How to Develop a Training Data Strategy for Machine Learning

February 12, 2021

A constructive data strategy can ensure a mechanism in providing the steady pipeline of data necessary for machine learning models for constant updates. A training data strategy alone may not guarantee the success of an AI system, but it will help ensure organizations are better positioned to leverage the benefits of AI.

Introduction to AI and ML

One of the latest technological trends talked about the most in the IT industry is Artificial intelligence (AI). AI is the concept of machines and robots simulating human decisions in the world of computing. Machine Learning (ML), on the other hand, is an approach to formulate AI. An AI system is a set of instructions programmed to perform a specific task. Machine Learning is the ability of a machine to intellectually understand, parse, extract, and learn from the set of data. Machine Learning thus intended to perform the task accurately without human intervention.

The growth rate of these technologies in the industries is overwhelming. As per the IDC forecast, the spending predicted for AI and ML was to grow from $12B in 2017 to $57.6B by 2021. According to a report published by PwC and CB Insights, in the year 2018 alone, the $9.3 billion VC funding has flown to AI-related ventures. Big businesses are seen investing in the development of AI or acquiring AI companies. The unicorns like Paytm, Swiggy, and Oyo are actively engaged in these moves.

Indian origin E2E Networks promotes the cloud servers under Cloud business. They are branded in cPanel Servers and are bundled with security and WHM Integrated pre-installed VM's. These servers are capable of coping with the needs for ML-based operations.  

The development of an AI system is governed by a set of examples fed into the system to help learn. The examples utilize high-quality data. The AI systems get trained with these examples. Therefore, high-quality training data can form reliable systems. Here accurate conclusions make the right decisions in computing.  

Machine Learning and Data

Data is a driving force to Machine learning models. A high-quality training data sets the foundation for smart ML systems. In case of poor data, even a high-performing algorithm fails to train the AI model. A robust ML model, when trained on poor data in terms of irrelevant or incorrect data in the early stages could fail to thrive. The results may deviate from the ideal range. Such ML models would turn unreliable. The poor data costs more for maintenance. IBM estimated that the data quality data costs the United States roughly $3.1 trillion per year.

Therefore, quality training data is considered an essential element in machine learning. The concept of “training data” refers to the base data used in the initial phases in developing an ML model. This is the stage from where the model creates and refines its rules.  

Data preparation is a standard procedure that systematically uses your dataset for machine learning consumption. In general, the data preparation aims at establishing the right mechanism of data collection.

Why Train Data?

It is a fact that the "training data" is a crucial aspect of any machine learning model. In industries, the data teams work towards challenging tasks of acquiring, classifying, labeling, and preparing a set of useful training data. Any compromises on the volume or quality of training data risk insignificant results later. A strong base work in nurturing data always rewards healthy ML models. Therefore, with the right combination of resources and foolproof processes coupled with technology aids, you can always transform your data operations to harvest quality training data. Seamless coordination is required between data experts, your ML project management, technology, and your labeling tools.

What is training data?

In the machine learning domain, training data means the data you will use to train your algorithm or an ML model. The foremost requirement in the training data is to set a protocol. Human involvement, such as by data experts, is necessary to analyze the process of the data consumption for machine learning. The type of ML algorithms adopted decides the categories of the expertise required. Also, the level of problem intended to be solved by the ML model determines the need of people involved to design the training data. Training data is a continuous process. As the real-world conditions go evolving, the initial training dataset may tend to lack accuracy. Therefore, you are required to fine-tune and update your training data. Ensure the latest changes reflect in your model. 

How is training data used in machine learning?

In the computing world, the pre-defined parameters that feed specific attributes from the data control conventional algorithms. Machine learning algorithms, on the other hand, for the specialty of their working patterns differ from traditional algorithms. Training data involve the algorithms to compete with the subject examples. The data labeling and quality determine the learning performance of ML models. The accuracy and precision of the predictions decide the adaptability of the ML algorithm. For example, transaction history data of an e-commerce site, labeled with product attributes, can be used to train the data. It helps to identify the domestic needs of the user. In particular, ideal training data is the set dataset used for training your ML algorithm or model.

The test data, usually in the name of validation, is used to work with the algorithm and parameters of the model you develop.

The sample data used to assess the algorithms that train the machine.  In turn, they predict subsequent possible results derived from trained data.

