By Ben Glickman Celanese Corp. will close two units at a facility in Germany as it seeks to cut production costs. The Dallas-based chemical and

Celanese to Close Part of German Facility, Incur Up to $125M in Expenses

Celanese (CE) doesn't possess the right combination of the two key ingredients for a likely earnings beat in its upcoming report. Get prepared with the key expectations.

Analysts Estimate Celanese (CE) to Report a Decline in Earnings: What to Look Out for

An Arlington retiree establishes golf tournament to address food insecurity and to pay tribute to his late daughter.

This golf tournament honors late daughter’s work to fight hunger in Arlington

Celanese Corp (NYSE:CE) recently announced a dividend of $0.7 per share, payable on 2023-11-13, with the ex-dividend date set for 2023-10-27. As investors look forward to this upcoming payment, the spotlight also shines on the company's dividend history,

Celanese Corp's Dividend Analysis

Celanese Corporation, a global leader in chemical innovation, has a rich history that spans over a century. Founded in 1918, the company has evolved and adapted to the changing needs of the industry, establishing itself as a prominent player in the ...

Celanese Corporation: A Global Leader in Chemical Innovation

Celanese Corporation, a global chemical and specialty materials company, has been a key player in the industry for over a century. Its success can be attributed to its ability to adapt and evolve its business model to meet the changing needs of the market.

The Secret Behind Celanese Corporation’s Success: A Deep Dive into its Business Model

Eastman Chemical (EMN) gains on cost-management actions and innovation amid challenges from soft demand and consumer de-stocking in certain markets.

Eastman Chemical (EMN) Benefits From Cost Cuts and Innovation

The adhesive is created as part of DuPont's (DD) efforts to broaden its portfolio of solutions to support the expanding medical device market.

DuPont (DD) Unveils Low-Cyclics Silicone Soft Skin Adhesive

Ganesh Moorthy is president, CEO, and a member of the board of directors of Microchip Technology Inc. Celanese said he will join its board effective Dec. 1, bringing the total number of board members to 11.

Celanese Corp. Adds Microchip Technology CEO to its Board of Directors

Celanese

celanese

How Homology Theory Helps Explain Shocks and Bifurcation in Economic Model

Tech Innovation and Cross-Industry Impact: Syed Aamir Aarfi on AI/ML Integration

Innovación tecnológica e impacto intersectorial: Syed Aamir Aarfi sobre la integración de IA y ML

Tehnološke inovacije i međuindustrijski utjecaj: Syed Aamir Aarfi o AI/ML integraciji

Inovasyon teknoloji ak enpak kwa endistri: Syed Aamir Aarfi sou Entegrasyon AI/ML

技術革新と業界横断的な影響: Syed Aamir Aarfi による AI/ML 統合について

기술 혁신과 산업 간 영향: AI/ML 통합에 대한 Syed Aamir Aarfi

Bokeli ya sika ya tekiniki mpe bopusi na kati ya ba industries: Syed Aamir Aarfi na ntina ya bosangisi ya AI/ML

Technische innovatie en impact op verschillende sectoren: Syed Aamir Aarfi over AI/ML-integratie

Технологические инновации и межотраслевое влияние: Сайед Аамир Аарфи об интеграции ИИ/МО

Tech Innovation uye Cross-Industry Impact: Syed Aamir Aarfi paAI/ML Kubatanidzwa

นวัตกรรมทางเทคโนโลยีและผลกระทบข้ามอุตสาหกรรม: Syed Aamir Aarfi พูดถึงการบูรณาการ AI/ML

Tehniki innowasiýa we pudagyň arasyndaky täsiri: AI / ML integrasiýasynda Saýed Aamir Aarfi

Texnik innovatsiyalar va tarmoqlararo ta'sir: Syed Aamir Aarfi AI/ML integratsiyasiga

