologies and determine the best solution for your team’s needs!

💡Learn more: What is hyperautomation, and what are the benefits for complex operations?

Containers

A container consists of a package of software code that includes an application’s code, its libraries, and other dependencies. This makes applications portable and easy to deploy and manage, allowing the same code to run on any device.

This lightweight form of virtualization enables the execution of applications in isolated environments. In other words, containers are a good option for portable applications that don’t require many resources.

Pros of using containers:

Check out the advantages of using containers:

1. Portability: Containers can run on any platform that supports the container runtime. This makes them an attractive option for applications that need to run in various environments, such as public and private cloud environments;
2. Efficiency: They are more efficient than virtual machines (VMs) because they share the host operating system’s kernel. This means that containers require fewer resources from the host operating system, leading to cost savings;
3. Scalability: They can be easily scaled to meet load demands. Therefore, they are a good choice for applications that need to scale quickly;
4. Ease of Deployment and Management: Containers are easy to deploy and manage, making them a better fit for organizations without dedicated IT resources.

Cons of using containers:

Now, take a look at the disadvantages:

1. Security: Containers are less secure than VMs because they share the host operating system’s kernel. Therefore, a malicious container can impact other containers or the host operating system.
2. Isolation: The isolation between containers is not as strong as the isolation between VMs. As a result, a malicious container can access the resources of other containers.
3. Complexity: Containers can be complex to manage in large environments, potentially requiring the deployment of a container orchestration system.

Virtual Machines

A virtual machine is a digital copy of a physical machine that enables the execution of an entire operating system in an isolated environment. VMs are robust software packages, allowing the complete emulation of low-level hardware devices such as CPU, disk, and network devices.

When considering containers vs. VMs, keep in mind that VMs are a good option for applications that require many resources or need to be isolated from other applications, as well as those that need to run on a specific platform.

Pros of using virtual machines:

Among their advantages, we have:

1. Security: VMs provide complete isolation between operating systems, making them a more secure option than containers. This means that a malicious operating system in a VM cannot affect other operating systems or the host.
2. Isolation: The isolation between VMs is stronger than between containers. In other words, a malicious VM cannot access the resources of other VMs.
3. Resources: Virtual machines can provide more resources for applications than containers. Therefore, they are a suitable option for applications that require many resources, such as database or real-time data processing applications.
4. Portability: They can run on any platform that supports the hypervisor. This makes them well-suited for applications that need to run in various environments, including public and private cloud environments.

Cons of using virtual machines:

Finally, the disadvantages of virtual machines:

1. Performance: VMs may have lower performance than containers because they require more resources from the host operating system. This is because VMs need to run the hypervisor, which is additional software that consumes resources.
2. Complexity: They are more complex to manage than containers, as they require more configuration and maintenance.

Container and VM Orchestration

Orchestration is the process of managing and automating the deployment, scaling, and maintenance of containers and VMs.

Various orchestration systems are available, such as Kubernetes, Docker Swarm, and Nomad. They provide a variety of features for orchestrating containers and VMs, including:

1. Automation: Orchestration systems can automate common tasks, such as deploying containers and virtual machines.
2. Scalability: These systems can scale containers and VMs to meet load demands.
3. Monitoring: They can also monitor containers and VMs to ensure they are functioning correctly.

Containers vs. VMs: Can they be used together?

A common approach is to use containers to run applications that do not require many resources or need to be portable. VMs are used to run applications that require many resources or need isolation from other applications.

In this way, an organization can use containers to run specific web or backend applications. Simultaneously, VMs can be used to run database applications or real-time data processing applications that require more resources.

Another approach is to use containers within virtual machines. This can be done to achieve the isolation and security of VMs with the portability and efficiency of containers.

Containers vs. VMs: Understand your business needs!

Containers and VMs are powerful virtualization technologies that can be used to run applications in isolated environments. Choosing the right technology depends on the specific needs of the application.

As you have seen throughout this article, if the application is portable and does not require many resources, containers are a good choice. If the application requires many resources or needs to be isolated from other applications, VMs are the better choice.

Orchestration systems can help manage and automate the deployment, scaling, and maintenance of containers and VMs.

We hope this content has been helpful to you. Stay tuned to our blog and take the opportunity to check out the guide we’ve prepared on RPA in IT.

