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Trex Company, Inc. is a manufacturer of wood-alternative composite decking, railing, and other outdoor items made from recycled materials. Headquartered in Winchester, Virginia, Trex is the world&#x27;s largest manufacturer of wood-alternative decking and railing.

Trex Company, Inc. - Wikipedia

Trex Company | 20,922 followers on LinkedIn. Engineering What&#x27;s Next in Outdoor Living | About Us At Trex, we’re out to change the way people live outdoors. Led by trailblazers and pioneers, we’re pushing the boundaries of what’s next and helping our customers enjoy outdoor living in new ways.

Trex Company | LinkedIn

WINCHESTER, Va., February 26, 2024--Trex Company, Inc. (NYSE:TREX) (Trex or Company), the world’s #1 brand of high-performance, low-maintenance and eco-friendly decking and railing and a leader in outdoor living products, today announced financial results for its fourth quarter and full year 2023.

Trex Company Reports Fourth Quarter and Full Year 2023 Results

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The #1 brand in outdoor living is proud to be named to Forbes’ 2021 List of America’s Best Mid-Size Companies and to Fortune magazine’s 2020 list of the world’s 100 Fastest-Growing Companies. Stocked in more than 6,700 retail locations worldwide, Trex outdoor living products offer a ...

TheTrexCo

The Trex Commercial Products segment offers Architectural Railing System, Aluminum Railing Systems, Staging Equipment and Accessories. The company was founded by Andrew U. Ferrari and Roger A. Wittenberg on September 4, 1998, and is headquartered in Winchester, VA.

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Trex Company, Inc. is a manufacturer of wood-alternative decking and residential railing and outdoor living products and accessories, which are marketed under the brand name Trex. The Company also designs, engineer and markets modular and architectural railing and staging systems, for the ...

