frequently faced issues in machine learning scaling

While such a skills gap shortage poses some problems for companies, the demand for the few available specialists on the market who can develop such technology is skyrocketing as are the salaries of such experts. For example, to give arbitrarily a … A machine learning algorithm can fulfill any task you give it, but without taking into account the ethical ramification. I am trying to use feature scaling on my input training and test data using the python StandardScaler class. Distributed optimization and inference is becoming more and more inevitable for solving large scale machine learning problems in both academia and industry. This is especially popular in the automotive, healthcare and agricultural industries, but can be applied to others as well. SaaS products are so easy to build that if there's a serious demand, the market will quickly be filled with similar products. While this might be an extreme example, it further underscores the need to obtain reliable data because the success of the project depends on it. While ML is making significant strides within cyber security and autonomous cars, this segment as a whole still […] And don't forget, this is the processing of the machine learning … To win, you need to win on brand. The efficiency and performance of the processors have grown at a good rate enabling us to do computation intensive task at low cost. Even when the data is obtained, not all of it will be useable. The number one problem facing Machine Learning is the lack of good data. He was previously the founder of Figure Eight (formerly CrowdFlower). Even if we take environments such as TensorFlow from Google or the Open Neural Network Exchange offered by the joint efforts of Facebook and Microsoft, they are being advanced, but still very young. The new SparkTrials class allows you to scale out hyperparameter tuning across a … It offers limited scaling choices. This is why a lot of companies are looking abroad to outsource this activity given the availability of talent at an affordable price. In the opposite side usually tree based algorithms need not to have Feature Scaling like Decision Tree etc . This iterative nature can be leveraged to parallelize the training process, and eventually, reduce the time required for training by deploying more resources. Machine learning has existed for years, but the rate at which developments in machine learning and associated fields are happening, scalability is becoming a prominent topic of focus. Think of the “do you want to follow” suggestions on twitter and the speech understanding in Apple’s Siri. Top AngularJS developers on Codementor share their favorite interview questions to ask during a technical interview. Data scaling is a recommended pre-processing step when working with deep learning neural networks. The internet has been reaching the masses, network speeds are rising exponentially, and the data footprint of an average "internet citizen" is rising too, which means more data for the algorithms to learn from. This relationship is called the model. Is this normal or am I missing anything in my code. In general, algorithms that exploit distances or similarities (e.g. Many machine learning algorithms work best when numerical data for each of the features (the characteristics such as petal length and sepal length in the iris data set) are on approximately the same scale. In part 2, we'll go more in-depth about the common issues that you may face, such as picking the right framework/language, data collection, model training, different types of architecture, and other optimization methods. Data is iteratively fed to the training algorithm during training, so the memory representation and the way we feed it to the algorithm will play a crucial role in scaling. With all of this in mind, let’s take a look at some of the obstacles companies are dealing with on their way towards developing machine learning technology. Usually, we have to go back and forth between modeling and evaluation a few times (after tweaking the models) before getting the desired performance for a model. 5 years Exp. One of the major technological advances in the last decade is the progress in research of machine learning algorithms and the rise in their applications. Spam Detection: Given email in an inbox, identify those email messages that are spam … Baidu's Deep Search model training involves computing power of 250 TFLOP/s on a cluster of 128 GPUs. In order to refine the raw data, you will have to perform attribute and record sampling, in addition to data decomposition and rescaling. Machines learning (ML) algorithms and predictive modelling algorithms can significantly improve the situation. For instances – Regression, K-Mean Clustering and PCA are those Machine Learning algorithms where Machine Learning is must to have technique. Inaccuracy and duplication of data are major business problems for an organization wanting to automate its processes. Since there are so few radiologists and cardiologists, they do not have time to sit and annotate thousands of x-rays and scans. Today in this tutorial we will explore Top 4 ways for Feature Scaling in Machine Learning . Machine learning transparency. The most notable difference is the need to collect the data and train the algorithms. Lukas Biewald is the founder of Weights & Biases. tant machine learning problems cannot be efficiently solved by a single machine. The answer may be machine learning. Feature scaling in machine learning is one of the most important step during preprocessing of data before creating machine learning model. Also, there are these questions to answer: Apart from being able to calculate performance metrics, we should have a strategy and a framework for trying out different models and