Parallel processing has emerged as an essential approach in response to the need to handle complex tasks and voluminous datasets simultaneously. Consequently, activities that can be performed in parallel represent an evolution of operations.

This enables significant advances in the speed, efficiency, and scalability of computational operations. In this article, we will explore everything from processor architecture to the main types of parallelism. Check it out now!

What is parallel processing?

Parallel processing occurs when two or more processors interact with each other to perform a task. In other words, it involves the simultaneous execution of multiple tasks to optimize efficiency and speed up processing time.

Parallel processing works by distributing the workload among various processors, allowing different parts of the same task to be executed simultaneously. This enhances performance compared to sequential processing.

This technology can be used in various applications, but it is particularly prevalent in those that handle large volumes of data. Parallel processing involves both hardware aspects (processors, memory, and machine architecture) and software aspects (programming languages, algorithms, process mapping, and distribution).

Flynn’s taxonomy architectures

Computer scientist Michael J. Flynn created a taxonomy in 1972 to classify parallel processors. The architecture contains two streams of information: data and instructions that are used to create the four architectures of the taxonomy. Check them out:

SISD (Single Instruction, Single Data)

SISD is the simplest processor model. In this model, a single instruction unit operates on a data stream. It was seen in early single-core computers.

MISD (Multiple Instruction, Single Data)

Multiple processing units that can read multiple instructions with a single data element. In other words, multiple instructions process the same data in parallel. Examples include pipeline and data-flow architectures.

SIMD (Single Instruction, Multiple Data)

In SIMD, one instruction is applied to multiple data simultaneously. Several processing units read a single instruction for different types of accessed data in each cycle. SIMD processors used in artificial intelligence are examples of SIMD processors.

MIMD (Multiple Instruction, Multiple Data)

Multiple instructions and multiple data are read and executed simultaneously. This is the most commonly used model in modern processors, such as multicomputers and multiprocessors.

Types of parallelism

Now that we understand what parallel processing is and its architecture, let’s explore the types of parallelism. Each type focuses on different aspects and levels. Check them out:

Data parallelism

Data parallelism involves the simultaneous execution of operations, but with data division. In this case, data is processed independently and can be achieved using SIMD (Single Instruction, Multiple Data).

Task and automation parallelism

Task parallelism involves the division of instructions that must be processed concurrently. In this case, there is a dependency between the processed data, and each task may involve the execution of multiple instructions.

Instruction parallelism

Instruction parallelism is the processor’s ability to execute multiple instructions simultaneously. It can be achieved through the pipeline technique, which divides the execution of instructions into several sequential stages.

Advantages of parallel processing

Explore the main advantages of parallel processing:

Enhanced performance

One of the key advantages of parallel processing is the improvement in system performance. With this technology, multiple processors can operate in parallel to support a greater workload. Several tasks can be executed simultaneously, resulting in faster completion times for complex tasks.

Resource utilization

Parallel processing allows for more efficient handling of complex tasks and large volumes of data. Different processors or cores can execute tasks and subtasks simultaneously, enabling faster task completion and increased overall processing capacity. Correct utilization of parallel processing can lead to cost reduction as tasks complete more quickly, resulting in less hardware usage.

Fault tolerance

Another advantage of parallel processing is increased system reliability, and critical operations can be performed more securely. In parallel processing, the failure of one processor does not necessarily affect all processes, enhancing system reliability.

Parallel processing of automations with Python

The Python programming language is extremely flexible and versatile, allowing for the programming of parallel automations in various environments, systems, and infrastructures. From web applications to desktop and legacy systems, Python can efficiently create and manage parallel automations.

BotCity, an expert in Robotic Process Automation (RPA), offers an Open-Source RPA software for creating and managing code-based robots, primarily developed in Python. When choosing how automation should be executed during deployment, it is possible to connect it to more than one runner.

This means that programmed robots can execute various tasks in parallel in different environments, such as virtual machines, containers, or in the cloud. This makes the automation process of your company much more agile and efficient.

Ready to use parallel processing?

We hope this article has clarified your main questions about parallel processing. Distributing tasks across multiple processors operating simultaneously can optimize efficiency and speed up the execution of complex operations.