The quality of properly labeled data in diverged volume always results better. Say, if you trained your model using training data from 1,000 transactions, its performance likely would stay high as against that of a model trained on data from 100 transactions. 

In terms of computing requirements, massively parallel processing is needed to train ML models. For an average ML model, traditional CPU cores on general-purpose servers would take months at a time. Whereas, a GPU based deployment speeds up machine learning workloads considerably. It will perform the same operation in hours and days instead of weeks and months. Lately, GPUs with several hundred cores are being developed. They are capable of handling multiple logic operations fast using massively parallel processing, resulting in a time reduction economically viable. One such innovative step by E2E Networks is designing a range of modern GPUs (source: for AI/ML. They offer a high-speed capability required for ML systems, in comparison to the traditional general-purpose processors. Moreover, the cloud-based GPUs are the best alternative to suit your machine learning requirements. They offer the best solution for training data and ML workloads. Also, the option of pay as you use simplifies the cost burden.

Training Data Strategy

Top executives in the industries have a fair understanding of ML and AI technology today. Businesses started investing in ML and applied development and on the verge of adopting AI in their business models. 

ML Models in AI systems are developed with algorithms that best learn from a wide range of examples. More the high-quality examples fed, the more reliable the ML model learns and results accurately. Limited or low-quality data often tend to introduce or influence bias and perform poorly and costs high. As estimated, the poor data quality in the United States costs the country’s economy nearly $3.1 trillion annually.

A well-defined strategy for procuring and structuring the data is mandatory for AI systems. The foremost step toward developing an AI system is to plan a strategy for training data. It is the foremost step toward capturing the value of an AI system. This approach essentially includes --setting your budget, identifying your data sources, labeling the classified data, ensuring the quality of data, and ensuring security. Primarily, the prerequisite to develop a decent ML model is adopting quality data. It means the data you can train, test, validate, and tune AI systems, in a given time.

Setting a Strategy enables Successful AI

A study by IHS Market recently revealed that 87% of businesses are adopting at least one or other form of transformative technologies like AI, and only 26% believe that requisite business models are in place ready to capture the fullest value from these technologies.

The below are the guiding indicators for building a successful training data strategy.

1. Budgeting

For any business, the cost factor acts as a catalyst. Budget, therefore, decides the level of adoption of technology on demand. AI is a prestigious trend in automation, the investment criteria before adopting a transition in the business practices need to be studied thoroughly. Management does a viable assessment before the budget allocation is put on paper. Note that rolling in a machine learning program is a long-term investment. Therefore, realizing a great return requires a long-term strategy.

Establish a Budget for Training Data

While deciding on the budgeting, it is important to be realistic about the time and money required to get the project realization, maintenance over time, and evaluate the features and functioning inline with your business, to keep the solution relevant and useful to your stakeholder. This data has been labeled their attributes manually as annotators to identify the contents. The categories, such as trees, buildings, roads, people, vehicles, etc., of the image. Going forward, depending on the type of solution, your ML has intended to build, your model needs to be periodically refreshed with data updates. After the training items and refresh rates specifications are in place, you are ready to evaluate options for sourcing data, the volume of data, and derive a budget.

2. Data sourcing

The level of the system you proposed in developing determines the type of data. The sourcing of data for your project thus needs to suit its adoption and availability of data over the period.

Source Appropriate Data

Selecting a data type is dependent on what AI solution you build. The data sources include public datasets, real-world usage data, surveyed data, and synthetic data. For example, a search solution requires text data you annotate.

Public datasets

Public datasets, on the other hand, are openly available data from community organizations, businesses, and charitable or commercial agencies. The sets in the public domain might contain data of weather history, healthcare records, land surveys, transportation and commodity price indexes, etc.  Most startups and businesses take advantage of public datasets to ship ML-based products to their users using the ML techniques. GitHub is a good example of a compilation of public datasets. 

An Open Images dataset from Google collects tagged images voluntarily submitted by the users. It saves redundant labeling pictures used to train an image recognition algorithm. The same analogy applies to datasets for speech and text recognition. 

3. Annotation Resources

Annotation is an important step in marking data for intelligence. Analyze what important considerations decide to either outsource your data annotation or source it internally.