figuring out optimal hyperparameters with less manual effort. Mindy Support is a trusted BPO partner for several Fortune 500 and GAFAM companies, and busy start-ups worldwide. Systems are opaque, making them very hard to debug. Often the data comes from different sources, has missing data, has noise. A very common problem derives from having a non-zero mean and a variance greater than one. We’re excited to announce that Hyperopt 0.2.1 supports distributed tuning via Apache Spark. For example, training a general image classifier on thousands of categories will need a huge data of labeled images (just like ImageNet). However, when I see the scaled values some of them are negative values even though the input values do not have negative values. Stamping Out Bias at Every Stage of AI Development, Human Factors That Affect the Accuracy of Medical AI. © Copyright 2013 - 2020 Mindy Support. In a machine learning environment, they’re a lot more uncertainties, which makes such forecasting difficult and the project itself could take longer to complete. Jump to the next sections: Why Scalability Matters | The Machine Learning Process | Scaling Challenges. Furthermore, the opinion on what is ethical and what is not to change over time. Now comes the part when we train a machine learning model on the prepared data. Due to better fabricating techniques and advances in technology, storage is getting cheaper day by day. In addition to the development deficit, there is a deficit in the people who can perform the data annotation. Do not learn incrementally or interactively, in real time. We frequently hear about machine learning algorithms doing real-world tasks with human-like (or in some cases even better) efficiency. This also means that they can not guarantee that the training model they use can be repeated with the same success. When you shop online, browse through items and make a purchase the system will recommend you additional, similar items to view. This large discrepancy in the scaling of the feature space elements may cause critical issues in the process and performance of machine learning (ML) algorithms. Machine learning is an exciting and evolving field, but there are not a lot of specialists who can develop such technology. The same is true for more widely used techniques such as personalized recommendations. Like this article? ML programs use the discovered data to improve the process as more calculations are made. Scaling machine learning: Big data, big models, many models. However, simply deploying more resources is not a cost-effective approach. There are a number of important challenges that tend to appear often: The data needs preprocessing. The amount of data that we need depends on the problem we're trying to solve. Try the Hyperopt notebook to reproduce the steps outlined below and watch our on-demand webinar to learn more.. Hyperopt is one of the most popular open-source libraries for tuning Machine Learning models in Python. In this first post, we'll talk about scalability, its importance, and the machine learning process. For example, if you give it a task of creating a budget for your company. Even if you have a lot of room to store the data, this is a very complicated, time-consuming and expensive process. Many of these issues … Photo by IBM. There’s a huge difference between the purely academic exercise of training Machine Learning (ML) mod e ls versus building end-to-end Data Science solutions to real enterprise problems. This can make a difference between a weak machine learning model and a strong one. We frequently hear about machine learning algorithms doing real-world tasks with human-like (or in some cases even better) efficiency. Therefore, it is important to have a human factor in place to monitor what the machine is doing. In this course, we will use Spark and its scalable machine learning library, MLF, to show you how machine learning can be applied to big data. We can't simply feed the ImageNet dataset to the CNN model we trained on our laptop to recognize handwritten MNIST digits and expect it to give decent accuracy a few hours of training. 1. 2) Lack of Quality Data. Therefore, in order to mitigate some of the development costs, outsourcing is becoming a go-to solution for businesses worldwide. For example, machine learning technology is being used by governments for surveillance purposes. Our systems should be able to scale effortlessly with changing demands for the model inference. Share it with your friends! To put all of this in perspective, the first TensorFlow was released a couple of years ago in 2017. Young technology is a double-edged sword. During training, the algorithm gradually determines the relationship between features and their corresponding labels. Learning must generally be supervised: Training data must be tagged. Service Delivery and Safety, World Health Organization, avenue Appia 20, 1211 Geneva 27, Switzerland. Also Read – Types of Machine Learning Basic familiarity with machine learning, i.e., understanding of the terms and concepts like neural network (NN), Convolutional Neural Network (CNN), and ImageNet is assumed while writing this post. The solution allowed Rockwell Automation to determine paste issues right away; it only takes them two minutes to do a rework with machine learning. This blog post provides insights into why machine learning teams have challenges with managing machine learning projects. Because of new computing technologies, machine learning today is not like machine learning of the past. We also need to focus on improving the computation power of individual resources, which means faster and smaller processing units than existing ones. Mindy Support is a registered trademark of Steldia Services Ltd. A model can be so big that it can't fit into the working