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ITRex

You’ve probably noticed this trend of automating tasks, processes, and even entire departments. Maybe, your company is also considering implementing an , but you still haven’t found the answer to the following questions: intelligent process automation solution Do I build a custom system or opt for an off-the-shelf solution? Do I need to prepare my organization for a digital overhaul? If yes, where do I start? In this article, ITRex CEO explains what ready-made and custom enterprise automation solutions can offer and which might better suit your business needs. Vitali also gives tips on getting started with automation and warns of challenges to expect on the way. Vitali Likhadzed What is enterprise automation, and how does it help your company? Definition and terminology clarification Enterprise automation is defined by Integrify as a systematic review of an organization’s processes to ensure optimal performance while looking for manual processes that can be replaced with digital alternatives. For the sake of terminology clarification, robotic process automation (RPA) and business process automation (BPA) are technologies one can use in enterprise automation. BPA focuses on streamlining complete processes and workflows across the organization, while RPA is responsible for automating individual tasks. UiPath, a prominent RPA vendor, enterprise robotic process automation (RPA) as a software technology that makes it easy to build, deploy, and manage software robots that emulate human actions when interacting with digital systems. Software robots can do things like figuring out what’s on the screen, completing the right keystrokes, navigating systems, identifying and extracting data, and performing a wide range of defined actions. business process automation (BPA) as the automation of complex business processes and functions beyond conventional data manipulation and record-keeping activities, usually through the use of advanced technologies. BPA focuses on “run the business” as opposed to “count the business” types of automation efforts and often deals with event-driven, mission-critical, core processes. Here is what these concepts mean: defines Gartner defines Intelligent enterprise automation enhances the traditional RPA’s capabilities with innovative , such as natural language processing (NLP), and . artificial intelligence (AI)-based technologies computer vision Intelligent enterprise automation can use one or a combination of these technologies to offer complex, innovative solutions. COVID-19 accelerates enterprise workflow management efforts Deloitte and McKinsey, both reputable consultancy firms, conducted surveys that proved the pandemic forced companies to rethink the way they work. McKinsey research shows that the percentage of respondents taking action on automation has increased from 57% in 2018 to 66% in 2020. Source: McKinsey & Company During the same period, Deloitte 441 executives from 29 countries to find that 73% of the respondents had already deployed automation technologies, such as machine learning and robotics. This number is up from 58% in 2019. Enterprise automation will continue its steady rise throughout the following decade. For example, according to Statista, the global robotic process automation market is predicted to surpass . questioned $13 billion by 2030 Source: Statista Enterprise workflow automation benefits In one of its enterprise process automation projects, ITRex helped digital networking and booking services company to build a healthcare RPA solution. This product (EMRs) of the company’s clients. Before deploying enterprise automation solutions, doctors wasted valuable time entering their availability data into various EMRs to give patients an accurate overview. This caused errors and frustration. To resolve this, the client employed ITRex to develop an automation healthcare system that would synchronize the client’s booking service with eClinicalWorks (eCW), one of the most popular EMR solutions in the US. Even though this automation effort was limited in scope, it already brought the following benefits: synchronizes patient appointment data between the company’s booking service and the electronic medical record systems Making the client’s booking service consistent with the eCW without modifying its internal code Eliminating human error Freeing up doctors’ time to take over more appointments instead of wasting time on data entry Reducing costs, as the client didn’t need to hire specialized eCW developers, who are relatively expensive. Neither did they pay eCW API fees Off-the-shelf enterprise automation software vs. custom enterprise automation Off-the-shelf (ready-made, packaged, etc.) software is a commercially offered and licensed digital product that you can deploy at your company for a monthly or annual fee. In contrast, custom solutions are designed and built catering to the requirements of a specific client. It is worth mentioning that some ready-made solution vendors are open to working with clients and customizing their offerings to fit into the client’s system. Below are the main differences between the two options. Disclaimer: this comparison is applicable in many cases. However, there can be exceptions, especially when speaking of customizing off-the-shelf solutions. Off-the-shelf enterprise automation tools are more suited for: Enterprises operating in heavily-regulated industries. In this case, packaged software might already come compliant so that you don’t have to investigate and apply these regulations yourself. Large companies. If large businesses want to implement enterprise-wide automation, it is best to turn to off-the-shelf solutions. Custom enterprise workflow management software would be too expensive, time consuming, and might not cover all the variable use cases. Ready-made products, on the other hand, are designed for this type of client. Additionally, vendors receive user feedback and improve their systems accordingly. Note: if you are a large enterprise that wants to automate only distinct tasks and use cases, feel free to turn to custom software solutions. Business cases with no special requirements where a standard set of features is sufficient. Turning to an to build a custom enterprise automation software is worth considering under the following conditions: RPA consultant Projects with limited automation scope, when three to five use cases are considered Business cases that are unique and complex, where off-the-shelf products would need customization Legacy systems are involved Enterprises with rigid processes that can’t be adapted Getting started with enterprise workflow automation: tips and challenges Vitali advises companies considering RPA or enterprise intelligent automation to prepare for this process across three pillars — governance, data and technology, and the human pillar. Companies can do this alone or together with their selected automation vendor. Governance: Identify the problems your company is trying to solve. For example, a bank may realize 50% of their clients who submitted a personal loan application form end up withdrawing before a decision is made Outline use cases for enterprise automation and prioritize them. We can investigate the business problem mentioned above to discover concrete use cases. Here is one way to proceed. Why do 50% of our clients withdraw? Because they must wait for a prolonged period. Why is that? One reason is that our loan application review process requires approval from five people. Human approval is not something we can automate, so we keep searching further. Another reason is that loan officers need to access data from different systems to make a decision, and afterwards, they enter all the information in the bank’s system. Here is a viable use case — i.e., automating data collection and information input processes. Optimize