Stream Processing - Concepts

Introduction Building generative AI requires working with a variety of developing machine learning and AI technologies. It also requires buckets of patience. These two requirements are not unrelated. Through working alongside extremely talented machine learning experts from different firms building generative AI applications, it’s clear that there are a number of ‘yikes’ aspects to developing fully realized generative AI tools. In this article, I’ll briefly summarise five pitfalls to avoid when developing a generative AI application. Don’t Assume the Outputs of LLMs Will Be Accurate This may seem obvious, but I was surprised at how unreliable the output of commercial LLMs could be. When testing major LLMs (in particular, ChatGPT, Anthropic’s Claude AI, and Amazon Bedrock) on complex financial datasets, I noted that they can generate errors or hallucinations at a rate of one per page. LLMs are guilty of often generating information that sounds plausible but is false. For example, when testing financial data, I found that LLMs produced financial ratios like ‘Non-operating income and expense’. These sound legitimate but don’t exist in the document and are completely hollow financial terms. Though effective prompt engineering can mitigate some of these hallucinations, many of them occur for initially unexplainable reasons (hence why every LLM has a recommendation for manually reviewing its output somewhere in its user interface and documentation). OCR (Optical Character Recognition)—the technology that converts images to machine-readable text—also generates a high rate of errors. It is challenging to develop a mechanism to combat OCR infidelity for each model. AI-based adaptive learning for user-flagged errors can be a helpful strategy but requires huge amounts of manual intervention. Overall, the lack of a calibrated confidence score in commercial LLMs is a huge limitation. Application developers must develop their own methods for calculating confidence and validating errors. 2. Don’t Be Complacent With Your Production Credits Any generative AI dependent on LLM production credits (such as Google’s Tensor Processing Units (TPUs)) needed to be technologically regulated to avoid spending more than accounted for - potentially bankrupting the company or developer. Bankruptcy through overenthusiastic spending of production credits might seem like an urban myth - certainly, there are no noted cases of production credits running amok - but better safe than sorry, right? There are several ways to stretch out your production credits to their fullest potential: Take advantage of short-lived, discounted, preemptible VMs from your cloud application provider (but only if your application is designed to handle interruptions and then resume from checkpoints). Leverage spot instances for non-critical workloads to significantly reduce costs while automatically shutting down idle instances. Cloud provider tools can be very helpful for this. Where appropriate, you could experiment with open-source frameworks that don’t require production credits. I’m very pro the democratic approach of open source models and their potential for collaborative innovation. And, of course, open-source models can be accessed for free. 3. Don’t Forget to Test User Experience (UE) Again, it may seem obvious, but when stuck looking at a profusion of code, it’s easy to forget that a real human being will be using the product. In a production environment, consider the pathways the user will take, even in complex and code-heavy environments. Don’t be like Google Bard, whose generative AI model couldn’t answer simple user questions about space upon release or, later, what to put on pizza (hint: it’s not glue). Many traditional UX testing tools can be adapted for generative AI products - such as a card sorting tool. Another effective way to test the technology is with a human-in-the-loop system, like beta testers or reviewers. At the moment, the company I’m with is using human beta testers to test their new generative AI technology. The testers use the tool for free - and they can harvest the usage patterns. 4. Don’t Forget the Importance of Training Data Clean, normalize, and potentially enrich your data to improve the training process. Techniques like tokenization and feature engineering might be helpful. Generally, the more data, the better (the AI algorithms I used were from a document store of 25 million documents, for example), but too much data may result in overfitting or computational bottlenecks. Slightly off-topic, but in the future, it’s possible that wrangling with training data may not be an issue. Promising advancements like AutoML automatically cleanse training data and assess the model’s performance using a number of techniques like meta-learning, Bayesian inference, and neural architecture search. For SMEs with limited coding resources, AutoML might be a promising innovation. 