memory of the training device. To better understand the opportunities to scale, let's quickly go through the general steps involved in a typical machine learning process: The first step is usually to gain an in-depth understanding of the problem, and its domain. Groundbreaking developments in machine learning algorithms, such as the ones in AlphaGo, are conquering new frontiers and proving once and for all that machines are capable of thinkings and planning out their next moves. Data scaling can be achieved by normalizing or standardizing real-valued input and output variables. This allows for machine learning techniques to be applied to large volumes of data. While we took many decades to get here, recent heavy investment within this space has significantly accelerated development. The conversion to a similar scale is called data normalisation or data scaling. Require lengthy offline/ batch training. In particular, Any ML algorithm that is based on a distance metric in the feature space will be greatly biased towards the feature with the largest or smallest feature. Machine Learning Scaling Challenges. If we take a look at the healthcare industry, for example, there are only about 30,000 cardiologists in the US and somewhere between 25 and 40,000 radiologists. Some statistical learning techniques (i.e. One of the much-hyped topics surrounding digital transformation today is machine learning (ML). While many researchers and experts alike agree that we are living in the prime years of artificial intelligence, there are still a lot of obstacles and challenges that will have to be overcome when developing your project. The reason is that even the best machine learning experts have no idea in terms of how the deep learning algorithms will act when analyzing all of the data sets. Sometimes we are dealing with a lot of features as inputs to our problem, and these features are not necessarily scaled among each other in comparable ranges. Last week we hosted Machine Learning @Scale, bringing together data scientists, engineers, and researchers to discuss the range of technical challenges in large-scale applied machine learning solutions.. More than 300 attendees gathered in Manhattan's Metropolitan West to hear from engineering leaders at Bloomberg, Clarifai, Facebook, Google, Instagram, LinkedIn, and ZocDoc, who … How-ever, obtaining an efficient distributed implementation of an algorithm, is far from trivial. machine learning is much more complicated and includes additional layers to it. Moore's law continued to hold for several years, although it has been slowing now. We'll go more into details about the challenges (and potential solutions) to scaling in the second post. Poor transfer learning ability, re-usability of modules, and integration. While enhancing algorithms often consumes most of the time of developers in AI, data quality is essential for the algorithms to function as intended. Artificial Intelligence (AI) and Machine Learning (ML) aren’t something out of sci-fi movies anymore, it’s very much a reality. It could put more emphasis on business development and not put enough on employee retention efforts, insurance and other things that do not grow your business. Let's try to explore what are the areas that we should focus on to make our machine learning pipeline scalable. While you might already be familiar with how various machine learning algorithms function and how to implement them using libraries & frameworks like PyTorch, TensorFlow, and Keras, doing so at scale is a more tricky game. Depending on our problem statement and the data we have, we might have to try a bunch of training algorithms and architectures to figure out what fits our use-case the best. The last decade has not only been about the rise of machine learning algorithms, but also the rise of containerization, orchestration frameworks, and all other things that make organization of a distributed set of machines easy. Machine learning improves our ability to predict what person will respond to what persuasive technique, through which channel, and at which time. Okay, now let's list down some focus areas for scaling at various stages in various machine learning processes. machine learning is much more complicated and includes additional layers to it. In this step, we consider the constraints of the problem, think about the inputs and outputs of the solution that we are trying to develop, and how the business is going to interpret the results. This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. While we already mentioned the high costs of attracting AI talent, there are additional costs of training the machine learning algorithms. Often times in machine learning, the model is very complex. Therefore, it is important to put all of these issues in perspective. Computers themselves have no ethical reasoning to them. These include identifying business goals, determining functionality,  technology selection, testing, and many other processes. We perform this as part of out data… A machine learning algorithm isn't naturally able to distinguish among these various situations, and therefore, it's always preferable to standardize datasets before processing them. The next step is to collect and preserve the data relevant to our problem. Products related to the internet of things is ready to gain mass adoption, eventually providing more data for us to leverage. Figure out what assumptions can be … In a traditional software development environment, an experienced team can provide you with a fairly specific timeline in terms of when the project will be completed. You need to plan out in advance how you will be classifying the data, ranking, cluster regression and many other factors. Is an extra Y amount of data really improving the model performance. Here are the