business processes before automating. Make sure the process is as efficient as possible before you automate. In the loan application example, do we need the approval of five people? Maybe in some cases, three approvals are enough. Define clear business goals you want to achieve with enterprise automation. Coming back to the bank example, the goal is to spend less time processing loan applications and give an answer to the client before they withdraw their file and submit it to competitors. Highlight KPIs to measure the value of automation to the business. One example of the bank’s KPI can be making a decision on personal loan applications within one working day. Ensure executive leadership support. This will help you get other employees to cooperate even when the initiative causes inconveniences or fails to produce value immediately. Identify all the stakeholders involved in enterprise automation. These people can come from different departments and vary in their background and expertise. It is best to make sure their views on automation are aligned. Define risks. Here are some examples: (1) decreased loyalty among employees as they start experiencing job security fears (2) integration with legacy systems can take much longer than we anticipated originally, (3) this new automated system might open vulnerabilities from the security standpoint. Data and technology: Choose the right technology for every business problem. If your company is small, the automation scope is modest, and the use cases are standard, then opt for a small-scale enterprise automation system that covers your needs. Nothing more. Leverage multiple technology solutions to bring end-to-end automation. In theory, it would be nice to have one platform across different departments. But if your business is fragmented, this will not be a viable option. Suppose you have one autonomous isolated (no Internet connection) department with a few straightforward requirements. In that case, a simple custom Python script might suffice, even if your other departments are using more complicated automation software. Select solutions that integrate well with the existing ecosystem. Your ecosystem has its established workflow, make sure the automation tool you choose is compatible with it and can integrate with your applications. Don’t trap yourself with a novel technology that doesn’t fit with your existing tech stack. Pay attention to data quality. Make sure the data you input and generate with this new enterprise automation solution is compliant with your overall data strategy. Humans and change management: Ensure everyone understands automation goals. The new system will take over repetitive, dull tasks and allow employees to focus on what matters and devote more time to creative and strategic duties. Drive workforce transformation, not replacement, if possible. Automation is expected to eliminate as well as create new job opportunities. Research shows workforce automation can displace in the coming years, but it is expected to create 97 million instead. Given this situation, it may be a wiser choice to reskill your employees. 85 million jobs Invest in employee education. Train users on the new work environment and support them to facilitate adoption. Develop an HR strategy to retain employees and attract talent for critical roles. When a part of their everyday duties becomes automated, employees might feel there is no place for them in your company anymore. But this workforce has other key skills that the enterprise can’t afford to lose. This is where the HR department comes in. They need to find ways to keep these people motivated and interested in staying or quickly recruit a replacement if they insist on moving on. Establish an agile way of working. Employees need to be prepared to pivot within their job role, take other responsibilities, and be prepared to learn. 5 Enterprise automation challenges to consider and ways to address them Vitali highlights five main challenges that can cause setbacks in enterprise workload automation initiatives: Struggling to scale: even though in automation, be it process discovery tools or software vendors, scaling is still hindered as a comprehensive automation strategy is lacking. According to Deloitte, only that are implementing and scaling automation have an enterprise-wide automation strategy. As a result, firms automate individual processes in one department but struggle to scale them across the enterprise due to employee resistance or other factors. To address this challenge, you can enlist the C-suite’s support from the start and establish a center for excellence, which would set standards for scaling. enterprises invest heavily 38% of companies Regulations and enterprise-related constraints: if you automate a task that is tied to rapidly evolving regulations, they can soon change, rendering your new solution unusable. Some workflows can’t be automated due to constraints. For example, tasks that require manual approval or processes that need to access data protected with captcha or other tools requiring human interference. Lack of alignment between IT and business functions: this challenge occurs when the IT team has their own goals that are not compatible with those of the business department. They can even be contradicting at times. Both parties can’t understand each other. Businesses want everything to work without appreciating the technical complexity. This creates friction between teams and delays the process. To ensure that everyone is on the same page, you can encourage open and consistent communication, constructive feedback, and collaboration between departments, and remind employees that they are working towards a common goal. Cultural adoption: this is one of the main challenges. While technology-related concerns can be resolved given sufficient time and finances, it is hard to make people adopt a tool when they are not motivated. If you look at enterprise automation as a way to support rather than replace your human capital, making this clear will relieve people from their fears. You may need to prepare and implement a cultural shift. Explain to your workforce which of their responsibilities will be automated and how this will impact their job. Organize training and support people during the learning process. Note that some enterprise automation tools are not user-friendly, which makes them harder to operate and prolongs learning time. Security risks: when you automate tasks, you introduce a new IT system, which in turn will present vulnerabilities and open entrance points for criminals to explore. Also, integration with existing systems will form loopholes for potential security breaches. To minimize these risks, perform security evaluations and risk management and consider the results as an important factor when making the final decision. For a more elaborate list of RPA pitfalls, you can refer to our article on the . top 13 RPA challenges Ready to take action? If after reading this article, you find yourself a perfect candidate for off-the-shelf enterprise automation tools, then go ahead and purchase one. It will be cheaper and faster than building a custom application. But if you want something unique and tailored to your processes and operations, then it’s a good idea to start searching for a vendor providing custom enterprise automation services. Be sure your vendor is familiar with your industry’s regulations and that he is willing to offer continuous support, help you scale, and introduce new features. The ITRex team will not only cover the points mentioned above but can also look beyond the traditional RPA into intelligent enterprise automation thanks to our expertise in AI and other related technologies. Are you looking for a custom enterprise automation solution that fits into your system? Together, you can build something really unique. Drop ITRex consultants a line!