5. Don’t Choose the Wrong Scale Modeling Strategy There are many ways to scale a model. For example, you might split the model across multiple machines (model parallelism) or replicate the model across multiple machines (data parallelism). Complex models will likely benefit from the model’s parallelism’s device distribution; larger datasets or models with smaller architectures may benefit from increased throughput. Another consideration is whether to upgrade compute resources on a single machine, such as by increasing GPU memory (vertical scaling) or adding more resources (e.g. GPUs) - also known as horizontal scaling. Don’t forget to containerize your generative AI application to ensure consistent behaviors across different environments. Of course, rigorous testing and validation after model scaling is a must. To ensure it can handle the increased load, consider trying a variety of by-the-book testing plus load and stress testing. Summary It’s unrealistic to expect building a generative AI product to work without encountering at least one of these challenges. Ultimately, each of these challenges presents a fork in decision-making. At each juncture, the choice you make gets you one step closer to building a mature, production-ready model. Working with the latest advances in academic and industrial machine learning will help counter some of the typical frustrations, as machine learning’s competitive landscape is constantly pushing out new innovations. I organize the London Machine Learning Meetup (the largest community of AI experts in Europe), which is a free community that hosts events that unpack the latest technical advances in machine learning. Above all, accuracy and cost-effectiveness don’t have to be mutually exclusive. With controlled and strategic experimentation, building a generative AI product can be more rewarding and less frustrating than you might think. Good luck! Introduction Introduction Building generative AI requires working with a variety of developing machine learning and AI technologies. It also requires buckets of patience. These two requirements are not unrelated. Through working alongside extremely talented machine learning experts from different firms building generative AI applications, it’s clear that there are a number of ‘yikes’ aspects to developing fully realized generative AI tools. In this article, I’ll briefly summarise five pitfalls to avoid when developing a generative AI application. Don’t Assume the Outputs of LLMs Will Be Accurate This may seem obvious, but I was surprised at how unreliable the output of commercial LLMs could be. When testing major LLMs (in particular, ChatGPT, Anthropic’s Claude AI, and Amazon Bedrock) on complex financial datasets, I noted that they can generate errors or hallucinations at a rate of one per page. LLMs are guilty of often generating information that sounds plausible but is false. sounds For example, when testing financial data , I found that LLMs produced financial ratios like ‘Non-operating income and expense’. These sound legitimate but don’t exist in the document and are completely hollow financial terms. testing financial data testing financial data Though effective prompt engineering can mitigate some of these hallucinations, many of them occur for initially unexplainable reasons (hence why every LLM has a recommendation for manually reviewing its output somewhere in its user interface and documentation). effective prompt engineering effective prompt engineering OCR (Optical Character Recognition)—the technology that converts images to machine-readable text—also generates a high rate of errors. It is challenging to develop a mechanism to combat OCR infidelity for each model. AI-based adaptive learning for user-flagged errors can be a helpful strategy but requires huge amounts of manual intervention. OCR OCR Overall, the lack of a calibrated confidence score in commercial LLMs is a huge limitation. Application developers must develop their own methods for calculating confidence and validating errors. 2. Don’t Be Complacent With Your Production Credits 2. Don’t Be Complacent With Your Production Credits Any generative AI dependent on LLM production credits (such as Google’s Tensor Processing Units (TPUs)) needed to be technologically regulated to avoid spending more than accounted for - potentially bankrupting the company or developer. Bankruptcy through overenthusiastic spending of production credits might seem like an urban myth - certainly, there are no noted cases of production credits running amok - but better safe than sorry, right? There are several ways to stretch out your production credits to their fullest potential: Take advantage of short-lived, discounted, preemptible VMs from your cloud application provider (but only if your application is designed to handle interruptions and then resume from checkpoints). Take advantage of short-lived, discounted, preemptible VMs from your