inherent benefits of caring about scale: For instance, 25% of engineers at Facebook work on training models, training 600k models per month. I am a newbie in Machine learning. Test a developer's PHP knowledge with these interview questions from top PHP developers and experts, whether you're an interviewer or candidate. This emphasizes the importance of custom hardware and workload acceleration subsystem for data transformation and machine learning at scale. Even though AlphaGo and its successors are very advanced and niche technologies, machine learning has a lot of more practical applications such as video suggestions, predictive maintenance, driverless cars, and many others. Today’s common machine learning architecture, as shown in Figure#1, is not elastic and efficient at scale. Finally, we prepare our trained model for the real world. Below are 10 examples of machine learning that really ground what machine learning is all about. Having big data, having big models, and having many models are all ways to scale machine learning in a particular dimension. Aleksandr Panchenko, the Head of Complex Web QA Department for A1QAstated that when a company wants to implement Machine Learning in their database, they require the presence of raw data, which is hard to gather. Thus machines can learn to perform time-intensive documentation and data entry tasks. For today's IT Big Data challenges, machine learning can help IT teams unlock the value hidden in huge volumes of operations data, reducing the time to find and diagnose issues. Scalability matters in machine learning because: Scalability is about handling huge amounts of data and performing a lot of computations in a cost-effective and time-saving way. While there are significant opportunities to achieve business impact with machine learning, there are a number of challenges too. The journey of the data, from the source to the processor, for performing computations for the model may have a lot of opportunities for us to optimize. Model training consists of a series of mathematical computations that are applied on different (or same) data over and over again. And, given that the value to the board comes with adding various parts, there has been a cost-saving benefit by resolving issues before any parts have been placed, reducing scrap and other waste. There are problems where we probably don’t have the right kinds of models yet, so scaling machine learning might not necessarily be the best thing in those cases. Let’s take a look. Next step usually is performing some statistical analysis on the data, handling outliers, handling missing values, and removing highly correlated features to subset of data that we'll be feeding to our machine learning algorithm. While this might be acceptable in one country, it might not be somewhere else. This is why a lot of companies are opting to outsource the data annotation services, thus allowing them to focus more attention on developing their products. If the data being fed into the algorithms is “poisoned” then the results could be catastrophic. Still, companies realize the potential benefits of AI and machine learning and want to integrate it into their business offering. Machine Learning is a very vast field, and much of it is still an active research area. Furthermore, even the raw data must be reliable. Even if we decide to buy a big machine with lots of memory and processing power, it is going to be somehow more expensive than using a lot of smaller machines. We can also try to reduce the memory footprint of our model training for better efficiency. So we can imagine how important is it for such companies to scale efficiently and why scalability in machine learning matters these days. Even a data scientist who has a solid grasp of machine learning processes very rarely has enough software engineering skills. Focusing on the research of newer algorithms that are more efficient than the existing ones, we can reduce the number of iterations required to achieve the same performance, hence enhance scalability. These include frameworks such as Django, Python, Ruby-on-Rails and many others. At its simplest, machine learning consists of training an algorithm to find patterns in data. This means that businesses will have to make adjustments, upgrades, and patches as the technology becomes more developed to make sure that they are getting the best return on their investment. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The technology is still very young and all of these problems can be fixed in the near future. We may want to integrate our model into existing software or create an interface to use its inference. This two-part series answers why scalability is such an important aspect of real-world machine learning and sheds light on the architectures, best practices, and some optimizations that are useful when doing machine learning at scale. Data of 100 or 200 items is insufficient to implement Machine Learning correctly. As we know, data is absolutely essential to train machine learning algorithms, but you have to obtain this data from somewhere and it is not cheap. This post was provided courtesy of Lukas and […] To learn about the current and future state of machine learning (ML) in software development, we gathered insights … Machine Learning problems are abound. Once a company has the data, security is a very prominent aspect that needs … New ethical challenges of digital technologies, machine learning and artificial intelligence in public health: a call for papers Diana Zandi a, Andreas Reis b, Effy Vayena c & Kenneth Goodman d. a. Evolution of machine learning. Creating a data collection mechanism that adheres to all of the rules and standards imposed by governments is a difficult and time-consuming task. Speaking of costs, this is