The common types of data fed in machine learning are numeric, text, graphics, image, audio, speech, and video. Before making use of these data items in ML, they must be annotated or labeled to identify what they are. Annotation attributes help the model to decide what to do with each piece of data.  For example, data item of type voice data uses a recording string, “book SFO flight tonight.” The annotation likely triggers the system to check the flight schedule for San Francisco when it hears “SFO flight,” narrows down to tonight availability, when hears “tonight”, reports back, appropriately.   

Select Appropriate Technology

Training data should be more intricate or nuanced. It fetches better results. Most businesses need a huge volume of high-quality training data, sourced fast, and at scale. This could be achieved by building a data pipeline. It channels enough volume at the speed needed to refresh the models. This therefore crucial to acquire the right data annotation technology.

The below considerations are important when making this decision:

  • The tools are compatible to handle the appropriate data types in your scope.
  • The system platform allows pilot runs and experimenting with data.
  • The technology is capable of handling consistent quality across an individual annotator task and that of overall project quality.
  • A tool can manage efficiency metrics for tasks and batches in the project.

4. Data Labeling

Annotating data accurately and expeditiously governs the accuracy of the ML model. You should therefore select the tool that can handle the appropriate data types and open to update with future developments in the technology. The labeling system should allow designing a flexible workflow, control annotator’s quality, and throughput, and generate machine learning-assisted data labeling guided by human annotator’s rules. 

What is labeled data?

Data labeling involves data tagging, annotation, transcription, processing, etc. Data items are labeled by annotating data to show the target; that is what you expect the ML model should predict. In the process, the labeled data explicitly call out the features you tagged with the data. These patterns train the algorithm differently than the same pattern in unlabeled data.

5. Data Quality

Quality is a critical aspect of any data training project. Data quality considerably affects business outcomes.

What affects training data quality?

The type of your data sourcing resource, usually the people, expertise, and processes determines the level of quality of your data.

  • People: You data worker might be in-house, crowdsourced, or outsourced teams. Manage the selection, development, and work balance.
  • Process: You decide how people do the work; from sourcing to task synthesizing to quality assurance workflows.
  • Tools: Making the use of the technology to access the work, assignments, and enhance productivity and quality.

Ensure Data Quality

Though data annotation can be relatively simple, it is also a repetitive, monotonous, and time-consuming task. Training a model demands a human intervention to ensure the right data is used. For any inconsistency in data, the model would predict wrong results. For example, say while training a computer vision system for automobiles outdoors, if the images of sidewalks are mistaken as streets, then the results could be worse.

Accuracy is how close a label is to reality. Consistency is the degree to which annotations sustain on various training items, repeatedly.

6. Data Security

Securing data is an important concern in ML projects. The strategy recommends implementing Data Security Safeguards, as needed. Securing confidential data protects your business and customer information. 

Data projects using personally identifiable information (PII) or confidential data are sensitive. For models leveraging that type of information, data security is more concerned than others, especially when you are working with financial or government records or user-specific content. Companies follow norms on government regulations when dealing with customer information. Practicing transparent and ethical policies is one of the good terms of service. Following data security norms adds you a competitive advantage.  


You can rely most on a data scientist in dataset preparation, however, by knowing some techniques in advance by the team there is possible load balancing easing the load of the person who is going to handle this Herculean task.

"As data scientists, our time is best spent fitting models. So we appreciate it when the data is well structured, labeled with high quality, and ready to be analyzed,” said Lander Analytics Founder and Chief Data Scientist Jared P. Lander. His full-service consulting firm helps organizations leverage data science to solve real-world challenges.

It is a need of time for businesses to transit from data center environments to the Cloud. E2E Network’s flagship Cloud Transformational services provide you with a total solution to help plan your cloud strategy and make optimal use of Machine Learning.

Get more details here:

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This is a decorative image for: Actions CEOs can take to get the value in Cloud Computing
September 28, 2022

Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

In this blog, we are going to address a few of the actions that CEOs can take to get value in cloud Computing.