cloud application provider (but only if your application is designed to handle interruptions and then resume from checkpoints). Leverage spot instances for non-critical workloads to significantly reduce costs while automatically shutting down idle instances. Cloud provider tools can be very helpful for this. Leverage spot instances for non-critical workloads to significantly reduce costs while automatically shutting down idle instances. Cloud provider tools can be very helpful for this. Leverage spot instances Where appropriate, you could experiment with open-source frameworks that don’t require production credits. I’m very pro the democratic approach of open source models and their potential for collaborative innovation. And, of course, open-source models can be accessed for free. Where appropriate, you could experiment with open-source frameworks that don’t require production credits. I’m very pro the democratic approach of open source models and their potential for collaborative innovation. And, of course, open-source models can be accessed for free. open-source frameworks 3. Don’t Forget to Test User Experience (UE) Again, it may seem obvious, but when stuck looking at a profusion of code, it’s easy to forget that a real human being will be using the product. In a production environment, consider the pathways the user will take, even in complex and code-heavy environments. Don’t be like Google Bard, whose generative AI model couldn’t answer simple user questions about space upon release or, later, what to put on pizza (hint: it’s not glue ). simple user questions about space simple user questions about space it’s not glue it’s not glue Many traditional UX testing tools can be adapted for generative AI products - such as a card sorting tool. Another effective way to test the technology is with a human-in-the-loop system, like beta testers or reviewers. At the moment, the company I’m with is using human beta testers to test their new generative AI technology. The testers use the tool for free - and they can harvest the usage patterns. card sorting tool. card sorting tool. 4. Don’t Forget the Importance of Training Data Clean, normalize, and potentially enrich your data to improve the training process. Techniques like tokenization and feature engineering might be helpful. tokenization tokenization Generally, the more data, the better (the AI algorithms I used were from a document store of 25 million documents, for example), but too much data may result in overfitting or computational bottlenecks. Slightly off-topic, but in the future, it’s possible that wrangling with training data may not be an issue. Promising advancements like AutoML automatically cleanse training data and assess the model’s performance using a number of techniques like meta-learning, Bayesian inference, and neural architecture search. For SMEs with limited coding resources, AutoML might be a promising innovation. AutoML AutoML 5. Don’t Choose the Wrong Scale Modeling Strategy There are many ways to scale a model. For example, you might split the model across multiple machines (model parallelism) or replicate the model across multiple machines (data parallelism). Complex models will likely benefit from the model’s parallelism’s device distribution; larger datasets or models with smaller architectures may benefit from increased throughput. Another consideration is whether to upgrade compute resources on a single machine, such as by increasing GPU memory ( vertical scaling ) or adding more resources (e.g. GPUs) - also known as horizontal scaling. Don’t forget to containerize your generative AI application to ensure consistent behaviors across different environments. vertical scaling vertical scaling Of course, rigorous testing and validation after model scaling is a must. To ensure it can handle the increased load, consider trying a variety of by-the-book testing plus load and stress testing. Summary It’s unrealistic to expect building a generative AI product to work without encountering at least one of these challenges. Ultimately, each of these challenges presents a fork in decision-making. At each juncture, the choice you make gets you one step closer to building a mature, production-ready model. Working with the latest advances in academic and industrial machine learning will help counter some of the typical frustrations, as machine learning’s competitive landscape is constantly pushing out new innovations. I organize the London Machine Learning Meetup (the largest community of AI experts in Europe), which is a free community that hosts events that unpack the latest technical advances in machine learning. London Machine Learning Meetup London Machine Learning Meetup Above all, accuracy and cost-effectiveness don’t have to be mutually exclusive. With controlled and strategic experimentation, building a generative AI product can be more rewarding and less frustrating than you might think. Good luck!