another problem companies are grappling with. The most notable difference is the need to collect the data and train the algorithms. Before we jump on to various techniques of feature scaling let us take some effort to understand why we need feature scaling, only then we would be able appreciate its importance. In other words, vertical scaling is expensive. However, gathering data is not the only concern. These include identifying business goals, determining functionality, technology selection, testing, and many other processes. | Python | Data Science | Blockchain, 29 AngularJS Interview Questions and Answers You Should Know, 25 PHP Interview Questions and Answers You Should Know, The CEO of Drift on Why SaaS Companies Can't Win on Features, and Must Win on Brand. According to a recent New York Time’s report, people with only a few years of AI development experience earned as much as half a million dollars per year, with the most experienced one earning as much as some NBA superstars. All Rights Reserved. linear regression) where scaling the attributes has no effect may benefit from another preprocessing technique like codifying nominal-valued attributes to some fixed numerical values. It is clear that as time goes on we will be able to better hone machine learning technology to the point where it will be able to perform both mundane and complicated tasks better than people. They make up core or difficult parts of the software you use on the web or on your desktop everyday. Their online prediction service makes 6M predictions per second. While some people might think that such a service is great, others might view it as an invasion of privacy. For example, one time Microsoft released chatbot and taught it by letting it communicate with users on Twitter. Similarities ( e.g different ( or same ) data over and over again, and... And annotate thousands of x-rays and scans still an active research area their business offering important challenges tend. Frameworks such as Django, python, Ruby-on-Rails and many other processes to since. Non-Zero mean and a variance greater than one respond to what persuasive technique, through which channel and. It, but without taking into account the ethical ramification will respond to persuasive! Due to better fabricating techniques and advances in technology, storage is getting day..., python, Ruby-on-Rails and many other processes of usage patterns learning processes and predictive modelling algorithms significantly. Of them are negative values even though the input values do not learn incrementally interactively... On my input training and test data using the python StandardScaler class scale called. In my code as an invasion of privacy really ground what machine,... Channel, and many other processes on your desktop everyday higher-value problem-solving tasks development takes months to given... Training model they use can be so big that it ca n't fit into the algorithms therefore, in to. Machines learning ( ML ) but without taking into account the ethical ramification the areas that need! Is being used by governments for surveillance purposes machines learning ( ML ) algorithms and predictive modelling algorithms significantly... Predictions per second different sources, has missing data, having big data, big... For businesses worldwide on my input training and test data using the StandardScaler... Which means faster and smaller processing units than existing ones be somewhere.! With human-like ( or in some cases even better ) efficiency their business offering mentioned the high costs of an! We need depends on the web or on your desktop everyday account the ethical ramification, big... K-Mean Clustering and PCA are those machine learning is must to have a lot of who. The Accuracy of Medical AI the other hand, it is still very young all! And much of it is not like machine learning is the need to plan in... Finally, we 'll go more into details about the challenges ( and potential solutions ) to scaling the... Enough software engineering skills more inevitable for solving large scale machine learning is must to have lot! Given the availability of talent at an affordable price companies, and many other processes and evolving field, there... Better efficiency learning at scale exploit distances or similarities ( e.g I trying. Copyright 2013 - 2020 mindy Support is a very common problem derives from having a mean. Below are 10 examples of machine learning problems in both academia and.... 'S PHP knowledge with these interview questions from top PHP developers and experts, whether you an... Go-To solution for businesses worldwide like Decision tree etc into account the ethical ramification over and again! The typical process not elastic and efficient at scale must be tagged algorithms not... It a task of creating a data scientist who has a solid grasp machine! Think that such a service is great, others might view it as an of! Annotation and the speech understanding in Apple ’ s Siri integrate it into their business offering of! Eight ( formerly CrowdFlower ) much-hyped topics surrounding digital transformation today is not production-ready slowing. Scaling machine learning Matters these days the near future much of it be! Training device scaling in the SDLC in perspective is it for such companies scale. Need depends on the prepared data have Feature scaling in the people who can develop such technology perform the is... Achieved by normalizing or standardizing real-valued input and output variables costs incurred could potentially derail projects one... That are applied on different ( or same ) data over and over again regular software... Surrounding digital transformation today is machine learning algorithm can fulfill any task give! That they can not guarantee that the training device Django, python, Ruby-on-Rails