If you are looking for an RPA solution to automate and accelerate your company’s processes, it is worth exploring BotCity, a platform known for its flexibility in Python automations.

Feel free to schedule a conversation with our experts or, if you want to try BotCity on your own, create a free account!

A research conducted by IBGE (Brazilian Institute of Geography and Statistics) revealed that, in 2022, 84.7% of medium and large-sized companies use advanced digital technology in their operations, with cloud computing (73.6%) being the most prominent, followed by the Internet of Things (48.6%).

This data shows that the business landscape has become increasingly dependent on automation models and various algorithms. For companies to succeed, areas such as IT governance become fundamental.

This is because, with IT governance, it becomes easier to ensure the management, control, and optimization of information technology resources, reinforcing the value of the IT department for the company. If you are interested and want to learn more about the pillars, benefits, and how to implement IT governance, just follow the article!

What is IT governance?

IT governance is a set of processes and practices aimed at ensuring the alignment of the Information Technology (IT) department with other sectors of a company. For this, the set of IT governance practices guides corporate management in making strategic decisions.

In this way, the IT department of an organization can stay aligned with the company’s strategic objectives, optimizing the use of IT resources and better managing the risks of applications, systems, and hardware used in all sectors.

IT governance is guided by various pillars, such as strategic alignment, risk management, resource management, and value management. These pillars help managers keep the information technology area running in accordance with the company’s norms and policies. Throughout the content, we will delve into how these pillars work.

What are the pillars of IT governance?

To be able to align the information technology area with the organization’s needs, IT governance has some pillars. Check it out:

Strategic alignment

A fundamental step for the success of IT governance, strategic alignment ensures that the IT area acts in accordance with business needs, taking into account its norms, policies, and objectives.

Thus, truly effective and high-value solutions for business development can be planned by those responsible for IT governance.

Value delivery

Another important pillar for IT governance is value delivery. Here is the moment to ensure that investments made in the IT area are, in fact, aligned with the area’s activities, business objectives, and stakeholder expectations.

With this, it is necessary for the information technology area to present tangible, quality results that effectively benefit the company.

Risk management

Risk management is important for identifying, assessing, and mitigating risks related to information technology activities. For this, those responsible need to develop a plan and carry out a comprehensive analysis to identify potential risks that can affect security, system performance, and reliability, for example.

Once this is done, it is the role of governance to communicate the risks to the company, developers, and customers. This pillar is fundamental to correcting possible risks identified with quick adjustments and ensuring the prevention of further damage.

Resource management

Resource management within IT governance refers to the responsibility of managing the budget of the information technology department to ensure the delivery of services within the expected spending limit set by the organization.

Aspects related to resource management include human resources, i.e., recruitment, development, and retention of talent, investment in hardware, software hiring, network usage, among others.

Performance measurement

Evaluating performance is an essential pillar in IT governance because it is through measuring results that it becomes possible to assess the effectiveness of strategies and ensure that the team is delivering what is expected by the company.

For this, many use key performance indicators (KPIs) and various orchestration methods that, when aligned with the company’s strategies, help measure performance, enabling the identification of trends, patterns, and areas for improvement.

Benefits of Implementing IT

Governance Adopting IT governance in your company can bring numerous advantages. Check out the main ones below:

Cost reduction

The costs of your company can be significantly reduced by implementing IT governance. This is because this area aims to optimize the use of resources, avoiding unnecessary costs, and eliminating redundant processes. In addition, a well-structured IT governance focuses on simplifying and automating processes, eliminating possible delays, and reducing the time spent on certain activities. In other words, IT governance can map processes in the information technology area and achieve the best results.

Improvement in quality

Complementing cost reduction, the improvement in quality arises with IT governance as strategic planning brings greater productivity to the company. This is because IT governance is responsible for analyzing the best ways to perform company activities using the benefits of technology.

For example, it is the responsibility of IT governance to verify if it is possible to automate business processes with tools such as RPA and BPM. In this way, many repetitive tasks manually performed by developers can be carried out by robots with the adoption of automation software.

As a result, there will be an improvement in quality and results with fewer human errors, reallocation of workforce to higher-value projects, and consequently, a reduction in operational costs, as robots can perform the same tasks in less time.