Building Production-Ready Generative AI: Five Things Not to Do

SQL vs T-SQL: Key Differences and Use Cases

One of my favorite interview questions is “What do such words as async and await tell you?” because it opens up an opportunity to have an interesting discussion with an interviewee… Or it doesn’t because they float on this topic. In my opinion, it is drastically important to understand why we use this technique. I feel like many developers prefer to rely on the “it is the best practice” statement and use asynchronous methods blindly. This article shows the difference between asynchronous and synchronous methods in practice. Tools .NET Web API application (test target) 2 Azure SQL Databases 2 Azure App Service on Windows (hosts the application) Azure App Insights (to gather metrics) locust framework (to simulate user load). Configuration I will run a benchmark in the following way. Two independent locust instances are running on two machines. Locust instances simulate a user that does the following: The user from the locust host 1 hits the synchronous endpoint of the App Service 1, gets the response, and stays idle for 0.5–1 seconds (the exact time delay is random). Repeats till the end of the experiment. The user from the locust host 2 behaves precisely the same, with only one difference — he hits the asynchronous endpoint of App Service 2. Under the hood, each App Service connects to its own database and executes a SELECT query that takes five seconds and returns a few rows of data. See the controller’s code below for references. I’ll use Dapper to make a call to the database. I’d like to draw your attention to the fact that the asynchronous endpoint calls the database asynchronously as well (QueryAsync<T>). It's worth adding that I deploy the same code to both app services. During the test, the number of users grows evenly to the target number (Number of Users). The speed of growth is controlled by a Spawn Rate parameter (number of unique users to join per second) — the higher the number is, the quicker the users are being added. The spawn rate is set to 10 users/s for all experiments. All experiments are limited to 15 minutes. You may find machine configuration details in the Technical Details section of the article. Metrics requests per minute — shows the number of requests that the application actually processed and returned a status code.
thread count — shows the number of threads the app service consumes.
median response time, ms The red lines refer to asynchronous, and the blue lines — to the synchronous endpoint, respectively. That’s it about theory. Let’s start. Experiment #1 number of users: 75 (per service) We can see that both endpoints perform similarly — handling around 750 requests per minute with a median response time of 5200 ms. The most fascinating graph in this experiment is a thread trend. You can see significantly higher numbers for the synchronous endpoint (a blue graph) — more than 100 threads! That’s expected, though, and matches the theory — when a request comes in, and the application makes a call to the database, the thread is blocked because it has to wait for a roundtrip to complete. Therefore, when another request comes in, the application has to produce a new thread to handle it. The red graph — the asynchronous endpoint thread count— proves different behavior. When a request comes in and the application makes a call to the database, the thread returns to a thread pool instead of being blocked. Therefore, when another request comes in, this free thread is reused. Despite incoming requests growing, the application doesn’t require any new threads, so their count remains the same. It’s worth mentioning the 3rd metric — median response time. Both endpoints showed the same result — 5200 ms. So, there is no difference in terms of performance. Now, it is time to pull up the stakes. Experiment #2 number of users: 150 We doubled the load. The asynchronous endpoint handles this task successfully—its request per minute rate floats around 1500. The synchronous brother eventually reached a comparable number of 1410. But if you look at the graph below, you will see that it took 10 minutes! The reason is that the synchronous endpoint reacts to a new user's arrival by creating another thread, but users are being added to the system (just to remind you that the Spawn Rate is 10 users/s) quicker than the web server can adapt. That’s why it queued so many requests at the very beginning. Unsurprisingly, the thread count metric is still around 34 for the asynchronous endpoint, while it increased from 102 to 155 for the synchronous one. The median response time degraded similarly to the request per minute rate — the synchronous response time was much higher at the beginning of the experiment. If I had kept the test for 24 hours, the median numbers would become even. Experiment #3 number of users: 200 The third experiment is intended to prove trends revealed during the second one —we can see further degradation of the synchronous endpoint. Conclusion Using asynchronous instead of synchronous operations doesn’t directly improve performance or user experience. First, it enhances stability and predictability under pressure. In other words, it raises the load threshold so the system can process more before it degrades. Appendix #1. Technical Details Azure App Service: B1, 100 ACU, 1,75 Gb Memory, A-Series compute equivalent.
Azure SQL Database: Standard S4: 200 DTUs, 500 Mb storage.
SQL Connection settings: Max Pool Size=200. Appendix #2. Notes To achieve the cleanest test result, I should have run tests from 2 VMs located in the same network where the target App Services sit. However, I assumed that a network lag would impact both apps in a more or less similar way. Therefore, it cannot jeopardize the main goal — comparing how asynchronous and synchronous methods behave. Appendix #3. Bonus Experiment What did I hack to force the synchronous endpoint to perform almost as asynchronous and plot the graph below (experiment conditions are the same as in the third one — 200 users)? One of my favorite interview questions is “What do such words as async and await tell you?” because it opens up an opportunity to have an interesting discussion with an interviewee… Or it doesn’t because they float on this topic. In my opinion, it is drastically important to understand why we use this technique. async await I feel like many developers prefer to rely on the “it is the best practice” statement and use asynchronous methods blindly. This article shows the difference between asynchronous and synchronous methods in practice. Tools .NET Web API application (test target) .NET Web API application (test target) 2 Azure SQL Databases 2 