How to Get Started With Enterprise Automation: Tips From ITRex

What is The True Price of Generative AI?

জেনারেটিভ এআই এর আসল দাম কত?

Was ist der wahre Preis der generativen KI?

¿Cuál es el verdadero precio de la IA generativa?

Quel est le vrai prix de l’IA générative ?

जेनरेटिव एआई की सही कीमत क्या है?

生成 AI の本当の価格はいくらですか?

제너레이티브 AI의 실제 가격은 얼마인가?

Qual é o verdadeiro preço da IA generativa?

Какова истинная цена генеративного ИИ?

Üretken Yapay Zekanın Gerçek Fiyatı Nedir?

Giá thực sự của AI sáng tạo là gì?

生成式人工智能的真正代价是什么？

From shaping stories to simplifying complex articles to engaging in conversations that feel genuinely human, large language models (LLMs) are driving a new era of AI. In this article, we share our decade-long experience as an and dive into the world of LLMs, unraveling their innards and exploring how they’re reshaping the future of artificial intelligence. AI software development firm Let’s Kick It Off With the Basics: What Are Large Language Models? A large language model is an algorithm trained to recognize, summarize, translate, predict, and generate any form of text. How Do LLMs Differ From Traditional Language Models? Large language models fall under the umbrella of deep learning algorithms known as . It is the transformer architecture that helped surpass the limitations of traditional language models that have been around for years. transformer neural networks Traditional language models operated in a sequential manner, processing one word (or character) at a time and delivering an output once the entire input text had been consumed. While quite functional, these models had a notable flaw: they used to “forget” the beginning of a sequence by the time they reached the end. It all changed in 2014 when which was later popularized by Google. The attention mechanism enabled a paradigm shift from sequential processing, allowing a transformer model to perceive the entire sequence simultaneously. the attention mechanism was first introduced This revolutionized the machines’ understanding of context. By embracing the entirety of the input at once, the transformer model gains the ability to comprehend the nuances and complex relationships among words in a text. How Do LLMs Work Under the Hood? Large language models learn from data. The datasets used to train LLMs are massive. For example, OpenAI’s well-known and much-loved GPT4 is believed to have been trained on about 13 trillion tokens (think: basic units of text that a model can process.) The model gradually learns words, the concepts behind them, and the relationships between them. Once a model has learned enough, it can transfer its “knowledge” to solving more complex problems, like predicting and generating text. It is possible thanks to a two-component transformer architecture consisting of an encoder and a decoder: Once an input text is fed into a model, it is converted into tokens that can be parts of words, entire words, parts of sentences, or full sentences. The tokens are then converted into vector-space representations that preserve the initial token meaning. The encoder structures these representations, picking out important details and creating a context vector based on that. So, the context vector holds the essence of the entire input text. Based on the initial output and relying on the context vector, the decoder generates a coherent output, say, picking out the most suitable word to finish a sentence. By repeating this process, a transformer model can generate the entire passage word by word. Due to this extensive training process, LLMs are not limited to performing any specific task and can serve multiple use cases. These types of models are also referred to as foundation models. However, you can fine-tune the to perform a narrow task by feeding them small bits of data to focus on. foundation models How Are Large Language Models Used in Business? Large language models are proving to be valuable assets across sectors. Here are just a few use cases to give you an idea of what LLMs are capable of. 1. Chatbots and Virtual Assistants LLMs are driving the evolution of customer service and engagement. and virtual assistants powered by LLMs can handle complex inquiries, provide personalized recommendations, and engage in human-like conversations, enhancing user experiences and operational efficiency. Chatbots Energy companies, like Essent, deal with a consistent influx of customer service demands. Jeroen Roes, lead conversational AI program manager at Esse as its primary customer service tool for decades. nt, says that the company had relied on telephony However, in the face of intensifying competition and a surge in customer service requests, Essent recognized the need to reinvent its operations to maintain a competitive edge. The company saw an opportunity in LLM-based chatbots. By harnessing this innovative technology, Essent managed to meet the growing customer service needs. 2. Sentiment Analysis, Market Research, and Trend Prediction Businesses are leveraging LLMs for to gauge public opinion, track brand perception, and predict market trends. By analyzing vast datasets, LLMs help businesses make informed decisions, optimize marketing strategies, and stay ahead of the competition. sentiment analysis For example, Sprinklr, a social media management and customer engagement platform, . This assists businesses in monitoring and engaging with discussions related to their brand or product on social media. harnesses large language models for sentiment analysis Sprinklr’s platform analyzes social media data to identify sentiment patterns and provide valuable insights into customer behavior and preferences. 3. Content Generation LLMs are changing the old approaches to writing content. They can generate high-quality articles, reports, and product descriptions. Content generated by LLMs can be customized to fit specific brand voices, ensuring consistency and authenticity. Here are some noteworthy LLMs used for content generation across sectors: GPT-3, 4: These models excel in human-like dialogue generation, copywriting, translation, and many other language-related tasks. LaMDA: Google’s LaMDA is designed for engaging conversations and text generation, offering valuable applications in human interaction. Megatron-Turing NLG: A versatile language model, Megatron-Turing NLG is used for a wide range of text-based tasks and is particularly known for its strong support for multiple languages. DALL-E, Stable Diffusion, MidJourney: These models are experts in generating images based on textual descriptions, opening up new possibilities in creative content generation 4. Personalized Recommendations E-commerce platforms and streaming services are harnessing LLMs to deliver to users. These models analyze user behavior and preferences to curate content, products, and services tailored to individual tastes, enhancing customer satisfaction and retention. personalized recommendations For instance, Instacart, a grocery delivery service, utilizes LLMs to address nutrition queries and offer personalized product recommendations. Adopting LLMs for Business: Factors to Consider Incorporating large language models into your business operations is a strategic move that can yield significant benefits. However, it’s crucial to navigate this transformation thoughtfully and meticulously. Here, we delve into the essential factors you should consider when adopting LLMs for your enterprise. 1. Available infrastructure and resources LLMs are hungry for processing power, so they require a robust infrastructure. Before diving in, assess your current IT infrastructure and determine if it can accommodate the substantial computational demands of LLMs, or if an upgrade or expansion is necessary. Also, keep in mind that LLMs can be resource-intensive. As your business grows and your reliance on LLMs increases, scalability becomes pivotal. Ensure that your infrastructure is not just suitable for current needs but can also adapt to future ones. Scalability might involve adding more powerful servers, , or a combination of both. utilizing cloud-based solutions 2. Choose whether to go open-source or custom There are two common ways to adopt LLMs: fine-tune an open-source model or train a custom one. Your choice should align with your business objectives. Open-source models, like GPT-3,5, offer a cost-effective entry point for experimenting with AI-powered applications. They’re pre-trained on extensive datasets and can perform a wide range of language-related tasks. However, they may not be tailored to your specific needs, which calls for fine-tuning. If you require a competitive edge and greater flexibility, custom models are the way to go. They offer flexibility in deployment, allowing you to tailor the model’s structure, configuration, and size to your specific requirements and objectives. For example, if you run an e-commerce platform, a custom LLM can be trained to better understand product-specific queries and user interactions. 3. Weigh the available expertise and skills Successful LLM deployment relies on a skilled team. , , and are crucial assets. These professionals can fine-tune and optimize LLMs for your specific use case, ensuring they meet your business objectives effectively. Natural language processing machine learning deep learning experts And if you lack in-house expertise, consider partnering with . Specializing in crafting AI solutions, they can provide the necessary skills and guidance. generative AI services providers 4. Make sure to consider data governance and compliance requirements Businesses operating in healthcare, finance, and other regulated industries adhere to strict data privacy regulations. Consequently, when implementing LLMs, they must pay attention to data governance and compliance. Establish robust and compliance measures to safeguard user data and maintain trust. Encryption, access controls, and audit trails are essential components of data protection. Ensure that your LLMs comply with industry-specific regulations, too, like . data governance policies HIPAA in healthcare Challenges and Risks Associated With LLMs While LLMs offer remarkable capabilities, they also present challenges that businesses must be aware of. Here, we look at these challenges and offer solutions to ensure successful LLM implementation: Challenge 1. Bias in training data LLMs learn from vast datasets, but these datasets may present in the original sources. As a result, LLM-generated content may unintentionally perpetuate or amplify this bias. contain bias : Regularly audit and fine-tune LLMs to identify and remove bias. Implement bias detection algorithms and guidelines during model training to reduce skews in the output. Mitigation Additionally, consider diversifying the training datasets to minimize inherent bias. Challenge 2. Data privacy and security concerns LLMs often handle sensitive customer data or proprietary information. Inadequate security measures can result in data breaches. : Implement stringent , including encryption of sensitive data, access controls to restrict data access to authorized personnel only, and compliance with relevant data protection regulations (e.g., GDPR, HIPAA). Regularly update security protocols to stay ahead of emerging threats. Mitigation data security measures Challenge 3. Learning curve and employee resistance Employees accustomed to traditional workflows may resist the integration of LLMs, to their roles. viewing them as disruptors or threats : Invest in comprehensive training programs to upskill employees and familiarize them with the LLM technology. Implement change management strategies that involve employees in the transition process and emphasize the benefits of LLMs in enhancing efficiency and productivity. Mitigation Challenge 4. Risk of overreliance Heavily relying on LLMs, especially when it comes to generating messages for business communication, can dilute brand authenticity and creativity. : Strike a balance between automation and human creativity. Use LLMs as tools to aid and augment human creativity rather than replace it. Continuously review and edit LLM-generated content to align it with your brand’s unique voice and values. Mitigation To Sum It Up Large language models are powerful tools that hold the promise of driving innovation, enhancing customer experience, and optimizing operations. Understanding the intricacies of LLMs and the considerations for their adoption is vital for businesses seeking a competitive edge. If you are considering embarking on the LLM journey and exploring the limitless possibilities they offer, we are here to help! , and we will answer any unanswered question and help you embrace the future with LLMs. Reach out to ITRex Group

Here's What Every Business Should Know About Large Language Models

বড় ভাষার মডেল সম্পর্কে প্রতিটি ব্যবসার যা জানা উচিত তা এখানে

Hier erfahren Sie, was jedes Unternehmen über große Sprachmodelle wissen sollte

Esto es lo que toda empresa debería saber sobre los modelos de lenguaje grandes

Voici ce que chaque entreprise devrait savoir sur les grands modèles linguistiques

यहां बताया गया है कि प्रत्येक व्यवसाय को बड़े भाषा मॉडल के बारे में क्या पता होना चाहिए

大規模言語モデルについてすべての企業が知っておくべきことは次のとおりです

모든 기업이 대규모 언어 모델에 대해 알아야 할 사항은 다음과 같습니다.