💡Learn more: RPA for IT: benefits, examples, and how to implement

Information Security

IT governance also assists in the information security of a company, as it is responsible for implementing policies and security mechanisms in IT. In addition, it is up to IT governance to organize security training for the team, ensuring that everyone is aware of the adopted guidelines.

Finally, IT governance is responsible for ensuring that the company’s security practices comply with existing laws and regulations, such as the LGPD (General Data Protection Law), for example.

More Innovation

Finally, IT governance has the strategic function of driving innovation in a company. This benefit can be achieved through the research and evaluation of new technologies and innovative practices to be implemented in the corporate environment.

It is important that IT governance always seeks to update itself on new technologies and market frameworks. In this way, your company can remain competitive in an industry that undergoes constant changes.

How to implement IT governance?

There are IT governance frameworks that guide the implementation of the area in the company. Frameworks are ready-made models that your company can follow to assist in the implementation process of IT governance. Get to know the most used ones:

COBIT

COBIT, acronym for Control Objectives for Information and Related Technologies, acts as a management and IT governance framework. It provides a set of practices, guidelines, and organizational strategies to help companies achieve their objectives.

The framework provides specific details on how IT governance can carry out its activities. Additionally, it identifies the KPIs for each process, helping companies measure the success of each process.

ITIL

ITIL (Information Technology Infrastructure Library) is a framework that encompasses a set of efficient practices and concepts that assist IT governance. The tool defines organizational functions to ensure that activities are carried out effectively, including Service Desk and Problem Management.

The tool is considered a library of best practices, that is, it is a set of flexible guidelines that organizations can adapt according to their needs.

PMBOK

Finally, PMBOK (Project Management Body of Knowledge) serves as a guide for IT governance. It contains widely recognized and used project management best practices.

PMBOK contributes to professionalism in project management by providing standards and guidelines to assist in performing work effectively.

IT Governance and IT Management

Despite being related, the concepts of IT governance and IT management have crucial differences and play important roles within an organization.

IT governance, as we’ve seen, has the role of aligning IT activities with the objectives of other areas of the company through a set of practices that ensure the effective use of IT resources.

In other words, IT governance operates more strategically, taking into account the company’s norms and policies.

On the other hand, IT management is responsible for the practical and operational implementation of IT activities, ensuring that the strategies determined by IT governance are executed properly.

Its activities include project management, technical support, system development, and infrastructure maintenance.

💡Learn more: What is IT orchestration and main tools

Ready to improve IT governance in your company?

We hope this content has been helpful in clarifying your doubts about IT governance. Remember that implementing this area in your company can bring numerous benefits.

If you want to optimize your business processes and be recognized for boosting governance in your company, using RPA for orchestrating IT automations and applications can be a great option.

With the BotCity RPA orchestrator, you can boost the efficiency of code automations in an integrated way, creating and orchestrating automations from the simplest to the most complex across different systems, applications, infrastructures, and devices.

So, schedule a conversation with our experts or, if you want to try the BotCity code assistant and orchestrator on your own, feel free to create a free account!

Robotic Process Automation (RPA) is a highly versatile technology that has revolutionized how businesses operate.

If you are an IT leader or a developer looking to update your skills, you need to understand how to create automations in various environments and systems, as well as how to orchestrate RPA in your organization.

An RPA course will help you develop the necessary skills to implement and manage successful robotic process automation projects.

In this article, you will learn about the benefits of RPA for businesses, the most recommended types of RPA courses, and some available course options on the market. Keep reading!

RPA Course: What types are there?

RPA, or Robotic Process Automation, is a technology that uses virtual robots to automate repetitive and manual tasks. These robots are software that can interact with systems and applications just like a human.

RPA offers several benefits to companies, including cost reduction, efficiency improvement, error reduction, and quality enhancement.

Different types of RPA courses are available:

Low-code RPA development course: usually aimed at business professionals with limited programming skills. These courses teach how to create simple automations with low-code RPA platforms;

Code-based RPA course: these are for technical professionals and programmers who want to automate tasks in their areas or specialize in RPA;

RPA management course: these are more focused on RPA orchestration and managing complex automation operations.

Why Invest in an RPA Course?