Azure SQL Databases 2 Azure App Service on Windows (hosts the application) 2 Azure App Service on Windows (hosts the application) Azure App Insights (to gather metrics) Azure App Insights (to gather metrics) locust framework (to simulate user load). locust framework (to simulate user load). locust Configuration I will run a benchmark in the following way. Two independent locust instances are running on two machines. Locust instances simulate a user that does the following: The user from the locust host 1 hits the synchronous endpoint of the App Service 1, gets the response, and stays idle for 0.5–1 seconds (the exact time delay is random). Repeats till the end of the experiment. The user from the locust host 1 hits the synchronous endpoint of the App Service 1, gets the response, and stays idle for 0.5–1 seconds (the exact time delay is random). Repeats till the end of the experiment. synchronous App Service 1, The user from the locust host 2 behaves precisely the same, with only one difference — he hits the asynchronous endpoint of App Service 2. The user from the locust host 2 behaves precisely the same, with only one difference — he hits the asynchronous endpoint of App Service 2. asynchronous App Service 2. Under the hood, each App Service connects to its own database and executes a SELECT query that takes five seconds and returns a few rows of data. See the controller’s code below for references. I’ll use Dapper to make a call to the database. I’d like to draw your attention to the fact that the asynchronous endpoint calls the database asynchronously as well ( QueryAsync<T> ). QueryAsync<T> It's worth adding that I deploy the same code to both app services. During the test, the number of users grows evenly to the target number ( Number of Users ). The speed of growth is controlled by a Spawn Rate parameter (number of unique users to join per second) — the higher the number is, the quicker the users are being added. The spawn rate is set to 10 users/s for all experiments. Number of Users Spawn Rate spawn rate All experiments are limited to 15 minutes. You may find machine configuration details in the Technical Details section of the article. Metrics requests per minute — shows the number of requests that the application actually processed and returned a status code. thread count — shows the number of threads the app service consumes. median response time, ms requests per minute — shows the number of requests that the application actually processed and returned a status code. requests per minute thread count — shows the number of threads the app service consumes. thread count median response time, ms median response time, ms The red lines refer to asynchronous, and the blue lines — to the synchronous endpoint, respectively. That’s it about theory. Let’s start. Experiment #1 Experiment #1 number of users: 75 (per service) number of users : 75 (per service) number of users We can see that both endpoints perform similarly — handling around 750 requests per minute with a median response time of 5200 ms. The most fascinating graph in this experiment is a thread trend. You can see significantly higher numbers for the synchronous endpoint (a blue graph) — more than 100 threads! That’s expected, though, and matches the theory — when a request comes in, and the application makes a call to the database, the thread is blocked because it has to wait for a roundtrip to complete. Therefore, when another request comes in, the application has to produce a new thread to handle it. The red graph — the asynchronous endpoint thread count— proves different behavior. When a request comes in and the application makes a call to the database, the thread returns to a thread pool instead of being blocked. Therefore, when another request comes in, this free thread is reused. Despite incoming requests growing, the application doesn’t require any new threads, so their count remains the same. It’s worth mentioning the 3rd metric — median response time . Both endpoints showed the same result — 5200 ms. So, there is no difference in terms of performance. median response time Now, it is time to pull up the stakes. Experiment #2 number of users: 150 number of users : 150 number of users We doubled the load. The asynchronous endpoint handles this task successfully—its request per minute rate floats around 1500. The synchronous brother eventually reached a comparable number of 1410. But if you look at the graph below, you will see that it took 10 minutes! request per minute The reason is that the synchronous endpoint reacts to a new user's arrival by creating another thread, but users are being added to the system (just to remind you that the Spawn Rate is 10 users/s) quicker than the web server can adapt. That’s why it queued so many requests at the very beginning. Spawn Rate Unsurprisingly, the thread count metric is still around 34 for the asynchronous endpoint, while it increased from 102 to 155 for the synchronous one. The median response time degraded similarly to the request per minute rate — the synchronous response time was much higher at the beginning of the experiment. If I had kept the test for 24 hours, the median numbers would become even. thread count median response time request per minute Experiment #3 number of users: 200 number of users : 200 number of users The third experiment is intended to prove trends revealed during the second one —we can see further degradation of the synchronous endpoint. Conclusion Using asynchronous instead of synchronous operations doesn’t directly improve performance or user experience. First, it enhances stability and predictability under pressure. In other words, it raises the load threshold so the system can process more before it degrades. Appendix #1. Technical Details Azure App Service: B1, 100 ACU, 1,75 Gb Memory, A-Series compute equivalent. Azure SQL Database: Standard S4: 200 DTUs, 500 Mb storage. SQL Connection settings: Max Pool Size=200. Azure App Service: B1, 100 ACU , 1,75 Gb Memory, A-Series compute equivalent. ACU Azure SQL Database: Standard S4: 200 DTUs, 500 Mb storage. SQL Connection settings: Max Pool Size=200. Appendix #2. Notes To achieve the cleanest test result, I should have run tests from 2 VMs located in the same network where the target App Services sit. However, I assumed that a network lag would impact both apps in a more or less similar way. Therefore, it cannot jeopardize the main goal — comparing how asynchronous and synchronous methods behave. Appendix #3. Bonus Experiment Appendix #3. Bonus Experiment What did I hack to force the synchronous endpoint to perform almost as asynchronous and plot the graph below (experiment conditions are the same as in the third one — 200 users)?