Aqui está o que toda empresa deve saber sobre grandes modelos de linguagem

Вот что каждый бизнес должен знать о больших языковых моделях

İşte Büyük Dil Modelleri Hakkında Her İşletmenin Bilmesi Gerekenler

Đây là những điều mọi doanh nghiệp nên biết về mô hình ngôn ngữ lớn

以下是每个企业都应该了解的关于大型语言模型的知识

Recently, we published an article shedding light on . In this blog post, we will focus on one of our AI subsets, , and estimate how much it costs to train, deploy, and maintain intelligent algorithms. the costs of developing an AI solution machine learning To keep it practical, we sat down with , ITRex CTO, and asked him to draw machine learning cost estimates from our portfolio. He also shared his expertise in and listed the steps for businesses to take in order to reduce investments in machine learning — without sacrificing quality or time to market. Kirill Stashevsky developing ML solutions Machine learning costs factors. But before getting down to numbers, let’s quickly highlight the factors determining the final cost of a machine learning solution. 1. The complexity of the solution you’re eyeing to create Machine learning solves many problems of different complexity. Social media engines making friends suggestions, smart surveillance cameras recognizing faces in video footage, and healthcare expert systems predicting heart failures all run on machine learning. However, their complexity, performance, responsiveness, compliance requirements, and, hence, costs vary a lot. 2. The approach to training an ML model There are three approaches to machine learning: supervised, unsupervised, and reinforcement learning. Whether you go this or that way impacts machine learning costs. The essence of each of these methods boils down to this: uses manually labeled datasets to teach algorithms to correctly classify or understand the relationships between data points Supervised learning means that algorithms search for patterns in datasets themselves, with no previous labeling, though it still requires some human intervention, mainly for validating output variables. Unsupervised learning is a bit trickier: instead of relying on data as a beacon for decision-making, the model trains in operation. When learning, a reinforcement agent takes a subtle action, and it’s either “rewarded” or “punished” for it. Reinforcement learning Opting for supervised learning, you don’t need that much computing power since the method is quite easily realized in R or . Unsupervised and reinforcement learning models are computationally more complex. They need a large training dataset to produce reliable outcomes. So, you’ll need more powerful tools for working with vast volumes of unclassified data that may drive machine learning costs up. Python In the context of machine learning, there is another cost-effective strategy organizations may go for to cut down the expenses associated with model development — . The models, often built upon , have become particularly prominent in recent years. using foundation models generative AI techs Foundation models--say, OpenAI’s GPT series--have been pre-trained on large data sets. By harnessing these pre-trained models, you can skip the resource-intensive process of training one from scratch. Instead, you fine-tune the foundation model to perform a specific task, which usually requires less data and computational power and, hence, less investment. However, by going for supervised or reinforcement learning, you can save investments that would otherwise be spent on data labeling. 3. The availability and quality of training data No matter the approach to machine learning, you will need enough . Machine learning costs thus include the price of acquiring, preparing, and — in the case of supervised learning — annotating training data. data to train the algorithms on If you have enough training data on hand, you’re lucky. However, it’s rarely the case. Numerous researchers state that around 96% of enterprises do not initially have enough training data. For your reference, a that, on average, need around 100,000 data samples to perform well. study by Dimensional Research shows ML projects You can synthetically generate the needed volume of data or augment the data you already have. Generating 100,000 data points via Amazon’s Mechanical Turk, for example, can cost you around $70,000. Once you have enough data on hand, you need to make sure it’s of high quality. The study referenced above suggests that 66% of companies run into es in their training data sets. Removing those can take 80 to 160 hours for a 100,000-sample data set. errors and bias In case you opt for supervised learning (which is often the case for commercial ML solutions), you need to add the price of data annotation to the total machine learning cost, too. Depending on the complexity of labeling, it can take 300 to 850 hours to get 100,000 data samples labeled. Drawing the line, a solid training data set of high quality can cost you anything from $10,500 to $85,000, depending on the nature of your data, the complexity of annotation, as well as the composition and location of your ML team. 4. The complexity and length of the exploratory stage During an , you carry out a feasibility study, search for an optimal algorithm, and run experiments to confirm the chosen approach. exploratory phase The cost of exploration depends on the complexity of the business problem, the expected time to market, and, subsequently, team composition. As a rule, a team of a business analyst, a data engineer, an ML engineer, and — optionally — a project manager is enough to carry out the task. In that case, you can expect the exploratory stage to round at $39,000 to $51,000. Outsourcing the effort, you can cut this figure down to $15,000-$20,000. 5. The cost of production Machine learning costs feature the cost of production, too. Production costs include the costs of the needed infrastructure (including cloud computing and data storage), integration costs (including designing a data pipeline and developing APIs), and maintenance costs. Cloud resources The price of the cloud infrastructure depends on the complexity of the models being trained. If you are building a simpler solution that relies on data of low dimensionality, you may get four virtual CPUs running on one to three nodes. This may cost you around $100 to $300 a month or $1,200 to $3,600 a year. If the solution you’re eyeing to create requires high latency and relies on complex , expect a monthly cost of $10,000 to $30,000 to be added to the total ML price. deep learning algorithms Integrations Developing integrations involves designing and developing the data pipeline and the needed APIs. Putting together a data pipeline takes up around 80 development hours. Putting API endpoints in place and documenting them to be used by the rest of the system requires another 20 to 30 hours, the cost of which should be added to the final machine learning cost estimates. Support and maintenance Machine learning models need ongoing support during their entire life cycle: incoming data needs to be cleansed and annotated; models need to be retrained, tested, and deployed. According to the study conducted by Dimensional Research, businesses commit 25% to 75% of the initial resources to maintaining . ML algorithms Assuming that the initial solution architecture and data pipelines are well designed and part of the recurring tasks is , you can go by one support engineer that may cost you around $30,000 a year. automated 6. The cost of consulting If you’re just tipping toes in the machine learning waters, you can’t really get too far without an experienced ML consultant. Two main factors determining the cost of ML consulting include: . It is worth making the experience a critical factor in your hiring decision. You want to partner with someone who has enough expertise in the field you may not necessarily be familiar with. Consultant’s experience . The more complicated the project, the more consultant involvement it will require. Moreover, if the scope of the project is undefined, search for a consultant who can carry out a discovery phase for you and offer a compelling proposal with all the necessary estimations. Project scope ML consulting rates usually reach $5,000 to $7,000 per project. 