  1. A Coordinated Effort

As the saying goes, the more you avoid the risk, the closer it gets. So, if CEOs and their management teams have yet to take an active part or give the necessary attention that their migration journey to the cloud requires, now is the best time to start top-team support for the cloud enablement required to expedite digital strategy, digitalization of the organization, 

The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

Funding is a critical component of shifting to the cloud. You will be creating various changes in your sector, from changing the way you now do business to utilizing new infrastructure. As a result, you'll have to spend on infrastructure, tools, and technologies. As CEO, you must develop a business strategy that ensures that every investment provides a satisfactory return on investment for your company. Then, evaluate your investments in order to optimise business development and value.

  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.


In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

This is a decorative image for: Top 12 skills a CEO should demand in a data scientist to hire in 2022
September 21, 2022

Top 12 skills a CEO should demand in a data scientist to hire in 2022

Two decades ago, data scientists didn’t exist. Sure, some people cleaned, organized and analyzed information — but the data science professionals we admire today stand at the head of a relatively new (and vaunted) career path.

It is certainly one of the most popular careers because it is in great demand and highly paid. With data being the primary fuel of industry and organization, company executives must now determine how to drive their company in this rapidly changing environment. Not only is a growth blueprint essential, but so are individuals who can put the blueprint into action. When most senior executives or human resource professionals think of data-driven employment, a data scientist is the first position that comes to mind.

In this blog, we will discuss the top 12 skills a CEO should demand if hiring a data scientist in 2022. 

  1. Problem-Solving and Critical Thinking

Finding a needle in a haystack is the goal of data science. You'll need a candidate who has a sharp problem-solving mind to figure out what goes where and why, and how it all works together. Thinking critically implies making well-informed, suitable judgments based on evidence and facts. That means leaving your own ideas at the door and putting your faith - within reason - in the evidence. 

Being objective in the analysis is more difficult than it appears at first. One is not born with the ability to think critically. It's a talent that, like any other, can be learned and mastered with time. Always look for a candidate who is prepared to ask questions and change his/her opinion, even if it means starting over.

  1. Teamwork 

If you go through job listings on sites like Indeed or LinkedIn, you'll notice one phrase that appears repeatedly: must work well in a team. Contrary to popular belief, most scientific communities, including those in data science, do not rely on a single exceptional mind to drive forward development. A team's cohesiveness and collaboration power are typically more significant than any one member's brilliance or originality. Your potential candidate will not contribute to success if s/he does not play well with others or believes that s/he does not require assistance from your colleagues. If anything, candidates' poisonous attitudes may cause stress, decreased levels of accomplishment, and failure on the team.

Harvard researchers revealed in 2015 that even "moderate" amounts of toxic employee conduct might increase attrition, lower employee morale, and reduce team effectiveness. Eighty percent of employees polled said they wasted time worrying about coworker incivility. Seventy-eight per cent claimed toxicity had reduced their dedication to their work, and 66 per cent said their performance had suffered as a result. The fact is that being a team player is significantly more productive and fulfilling than being a solo act. Look for a candidate with good cooperation abilities, and both you and your team will profit!

  1. Communication 

Capable data scientists must be able to communicate the conclusions they get from data. If your candidate lacks the ability to convert technical jargon into plain English, no matter how significant the results are, your audience will not grasp them. Communication is one of the most important skills a data scientist can learn — and one that many pros struggle with. 

One 2017 poll that tried to uncover the most common impediments that data scientists encountered at work discovered that the majority of them were non-technical. Among the top seven barriers were "explaining data science to others," "lack of management/financial support," and "results not utilised by decision-makers."

You fail if you can't communicate - therefore look for a candidate who knows how to interpret! And can break down complicated topics into digestible explanations; rather than giving a dry report.

  1. Business Intelligence 

Sure, a candidate can’t start teaching abstruse mathematical theory whenever you want — but can they explain how that theory can be applied to advance business? True, data scientists must have a strong grasp of their field as well as a solid foundation of technical abilities. However, if a candidate is required to use those abilities to advance a corporate purpose, they must also have some level of business acumen. Taking a few business classes will not only help them bridge the gap between their data scientist peers and business-minded bosses, but it will also help them advance the company's growth and their career as well. It may also assist them in better applying their technical talents to create useful strategic insights for your firm.

  1. Statistics and mathematics 

When it comes to the role of arithmetic in machine learning, perspectives are mixed. There is no disputing that college-level comprehension is necessary. Linear algebra and calculus should not sound like other languages. However, if you're looking for a candidate for an internship or a junior position, then they don't need to be a math guru. But if you are looking for a candidate to work as a researcher, then the candidate must have more than just a strong math background. After all, research propels the business ahead, and you won't be able to accomplish anything until you have a candidate with a thorough grasp of how things function.