Async vs Sync Benchmark (.NET): The Difference Between Asynchronous and Synchronous Methods

مقارنة بين Async وSync (.NET): الفرق بين الطرق المتزامنة وغير المتزامنة

Async vs Sync Benchmark (.NET): Asinxron və Sinxron Metodlar Arasındakı Fərq

Async vs Sync Benchmark (.NET): la diferencia entre métodos asincrónicos y sincrónicos

Async vs Sync Benchmark (.NET): a diferenza entre os métodos asíncronos e síncronos

Async vs Sync Benchmark (.NET): Az aszinkron és a szinkron módszerek közötti különbség

Async vs Sync Benchmark (.NET): la differenza tra metodi asincroni e sincroni

非同期 vs 同期ベンチマーク (.NET): 非同期メソッドと同期メソッドの違い

Async vs Sync Benchmark (.NET)៖ ភាពខុសគ្នារវាងវិធីសាស្ត្រ Asynchronous និង Synchronous Methods

Async vs Sync Benchmark (.NET): Асинхрондуу жана синхрондуу методдордун ортосундагы айырма

Async vs Sync Benchmark (.NET): różnica między metodami asynchronicznymi i synchronicznymi

Async vs Sync Benchmark (.NET): Rozdiel medzi asynchrónnymi a synchrónnymi metódami

Async vs Sync Benchmark (.NET): Musiyano uripakati peAsynchronous uye Synchronous Methods

16 Key Principles for Tech-Led Startups to Scale Efficiently

Meet New & Improved BigQuery: Single, Unified AI-Ready Data Platform

At the Potomac, Where DC, the Analog Political National Capital, and VC, the Digital Capital, Meet

"When You Take VC Funding, the Clock Often Starts Ticking" Entrepreneur Jack Smith on Exiting Vungle

"আপনি যখন ভিসি ফান্ডিং নেন, ঘড়ি প্রায়শই টিক টিক শুরু করে" উদ্যোক্তা জ্যাক স্মিথ এক্সিটিং ভাংলে

„Wenn man VC-Finanzierung annimmt, beginnt die Uhr oft zu ticken“ – Unternehmer Jack Smith über den Ausstieg aus Vungle

"Cuando se obtiene financiación de capital de riesgo, el tiempo a menudo empieza a correr" El empresario Jack Smith habla sobre su salida de Vungle

"Lorsque vous prenez un financement en capital-risque, le temps commence souvent à tourner" L'entrepreneur Jack Smith à propos de sa sortie de Vungle

"जब आप वी.सी. फंडिंग लेते हैं, तो अक्सर समय की गति बढ़ने लगती है" उद्यमी जैक स्मिथ ने वुंगल से बाहर निकलने पर कहा

「VC から資金を調達すると、時間が刻々と動き始めることが多い」起業家ジャック・スミスが Vungle からの撤退について語る

"VC 자금 지원을 받으면 시계가 똑딱거리기 시작하는 경우가 많습니다." 기업가 Jack Smith, Vungle 종료

“Quando você recebe financiamento de capital de risco, o relógio geralmente começa a contar” Empreendedor Jack Smith sobre a saída do Vungle

«Когда вы берете венчурное финансирование, часы часто начинают тикать» Предприниматель Джек Смит о выходе из Vungle

Girişimci Jack Smith, Vungle'dan Çıkış Konusunda "VC Finansmanı Aldığınızda Zaman Sıklıkla İşlemeye Başlıyor"

"Khi bạn nhận tiền từ VC, đồng hồ thường bắt đầu tích tắc" Doanh nhân Jack Smith khi rời khỏi Vũngle

“当你接受风险投资时，时钟通常开始滴答作响”企业家杰克·史密斯谈退出 Vungle

Stories, News & Mentions about CELANESE

Celanese’s Stories on HackerNoon

Celanese's News & Mentions