7. Opportunity costs Opportunity costs can be defined as forfeiting all benefits associated with not taking an alternative route. To put things into perspective, think of Blockbuster, a former leader in the movie rental market. Foregoing innovation, the company lost to a newly emerged leader — Netflix. The opportunity cost equaled $6 billion and a near-bankruptcy. The same idea goes for machine learning initiatives. Enterprises lagging in ML adoption can’t tap into predictive insights and informed decision-making that come with it. On the opposite side, implementing machine learning just for the sake of innovation, say, to solve problems that require rule-based solutions, is a loss as well. Therefore, before you decide to , consider the cost vs. benefit ratio and carefully weigh implementation risks. implement AI in business So, how much does ML cost? These estimates from ITRex’s portfolio might give you an idea. Now that you are familiar with the factors affecting the total ML price let’s look at some examples from ITRex’s portfolio to help you better understand the costs involved. Note that we draw effort estimations, too. The reason is that the price of developing an ML solution depends greatly on the composition and location of your ML development team. You can get an idea of the total cost associated with developing a similar ML solution based on the following rates: Location Average hourly rate United States $59 Central Europe $39-$41 Eastern Europe $25 Asia $10 Latin America $12 Please be aware that the estimated budgets provided below apply exclusively to the development of the machine learning component within these solutions. It’s essential to consider additional expenses, such as infrastructure, productization, and other associated costs, as machine learning operates in conjunction with various elements within the wider solution. Project 1. Emotion recognition solution A multinational media and entertainment company wanted to analyze footage from their surveillance cameras to recognize people’s emotions. The task was complicated by degraded visual conditions, such as the quality of the footage itself, as well as people wearing face masks, glasses, and other items that made recognition difficult. The media tycoon was seeking a trusted to conduct extensive research and power future development. The ITRex team of two ML engineers tested out three neural networks, selected the one optimal for the task, fine-tuned it for better performance, and provided other strategies for achieving a higher accuracy score. media and entertainment software vendor Efforts: approx. 300-350 hours ML costs: approx. $26,000 Project 2. A fitness mirror with a personal coach inside The customer wanted to build an innovative fitness mirror that can act like a personal coach — offering personalized training plans and guiding users through training sessions with real-time recommendations. The ITRex team built the hardware components of the smart device and provided end-to-end software development, spanning infrastructure setup, , and content management. embedded software/firmware development When it comes to the machine learning component of the solution, we designed and trained a deep learning model using a dataset of workout records to provide guidance for users, implemented computer vision algorithms for motion tracking and , as well as object recognition algorithms for overseeing the sports equipment used in workouts. human pose estimation Efforts: approx. 640-700 hours Costs: approx. $51-56,000 Project 3. Automated document recognition solution Our customer was eyeing to create a solution that would automate the process of filling out documents. The key goal of the project was to develop an independent that would recognize and index batches of incoming documents, as well as seamlessly integrate the solution into the customer’s existing document processing system. optical character recognition (OCR) solution The OCR solution we crafted helps automate the traditionally resource-intensive process of marking and indexing documents, leading to time and cost savings. By drastically reducing the manual effort typically allocated to document marking and indexing, the solution allows for handling more documents within the same timeframe. The outcome? Enhanced productivity and swift, accurate processing of critical documents. Team efforts: approx. 300-400 hours ML costs: $28-32,000 How can you reduce ML development costs — and get ROI fast? If you are thinking about venturing into AI development and looking for ways to lower machine learning costs without putting the quality of the final product at risk, look through our field-tested recommendations. Start small, but have a bigger picture in the back of your mind When kicking off an ML project, it often pays off to keep the initial scope smaller. By starting with a minimum viable product, you can focus your resources on a specific problem and iterate quickly. This approach helps save machine learning costs in several ways: Starting small allows you to test your ideas and hypotheses with a smaller dataset and a reduced set of features. This, in turn, lets you quickly assess the feasibility and effectiveness of your ML solution — without investing significant resources upfront. By keeping the scope smaller, you can pinpoint and address potential challenges or limitations in your machine-learning pipeline at an early stage. This helps avoid costly rework at the later stages of development. By prioritizing critical use cases and features, you allocate resources more effectively and focus on the areas that provide the fastest ROI rather than tackling the entire project at once. Follow MLOps best practices from day one to avoid scalability issues MLOps refers to a set of practices that enhance collaboration and automation in ML development projects. By setting up an MLOps pipeline from the outset, you can mitigate potential scalability issues and reduce machine learning costs. The cost reduction is achieved via: Streamlined development process: MLOps promotes standardization and automation while reducing the need for manual, error-prone operations. Scalable infrastructure: MLOps focuses on building scalable infrastructures to support the entire ML development lifecycle, from data preprocessing to model deployment. This helps accommodate growing data volumes, increasing model complexity, and higher user demand without introducing significant changes to the infrastructure. CI/CD: CI/CD practices ensure that changes introduced to your ML solution are automatically integrated, tested, and deployed in a reliable and automated manner. Use pre-trained machine learning models. Using machine learning models that have been previously trained helps reduce machine learning costs in the following ways: Transfer learning: Serving as a starting point for many ML tasks, pre-trained models allow transferring the knowledge learned from a different but related task to the problem in question, which saves substantial computational resources and training time. Reduced data requirements: Training ML models from scratch calls for large volumes of annotated data, which can be quite costly and time-consuming to collect and label. Pre-trained models can be fine-tuned on relatively small volumes of domain-specific data. Faster prototyping and iteration: Pre-trained models allow you to quickly prototype and iterate your ML solution. Do you have an idea of a machine learning solution in mind? , and we will help you draw machine learning cost estimations and bring your solution to reality! Get in touch with us Hang on a little longer Also published . here