As a technology leader or RPA developer, you are responsible for ensuring that your organization’s automations work correctly and are well integrated with other processes. An RPA course can help you:

Understand the fundamentals of RPA: This training will provide you with a basic understanding of concepts, integration possibilities, and tools involved in robotic process automation.

Implement RPA automations: You will learn how to develop and implement successful RPA projects, whether they involve low-code or coded automations, such as in Python.

Manage RPA projects: In a context of hyperautomation, it is necessary to have the skills to effectively manage large RPA projects, in order to minimize errors and maximize project ROI through automation orchestration.

RPA Course: Discover 4 options from beginner to advanced

We have compiled some of the best RPA course options for developers and automation leaders. Check them out:

Python Driven AI Automation: BotCity RPA & Trending AI Tools

This course presented by Sandip Ganguli, automation an IA expert, is the right choice to learn Next-Gen AI Skills and build automations in Python Framework, while Integrating OpenAI, ChatGPT & Google Bard.

The course uses the BotCity ecosystem, a development and orchestration platform for code-based automations, to create robots for common automation cases.

The course syllabus includes the following modules:

• Intelligent Computer Vision Framework. Learn to build Desktop BOT, Web BOT, PDF Bot, Google Workspace Plugins, Orchestration and More.
• OpenAI & Python – Build AI ChatBot as Text-To-Audio, Audio-To-Audio (Voice Assistant), and more. Integrate OpenAI library with other open source libraries.
• AI & RPA Integration – Learn to build the Framework which can integrate AI offerings, BotCity RPA offerings, and other Python libraries.
• AI Automation – Complete project as a package and set you to start your AI Automation journey.

UiPath Courses

For people already working in companies with UiPath, a low-code RPA license, there are several different courses for RPA development with this software. UiPath itself has an area for free UiPath courses, but there are also other highly rated courses created by experts. Some examples include:

• Complete UiPath RPA Developer Course, presented by Marcelo Cruz.
• UIPath 2023 – Level 1 RPA developer, presented by Bryan Lamb.
• UI Path -RPA – Tech Primer, by Brian Lamb.

Automation Anywhere Courses

Like UiPath, Automation Anywhere is another highly used low-code platform by companies with automation operations. It is worth studying this platform more deeply if the company you work for already uses this solution.

Some examples of courses include:

• Automation Anywhere University, with different trainings and certifications;
• Automation Anywhere, complete training course by Marcelo Cruz;

However, it’s important to note that licenses for platforms like UiPath and Automation Anywhere can be costly. Depending on your needs or your team’s, it may be worth exploring open-source RPA software, such as those based on Python automations, which offer more efficiency and scalability in the implementation of automations.

Creating Automations with Computer Vision

Another free RPA course for those already familiar with the world of RPA automations in Python is “Creating Automations with Computer Vision.” It teaches how to create desktop automations and read data from YouTube and other image and video assets with computer vision for automations.

Additionally, it teaches basic orchestration fundamentals, such as:

• Automation management
• Task, log, and alert management
• Automation execution

Have you chosen the best RPA course for your needs?

As you’ve seen in this article, there are various Robotic Process Automation courses to meet different needs.

If you are an IT leader or are looking for a career transition in the RPA area, it’s important to consider a course to develop the skills necessary to implement and manage successful RPA projects.

Why not try applying robotic process automation in your company and enjoy all the benefits described at the beginning of this article right now? Register for free at BotCity!

We have a free Python code assistant for creating RPA automations, as well as more than 30 RPA frameworks for common use cases of automation.

According to a survey conducted by Mordor Intelligence, the computer vision market is expected to grow from $14.65 billion in 2023 to $33.13 billion by 2028.

These numbers indicate significant growth and increased technology use across various sectors. The research also states that due to advances in image and object sensors, the application of computer vision has expanded in many sectors, such as healthcare and security, for example.

So, if you are interested and want to learn more about what computer vision is, how it works, its main applications, and tasks, keep reading the article.

What is computer vision?

Computer Vision is a field of computer science that leverages Artificial Intelligence (AI), Machine Learning (ML), and Optical Character Recognition (OCR) to develop computational systems capable of seeing, interpreting, and classifying visual information in a manner similar to humans.

This is achieved through the development of algorithms capable of extracting patterns and useful information by simply analyzing images and videos. This enables the generation of relevant data, automation of tasks, and acceleration of decision-making in various areas.