The fact is that just because data science libraries enable data scientists to perform complex arithmetic without breaking a sweat doesn't mean they shouldn't be aware of what's going on behind the surface. Get a candidate with the fundamentals right.

  1. AI and Machine Learning 

Machine learning is an essential ability for any data scientist. It is used to create prediction models ranging from simple linear regression to cutting-edge picture synthesis using generative adversarial networks. When it comes to machine learning, there is a lot to look for in a potential candidate. Regression, decision trees, SVM, Naive Bayes, clustering, and other classic machine learning techniques (supervised and unsupervised) are available. Then there are neural networks, which include feed-forward, convolutional, recurrent, LSTM, GRU, and GAN. There's also reinforcement learning, but you get the idea - machine learning is a vast subject. 

  1. Skills in cloud and MLOps

To remain relevant to the industry's current demands, more than three out of five (61.7%) companies say they need data scientists with updated knowledge in cloud technologies, followed by MLOps (56.1%) and transformers (55%). Three out of every four professionals with ten or more years of experience are learning MLOps to expand their skill sets. Cloud technologies (71.7%) are being learned as a fundamental new talent by mid-career professionals with 3-6 years of experience, followed by MLOps (62.3%), transformers (60.4%), and others.

Professionals in retail, CPG, and e-commerce are more likely (73.7%) to learn cloud technology as a new skill. As much as 70% of BFSI personnel upskill in MLOps. Another 70% and 60% of pharma and health workers are interested in acquiring transformers and computer vision as fundamental skills.

So make sure you don't miss out on such a talent who can bring cloud and MLOps skills into your company. 

  1. Storytelling and Data Visualization 

Data visualisation is enjoyable. Of course, it depends on who you ask, but many people consider it the most gratifying aspect of data science and machine learning. Look for a candidate who is a visualisation specialist and understands how to show data based on business requirements, and also how to integrate visualisations so that they tell a story. It might be as easy as integrating a few plots in a PDF report or as sophisticated as creating an interactive dashboard suited to the client's requirements.

The data visualisation tools utilised are determined by the language. Plotly, which works with R, Python, and JavaScript, may be the best option if you need a candidate for searching for a cross-platform interactive solution. Consider Tableau and PowerBI when you need a candidate for viewing data using a BI tool. 

Figure: Use of Data Visualization tools. 

  1. Programming 

Without programming, there is no data science. How else would you give the computer instructions? All data scientists must be familiar with writing code, most likely in Python, R, or SQL these days. The breadth of what a candidate will perform with programming languages differs from that of traditional programming professions in that they’ll lean toward specific libraries for data analysis, visualisation, and machine learning. 

Still, thinking like a coder entails more than just understanding how to solve issues. If there is one thing that data science sees a lot of, it is issues that need to be solved. But nothing is worse than understanding how to fix an issue but failing to transform it into long-lasting, production-ready code.

Out of the host of programming languages, 90% CEOs hire data science specialists who are specialists in Python as their preference for statistical modelling. Beyond that, the use of SQL (68.4%) is highest in retail, CPG, and ecommerce, followed by IT at 62.9%. R is the most widely used programming language if you operate in the pharma and healthcare business, with three in five (60%) data scientists reporting using it for statistical modelling.

  1. Mining Social Media 

The process of extracting data from social media sites such as Facebook, Twitter, and Instagram is referred to as social media mining. Skilled data scientists may utilise this data to uncover relevant trends and extract insights that a company can then use to gain a better knowledge of its target audience's preferences and social media actions. You need data scientists well versed with this type of study as it is essential for building a high-level social media marketing plan for businesses. Given the importance of social media in day-to-day business and its long-term viability, hiring data scientists with social media data mining abilities is an excellent strategy for company growth.

  1. Data manipulation 

After collecting data from various sources, a data scientist will almost surely come across some shoddy data that has to be cleaned up. You need to hire a candidate that knows what Data wrangling is. How to use it for the rectification of data faults such as missing information, string formatting, and date formatting. 