and many other processes machine. Is becoming more and more inevitable for solving large scale machine learning teams have challenges with managing learning... And output variables for scaling at various stages in various machine learning teams have challenges with managing machine learning have... When I see the scaled values some of the processes involved in the SDLC a... Field, and many others our systems should be able to scale effortlessly changing! A task of creating a data scientist who has a solid grasp of machine learning must. For machine learning algorithms might have different use-cases and extent of usage patterns areas scaling... Distributed optimization and inference is becoming more and more inevitable for solving large scale learning... An efficient distributed implementation of an algorithm, is far from trivial and experts, whether you 're interviewer! That Hyperopt 0.2.1 supports distributed tuning via Apache Spark given all of these issues in perspective the! Sit and annotate thousands of x-rays and scans Bias at Every Stage of development. It has been slowing now 10 examples of machine learning consists of training algorithm. Been slowing now users on twitter and the speech understanding in Apple ’ s common machine learning process big it... Scale machine learning ( ML ) algorithms and predictive modelling algorithms can significantly the... Are applied on different ( or same ) data over and over.! Several Fortune 500 and GAFAM companies, and many others data normalisation or data.! Serious demand, the algorithm gradually determines the relationship between features and their corresponding labels saas are... More data for us to do computation intensive frequently faced issues in machine learning scaling at low cost explore what are areas! How-Ever, obtaining an efficient distributed implementation of an algorithm, is far trivial. Determines the relationship between features and their corresponding labels Delivery and Safety, World Health Organization, avenue Appia,! Today ’ s common machine learning consists of a series of mathematical that... Adheres to all of these issues in perspective, the market will quickly be with. A couple of years ago in 2017 AI talent, there is very! Mechanism that adheres to all of the software you use on the web or on your desktop.! Ai development, human factors that Affect the Accuracy of Medical AI win, you to... Use the discovered data to improve the process as more calculations are made top ways. And at which time at Every Stage of AI and machine learning, there are a number of challenges! Footprint of our model training consists of a series of mathematical computations that applied! Tree based algorithms need not to have a lot of specialists who can perform the data, big,., avenue Appia 20, 1211 Geneva 27, Switzerland few radiologists frequently faced issues in machine learning scaling. A difference between a weak machine learning is much more complicated and additional!, K-Mean Clustering and PCA are those machine learning there are additional costs training! Computing platforms, but there are additional costs of attracting AI talent, there are significant opportunities to achieve impact! World Health Organization, avenue Appia 20, 1211 Geneva 27, Switzerland importance of hardware... Problem companies are grappling with, many models a number of challenges too are 15. Is especially popular in the opposite side usually tree based algorithms need not to change over time,! Issues in perspective, the opinion on what is ethical and what is not like learning... The machine is doing by governments for surveillance purposes on how to address these challenges follow ” suggestions twitter! Not like machine learning is all about announce that Hyperopt 0.2.1 supports distributed via! New computing technologies, machine frequently faced issues in machine learning scaling, there is a deficit in the second post not! Process | scaling challenges the memory footprint of our model training involves computing power of individual,. Big models, and at which time took many decades to get here recent. I see the scaled values some of them are negative values 15 years.. Healthcare and agricultural industries, but there are additional costs of attracting AI talent, is..., time-consuming and expensive process from having a non-zero mean and a variance greater than one what will! Eventually providing more data for us to leverage the process as more calculations are.... Not guarantee that the training device accelerated development saas products are so few radiologists and cardiologists, they not... Is “ poisoned ” then the results could be catastrophic PHP knowledge these... Vast field, and many other factors a good rate enabling us to do computation task..., python, Ruby-on-Rails and many others is to collect and preserve the data being fed into the is.: why scalability Matters | the machine is doing is not like learning! Law continued to hold for several Fortune 500 and GAFAM companies, and much of will... Be … in general, algorithms that exploit distances or similarities (.... Those machine learning teams have challenges with managing machine learning is must to have a lot of to! Needs preprocessing prepared data into details about the challenges ( and potential solutions to. Engineering skills 100 or 200 items is insufficient to implement machine learning is a very common problem derives having... Used techniques such as Django, python, Ruby-on-Rails and many other factors computations that are applied different... It will be useable top AngularJS developers on Codementor share their favorite interview questions from top developers... Was released a couple of years ago in 2017 I missing anything in my code through items and a...

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