How does computer vision work?

For us humans, the process of seeing, analyzing, and understanding the world around us is a routine task. However, this physiological system involves several steps: when light reflected by objects passes through the cornea, it travels along the nerves of the retina and only then reaches the brain so that images can be processed and understood.

With continuous learning, we enhance our ability to differentiate objects, measure distances, and understand patterns, gaining context about the world around us.

With computer vision, the process is similar. Through the use of algorithms and operating systems with mathematical and physical data, the machine becomes capable of analyzing, interpreting, and extracting information from visual elements.

To operate correctly, computer vision systems rely on techniques such as Machine Learning and Deep Learning to improve their capabilities over time. In other words, machines need to be trained and fed with a large volume of data to optimize their processing capacity.

Only then can the system be trained to succeed in its goal, distinguishing characters, symbols, animals, people, and objects, for example. This process is cumulative – the more data and information provided to the system, the greater its ability to perform tasks appropriately.

Applications of computer vision

After understanding what computer vision is and how it works, let’s explore some examples of applications of this technology in different processes and sectors. Check it out:

IT Automation

Computer vision technology is also used in Robotic Process Automation (RPA) in various areas, such as IT.

With computer vision, robots created to automate IT processes can “see” systems and navigate between screens, being able to fill in fields, update files, and more.

As a result, if changes are made to the system the robot works on, it can still scan the screens until it finds the information it is looking for.

For example, BotCity, a company specialized in business process automation, offers a code assistant and a document converter in Python RPA based on computer vision, helping developers create robots in Python RPA.

💡Learn more: How to optimize performance with Python RPA

Agriculture

Computer vision enhances agriculture in various aspects: planting control, pest control, weed identification, herd tracking, soil moisture verification, harvest robotization, and advanced weather analysis, for example.

All these functionalities contribute to increased productivity, improved time between sowing and harvesting, and, especially, cost reduction.

Autonomous vehicles

For autonomous vehicles, computer vision allows them to understand and react to the environment very similarly to human capability. This is because the technology gives autopilot the ability to map, analyze, and react quickly to all objects around.

Tasks such as object detection, recognition of signage, depth perception, recognition of pedestrians and cyclists, autonomous decision-making, and mapping are applied.

Intelligent document processing

Within computer vision, there is also Optical Character Recognition (OCR), a widely used area for intelligent document processing.

For example, OCR-based robots are widely used for reading and classifying documents in various business sectors. They can also convert documents into code and trigger automations based on their reading.

Applications are diverse: from a purchase order, payment approval to bank reconciliation, and inventory checking, much can be done with OCR combined with process automation.

Facial recognition

Facial recognition is available in personal use applications and is widely used by various individuals on their smartphones and companies working with identity verification.

The public security industry uses facial recognition systems in various cities around the world to detect and prevent criminal activities. The facial recognition process begins with computer vision algorithms delineating faces in images. Then, facial features are extracted and transformed into numerical data for analysis.

Finally, the person is identified based on the recognition of unique patterns present in facial features, allowing computer vision to compare these patterns with stored data.

Entertainment

Entertainment has increasingly presented itself with innovative and interactive experiences. Users can participate and interact, making experiences immersive and dynamic.

Computer vision is an essential component in the creation of Augmented Reality and Virtual Reality, as it allows devices to interact with the environment. Digital games have become more interactive with systems like Microsoft’s Kinect, for example.

Health

The health sector is also benefiting from computer vision to optimize processes, especially in imaging exams such as X-rays and ultrasounds.

Algorithms analyze images and can identify possible anomalies that require attention from the doctor. This way, diagnoses become faster, more accurate, and the chance of human error decreases.

Main tasks of computer vision

Now, let’s check the main tasks of computer vision:

Image Classification

Through algorithms and various learning models, computer vision can classify images by identifying patterns and, based on that, categorize them by class. For example, after identifying objects, the technology can separate them into furniture, clothing, and electronics.

Object Tracking

Present in surveillance systems and autonomous vehicles, object tracking is the ability to follow and locate static or moving objects in sequences of images and videos.

And based on this, analyze and perform a specific function. For example, a car can trigger a warning sound when it identifies that another vehicle is approaching quickly.