Machine Learning Costs: Price Factors and Real-World Estimates

PwC predicts that healthcare costs will . This increase is primarily attributed to healthcare workers experiencing burnout, the subsequent workforce shortage, disputes between payers and providers, and inflation. To ensure efficient patient care without incurring excessive operational costs, the industry is exploring innovative technologies, such as generative AI in healthcare. go up by 7% in 2024 Accenture reports that can be enhanced with AI, while a recent Forbes article suggests that this technology can spare the US medical sector . 40% of healthcare providers’ working hours at least $200 billion in annual expenses in healthcare uses machine learning algorithms to analyze unstructured data, such as patient health records, medical images, audio recordings of consultations, etc., and produce new content similar to what it has been trained on. Generative AI In this article, our generative AI development company will explain how the technology can support healthcare organizations. Generative AI use cases in healthcare Facilitating medical training and simulation Assisting in clinical diagnosis Contributing to drug development Automating administrative tasks Generating synthetic medical data Facilitating medical training and simulations can come up with realistic simulations replicating a large variety of health conditions, allowing medical students and professionals to practice in a risk-free, controlled environment. AI can generate patient models with different diseases or help simulate a surgery or another medical procedure. Generative AI in healthcare Traditional training involves pre-programmed scenarios, which are restrictive. AI, on the other hand, can quickly generate patient cases and adapt in real-time responding to the decisions the trainees make. This creates a more challenging and authentic learning experience. Real-life example The University of Michigan built a generative AI in healthcare model that can produce various scenarios for . simulating sepsis treatment The University of Pennsylvania to simulate the spread of COVID-19 and test different interventions. This helped the researchers evaluate the potential impact of social distancing and vaccination on the virus. deployed a generative AI model Assisting in clinical diagnosis Here is how generative AI for healthcare can contribute to diagnostics: . Hospitals can employ generative AI tools to enhance the traditional AI’s diagnostic abilities. This technology can convert poor-quality scans into high-resolution medical images with great details, apply , and present the results to radiologists. Generating high-quality medical images anomaly detection AI algorithms . Researchers can train generative AI models on medical images, lab tests, and other patient data to detect and diagnose early onsets of different health conditions. These algorithms can spot , , , , , and more. Additionally, AI models can unveil biomarkers that can cause particular disorders and predict disease progression. Diagnosing diseases skin cancer lung cancer hidden fractures early signs of Alzheimer’s diabetic retinopathy . Diagnosticians can turn to generative AI in healthcare if they have questions instead of looking for an answer in medical books. AI algorithms can process large amounts of data and generate answers fast, saving doctors precious time. Answering medical questions Real-life examples A team of researchers to extract and enhance features in low-quality medical scans, transforming them into high-resolution images. This approach was tested on brain MRI scans, dermoscopy, retinal fundoscopy, and cardiac ultrasound images, displaying a superior accuracy rate in anomaly detection after image enhancement. experimented with Generative Adversarial Network (GAN) models In another example, was trained on the MedQA dataset and achieved an 85% accuracy rate while answering relevant medical questions. Google admits that the algorithm still needs improvement, but it’s a strong start for generative AI as a diagnostics assistant. Google’s AI-powered Med-Palm 2 Contributing to drug development According to the Congressional Budget Office, the process of new drug development , which also includes failed drugs. Fortunately, there is evidence that AI has the potential to cut the time needed to design and screen new drugs almost by half, saving the pharma industry around in the process. Additionally, this technology can reduce costs associated with clinical trials by $28 billion per year. costs on average $1 billion to $2 billion $26 billion in annual expenses Pharmaceutical companies can deploy generative AI in healthcare to speed up drug discovery by: Designing and generating new molecules with desired properties that researchers can later evaluate in lab settings Predicting properties of novel drug candidates and proteins Generating virtual compounds with high binding affinity to the target that can be tested in computer simulations to reduce costs Forecasting side effects of novel drugs by analyzing their molecular structure You can find more information on the role of AI in drug discovery and how on our blog. it facilitates clinical trials Real-life examples The rise of strategic partnerships between biotech companies and AI startups is an early sign of generative AI taking over the pharmaceutical industry. Just recently, Recursion Pharmaceuticals for $88 million. One of them, Valence, is known for its generative AI capabilities and will work on designing drug candidates based on small and noisy datasets that are not sufficient for traditional drug discovery methods. acquired two Canadian AI startups Another interesting example comes from the University of Toronto. A research team built a generative AI system, ProteinSGM, that after studying imagery representations of existing protein structures. This tool can produce proteins at a high rate, and then another AI model, OmegaFold, is deployed to evaluate the resulting proteins’ potential. Researchers reported that most of the novel generated sequences fold into real protein structures. can generate novel realistic proteins Automating administrative tasks This is one of the most prominent generative AI use cases in healthcare. Studies show that burnout rate among physicians in the US has . Doctors suffering from this condition are more likely to be involved in incidents endangering their patients and are more inclined to alcohol abuse and having suicidal thoughts. reached a whopping 62% Fortunately, generative AI in healthcare can partially alleviate the burden off the doctors’ shoulders by streamlining administrative tasks. It can simultaneously reduce costs associated with administration, which, according to HealthAffairs, of overall healthcare spending. Here is what generative AI can do: accounts for 15%-30% Extract data from patients’ medical records and populate the corresponding health registries. Microsoft is planning to . This tool will perform various administrative tasks, such as replying to patient messages. integrate generative AI into Epic’s EHR