  1. Deployment of a Model 

What is the use of a ship if it cannot float? Non-technical users should not be expected to connect to specialised virtual machines or Jupyter notebooks only to check how your model operates. As a result, the ability to deploy a model is frequently required for data scientist employment.

The easiest solution is to establish an API around your model and deploy it as any other application — hosted on a virtual machine operating in the cloud. Things get harder if you wish to deploy models to mobile, as mobile devices are inferior when it comes to hardware. 

If speed is critical, sending an API call and depending on an Internet connection isn't the best option. Consider distributing the model directly to the mobile app. Machine learning developers may not know how to design mobile apps, but they may examine lighter network topologies that will have reduced inference time on lower-end hardware.

Consider hiring a candidate who is well versed with all the things discussed above related to deploying a model. 


And there you have it: the top twelve talents skills a CEO must look for while hiring a data scientist. Keep in mind that skill levels or talents themselves may differ from one firm to the next. Some data science jobs are more focused on databases and programming, while others are more focused on arithmetic. Nonetheless, we believe that these 12 data science skills are essential for your potential candidate in 2022.

This is a decorative image for: Towards Complete Icon Labelling in Mobile Applications
September 21, 2022

Towards Complete Icon Labeling in Mobile Applications

Why is Icon Labeling Important?

Icon labeling projects aim to create a machine learning algorithm that can automatically label icons in mobile applications. The algorithm is generally trained on a dataset of labeled images and learns to recognize the objects in the pictures. Labeling icons is a tedious task and often requires human intervention.

Thus, automating this process by training an algorithm on a labeled image dataset can pave the way for complete icon labeling. This article will walk you through labeling icons using machine learning. Icons may seem like a small part of your app, but they're critical for branding and user experience. Icons need to be labeled by hand, which is time-consuming and tedious.

It isn't easy to keep up with the volume of new icons on mobile phones, and keeping the icons organized takes a lot of effort. The wrong icon can ruin your app's design and make it difficult for users to use. With any icon labeling project, labeling icons is easy. Your database will be automatically and consistently labeled by Artificial Intelligence that recognizes objects in images.

How to Label Icons Effectively?

Prepare your data set. It should include the icon's name, a short description, and an image of the icon. You can use any file type uploaded to any storage or drive.

Next, you will need to create a project in the platform and enable billing if it has not been done already. Then you can create a new dataset by specifying a dataset ID and name.

The use of labeling icons in UI design has been around for many years. The most popular use case is to offer users an indication of what they can do on a particular screen. You can do so by adding labels to the icons.

Icons often indicate the user's action to complete a task (e.g., save, delete, etc.). However, this could be problematic for people with disabilities or who cannot understand or read English fluently due to language and communication barriers.

Labeling icons is complex, especially when the icon is not well-known. We propose a novel method for labeling icons with conversational agents and chatbots. Machine Learning techniques can help generate a set of labeled examples for a conversational agent or chatbot training.

Tips for using icons in your app

Labels are the most critical component of an icon, as they communicate the meaning to users. Designers should keep their icons simple and schematic and include a visible text label to make them good touch targets.

Icon designers also need to be careful when designing icons. Designers should keep their icons simple and schematic, include a visible text label and make them good touch targets. Labels are the most crucial component of an icon as they communicate meaning to users.

Icons should be simple and schematic with a clear visible text label that communicates what the icon means to users. Icons are also suitable for touching targets for screen readers, so designers must consider this when designing them.

Icon labels are an essential feature that can make or break an icon. Designers are often designing icons with less-than-perfect or downright nasty labels. Terrible labels can lead to misinterpretation and confusion, leading to lost business or a tarnished reputation. Labels are not just crucial for designers; they're critical to users.

The label conveys the meaning of a symbol, so it should be simple, visible, and easy for interaction purposes. If designers ignore these principles, icons will become meaningless, unhelpful, and challenging to navigate. Designers must create good touch targets that are easily recognizable. After all, it's about bringing users the best.


Iconography is the basis of every UI design. Designers need to understand how it shapes an interface’s usability. Every icon in an interface serves a purpose. When implemented carefully and in the correct manner, icons can help users navigate through the workflow. It's good to be a part of this cutting-edge iconography which can help you further push the boundaries of Deep Learning and expand your understanding of recognizing icon types.

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