Segmentation

The segmentation algorithm in computer vision can separate images into significant parts according to visual characteristics, such as brightness and color. This task is fundamental for understanding the structure of a scene and identifying objects.

Ready to use computer vision?

We hope this content has clarified the main questions about computer vision, its operation, applications, and tasks. This is a promising field that evolves constantly and aids in the development of companies.

If you are interested in computer vision for your area, it’s worth checking out BotCity’s computer vision solutions.

If your business is ready to take the next step and boost the efficiency and orchestration of your automations, it’s worth exploring BotCity’s RPA orchestrator. Click here to speak with a specialist!

Or, if you’re taking the first steps in robotic process automation (RPA), take advantage of the opportunity to register for free at BotCity right now!

BotCity offers a free Python code assistant for RPA, as well as more than 30 RPA frameworks for common use cases of automation.

With the increasing complexity and diversity of software environments, the use of virtual machines (VMs) has emerged as a robust solution for challenges in the development, testing, and execution of systems.

In this article, we’ll explore what a virtual machine is, its key advantages for a company, and how to use VMs in conjunction with Robotic Process Automation (RPA). Check it out!

What is a virtual machine and how does it work?

A virtual machine (VM), also known as a guest or guest virtual machine, refers to a virtualized computing environment created using software to emulate the operation of physical hardware, such as a CPU, disk, and network devices. The existing operating system on the machine is known as the host, and it is responsible for hosting the guest systems.

In other words, a virtual machine is a representation of a computer system within another system, allowing a physical server to host multiple virtual machines simultaneously, each capable of running its independent operating system.

Virtual machines operate through virtualization (hypervisor), a process that creates an abstraction layer between the computer’s physical hardware and the running operating system.

Virtual machine in the cloud

In addition to a virtual machine hosted on physical hardware, there’s also the possibility of creating a VM running on a cloud system, emulating various operating systems on platforms like Google Cloud Platform, Amazon Web Service (AWS), and Microsoft Azure.

This type of VM operates on a remote server maintained by one of the mentioned providers. Typically, using a virtual machine in the cloud is recommended for medium and large enterprises due to cost considerations.

Its usage includes hosting applications, data analysis, software development and testing, and more.

Advantages of virtual machines (VMs): why use them?

Virtual machines play a crucial role by allowing the creation of various differentiated environments within the same computer. This enables testing without modifying the main workspace.

Here are the key benefits of using virtualization of machines:

• Creation of multiple virtual machines to access different operating systems within the same physical machine, provided that the computer has internal space to store virtual disks;
• Running various applications simultaneously occurs independently in isolated systems, ensuring that possible failures do not affect other virtual machines.
• Testing other applications without modifying the main workspace;
• The ability to run two or more applications simultaneously, as each virtual machine operates in an isolated environment, acting as a sandbox;
• Running multiple parallel automations with high performance and speed, as seen in Python RPA automations;
• Adopting virtual machines on the same computer reduces costs associated with hardware acquisition and maintenance;
• Optimizing the operation of the environment facilitates the management, migration, and replication of computers.

Virtual machine vs. container: when is VM more suitable?

Virtual machines offer superior operational capability compared to containers, making them a preferential choice for handling monolithic workloads due to their ability to manage complex tasks.

However, this versatility results in lower portability since virtual machines depend on the operating system, applications, and libraries. Virtual machines are particularly suitable for:

• Managing monolithic, traditional, and legacy system architectures.
• Separating development environments that pose risks.
• Allocating infrastructure resources, including networks, servers, and data storage.
• Allowing the operation of a different operating system within another, such as running Windows in a Linux environment.

Virtual machine for RPA automations: how to use?

Combining Robotic Process Automation (RPA) technology with virtual machines can be an excellent option to optimize your company’s results.

BotCity offers tools for RPA development and orchestration in Python. One of its advantages is the ability to operate multiple RPAs simultaneously in the same execution environment.

In web automations operating in headless mode (background, without a graphical interface), you can run them on a single VM, with each automation having its distinct process in headless mode.

This significantly increases the scalability of Python RPA operations, which is crucial in environments where the demand for data processing and simultaneous tasks is high.

It’s essential to note that the location where the automation will be executed depends on the process to be addressed. Execution can take place on the user’s machine, a server computer, a virtual machine, or in the cloud.