Transcribe and summarize patient consultations, fill this information into the corresponding EHR fields, and produce clinical documentation. Microsoft’s into its clinical transcription software. Doctors can already test the beta version. Nuance integrated generative AI tech GPT-4 Generate structured health reports by analyzing patient information, such as medical history, lab results, scans, etc. Produce treatment recommendations Answer doctors’ queries Find optimal time slots for appointment scheduling based on patients’ needs and doctors’ availability Generate personalized appointment reminders and follow-up emails Review medical insurance claims and predict which ones are likely to be rejected Compose surveys to gather patient feedback on different procedures and visits, analyze it, and produce actionable insights to improve care delivery Real-life example , a medical AI startup, built a generative AI assistant that helps doctors tackle administrative duties more efficiently. This tool can access patient data, including EHRs, insurance claims, and scanned documents, give status updates, recommend care options, and answer doctors’ questions. It can even generate structured documents, such as referral letters and progress notes. Navina Navina has already scored $44 million in funding, which indicates a strong interest from the medical community. Generating synthetic medical data Medical research relies on accessing vast amounts of data on different health conditions. This data is painfully lacking, especially when it comes to rare diseases. Also, such data is expensive to gather, and its usage and sharing are governed by privacy laws. Generative AI in medicine can produce synthetic data samples that can augment real-life health datasets and are not subject to privacy regulations, as the healthcare data doesn’t belong to particular individuals. Artificial intelligence can generate EHR data, scans, etc. Real-life examples A team of German researchers built an AI-powered model, GANerAid, to . This model is based on the GAN approach and can produce medical data with the desired properties even if the original training dataset was limited in size. generate synthetic patient data for clinical trials Another team of scientists experimented with generative AI to . The researchers were motivated by restrictive data privacy regulations and the inability to effectively share patient data between hospitals. They built the EHR-M-GAN model that could derive heterogeneous, mixed-type EHR data (meaning it contains both continuous and discrete values) that realistically represents patient trajectories. synthesize electronic health records Ethical considerations and challenges of generative AI in healthcare Even though tech and consulting giants , we can also see how prominent AI experts, including Tesla CEO Elon Musk and OpenAI CEO Sam Altman, warn of the risks associated with the technology. So, which challenges does generative AI bring to healthcare? continue to invest in AI . AI models’ performance is as good as the dataset they were trained on. If the data does not fairly represent the target population, this will leave room for bias against less represented groups. As generative AI tools train on vast amounts of patient records data, they will inherit any bias present there, and it will be a challenge to detect, let alone eradicate it. Bias . Even though AI presents considerable ethical concerns, there are no official regulations yet to govern the use of this technology. The towards formalizing relevant policies, but this won’t take place in the near future. Lack of regulations US and the EU are working . AI does make mistakes, and in healthcare, the price of such mistakes is rather high. For instance, large language models (LLMs) can . Meaning they can produce syntactically probable outcomes that are factually incorrect. Healthcare organizations will need to decide when to tolerate errors and when to require the AI model to explain its conclusions. For instance, if generative AI is used to assist in cancer diagnosis, doctors are unlikely to adopt such a tool if it can’t justify its recommendations. Accuracy concerns hallucinate . Who is responsible for the final health outcome? Is it the doctor, the AI vendor, the AI developers, or yet another party? Lack of accountability can have a negative impact on motivation and performance. Accountability Ready to enhance your healthcare practice with generative AI? Generative AI algorithms are becoming increasingly powerful. Robert Pearl, a clinical professor at Stanford University School of Medicine, : said “ChatGPT is doubling in power every six months to a year. In five years, it’s going to be 30 times more powerful than it is today. In 10 years, it will be 1,000 times more powerful. What exists today is like a toy. In next-generation tools, it’s estimated there will be a trillion parameters, which is interestingly the approximate number of connections in the human brain.” AI can be a powerful ally, but if misused, it can cause significant damage. Healthcare organizations need to approach this technology with caution. If you are considering deploying AI-based solutions for healthcare, here are three tips to get you started: . Even if you decide to opt for a pre-trained, ready-made AI model, you might still want to retrain it on your proprietary dataset, which needs to be of high quality and representative of the target population. Keep medical data secure at all times and safeguard patient privacy. It would be useful to disclose which dataset an algorithm was trained on as it helps to understand where it will perform well and where it might fail. Prepare your data . Cultivate the concept of responsible AI in your organization. Make sure people know when and how to use the tools and who assumes responsibility for the final outcome. Test the generative AI models on use cases with limited impact before scaling to more sensitive applications. As mentioned earlier, generative AI can make mistakes. Decide where a small failure rate is acceptable and where you can’t afford it. For instance, 98% accuracy can suffice in administrative applications, but it’s unacceptable in diagnostics and patient-facing practices. Devise a framework that will govern the use of generative AI in healthcare at your hospital. Take control of your AI models . AI still needs human guidance, especially in the heavily regulated healthcare sector. remains an essential ingredient for the technology to succeed. The medical and administrative staff will be expected to supervise AI models, so hospitals need to focus on training people for this task. Employees, on the other hand, should be able to reinvent their daily routine, now that AI is a part of it, to use the freed-up time to produce value. Help your employees accept the technology and use it Human-in-the-loop Want to benefit from generative AI but not sure how to proceed? ! We will help you prepare your data, implement the tool, and integrate it into your operations. Drop ITRex a line Also published here.

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