Ready to use a virtual machine?

We hope this content has clarified everything about virtual machines and the conveniences that their use can provide for companies seeking growth. As we’ve seen, opting for this technology can optimize your organization’s processes.

To scale your business even further, combining RPA automation with VMs is an excellent strategy. You can start right now with BotCity, a high-code platform that stands out for its flexibility and the possibility of unlimited customizations, focusing on Python automations.

After all, this language is familiar to most developers and is ideal for businesses seeking greater autonomy and control over complex processes.

So, schedule a conversation with our experts, or if you want to try BotCity on your own, feel free to create a free account.

RPA (Robotic Process Automation) and BPM (Business Process Management) are technologies that have gained increasing prominence in the era of hyperautomation, as they play a fundamental role in operational efficiency and automation of an organization’s processes.

Standing out in the market currently requires companies to adopt innovative technologies such as RPA and BPM. These solutions offer a significant advantage, empowering organizations in operational efficiency and continuous improvement of business processes.

In this article, we will explore what RPA and BPM are, their main differences, and understand which technology is ideal for your type of business. Check it out!

What is RPA?

RPA (Robotic Process Automation) is the technique of automating repetitive and operational activities in various areas of a business. The technology automates these processes using software to create and orchestrate automations efficiently.

With RPA, robots are programmed to perform specific rule-based actions, interacting with operating systems in the same way a human would.

RPA automation has several advantages, such as reducing human errors, freeing up employees for more complex tasks, increasing operational efficiency, and, in the long run, reducing costs.

💡 Learn more: RPA Software: Types, Features, and the Best Ones

What is BPM?

BPM (Business Process Management) is an approach to managing a company’s processes. In other words, it acts as a management tool that uses techniques to optimize, manage, and monitor business processes.

Its goal is to improve workflows and activities performed throughout the organization, thus helping increase efficiency, productivity, and alignment with strategic objectives.

RPA vs BPM: what are the differences?

Although related concepts, RPA and BPM play different roles in business organization. BPM aims to operate within the organization as a whole, involving management and bringing about structural transformations in the long term.

On the other hand, RPA involves technical automation solutions focused on specific sectors and specific activities that need to be automated within a business, making it more present in technical areas. We can understand BPM as more focused on the business side of an organization.

The technologies also differ in their implementation; BPM involves the entire company, so its implementation takes longer. Additionally, it is responsible for changing the cultural aspects of the business.

With RPA, the scenario is different: the demands are more isolated and can be addressed more quickly, so its implementation does not take as much time.

Finally, the approach is also different. In BPM, the process involves analysis, modeling, and continuous improvement of organizational processes. RPA, on the other hand, works by executing processes as a human would, interacting with system interfaces.

How to choose between RPA vs BPM?

The good news is that it is not necessary to choose between RPA and BPM for your company; you can take advantage of both at the same time. This is because, as mentioned earlier, the technologies play a different role and can help your business in a complementary way, obtaining a comprehensive approach to process optimization and automation.

RPA is recommended for automating repetitive processes, so RPA software robots are configured to perform certain operational tasks. These processes automated by RPA can be integrated with the BPM platform, allowing for a broad management context.

If your company already operates RPA automations at scale, it is also worth having an RPA orchestration platform, in addition to a BPM system. This is because it will help technical teams focus on performance and implementation of RPA tools – which can be more challenging with just BPM platforms.

With both approaches, BPM provides the option to analyze and improve processes throughout the organization, while RPA efficiently performs operational tasks.

The combination of technologies ensures operational efficiency, effective control, and holistic optimization from specific tasks to broad workflows.

💡 Learn more: Trends in RPA and Hyperautomation in Latin America in 2023

All set about RPA vs BPM?

We hope this content has clarified what RPA and BPM are and their main differences. Remember that the functionalities are different, and using both tools can be a great option to optimize your company’s processes.

And, if you are looking for a flexible, scalable RPA software with advanced technical capabilities, BotCity is the right choice. Our orchestrator offers tools that assist in building custom automation flows, with options for managing robot queues, error alerts, and management dashboards for RPA.

So, schedule a conversation with our experts, or if you want to try BotCity on your own, feel free to create a free account!