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Machine Learning

What is Training and Testing Data in Machine Learning with Types?

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Machine learning (ML) is a one of the fastest growing technology interchangeably used with artificial intelligence (ML) on which many companies across the world are working with more innovative models and applications developed with encouraging results.

To develop such models on machine learning principles a training data is used that can help machines to read or recognize a certain kind of data available in various formats like texts, numbers and images or videos to predict as per the learned patterns.

Difference Between Training and Testing Data in ML

Training Data is kind of labeled data set or you can say annotated images used to train the artificial intelligence models or machine learning algorithms to make it learn from such data sets and increase the accuracy while predating the results.

Also Read: How Much Training Data is Required for Machine Learning Algorithms?

While on the other hand, after using the training data sets each machine learning model needs to be tested to check the accuracy and validate the model prediction. Testing data is quite different from training data, as it is a kind of sample of data used for an unbiased evaluation of a final model fit on the training dataset to check model functioning.

Why Training Data is Important?

Training data is important because without such data a machine cannot learn anything and if you want to train model you have to feed the curated data sets allowing machines learn from the repetitive or differentiated patterns and predict accordingly.

Also Read: Reasons Why AI and ML Projects Fail Due to Training Data Issues

As much as quality training data is feed into the AI model or ML algorithms with the right algorithm you will get the more accurate results. The accuracy of model prediction mainly depends on the quality and quantity of training data sets used to train such models.

What are the Different Types of Training Data?

Apart from annotated text and video, there are different types of image training data sets available in the market depending on the field of industry of model development. And image annotation technique as training data is used for self-driving or autonomous vehicles, drones, satellite imagery, AI in agriculture, security surveillance and sports analytics.

Also Read: What are the various Types of Data Sets used in Machine Learning?

Image Annotations Types for Training Data in Machine Learning:

These annotation types are used for computer vision to recognize the objects of interest in the images and store the information into their system for future prediction. And the main purpose of image annotations is to train the machines and develop a fully-functional AI model that can detect the various types of objects and take the action accordingly. And acquiring the right quality of annotated images as training data become an important factor for machine learning engineers or companies working on AI.

How to Get Training Data for Machine Learning?

Collecting the right quality and amount of data sets from a reliable source is a challenging task in the AI world. As most of the data sets used to train machine learning models are in the form of annotated images that a computer vision can easily recognize and learn for predictions.

To get the right quality and quantity of training data sets you need to get in touch with a professional company like Cogito that provides the machine learning training data with image annotations and data labeling service. You can get all types of annotated images as per your AI model or machine learning algorithm training needs and affordability.

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Artificial Intelligence

The Main Objective of Image Annotation in Machine Learning & AI

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Objective of image annotation

Artificial Intelligence (AI) and Machine Learning (ML) are getting more interest by computer engineers who bring to light this progressive technology implement into untapped fields or improve the performance and efficiency of existing fields.

And availability of machine learning training data is the crucial point to improve the AI performance. While image annotation is the techniques used to create training data for visual perception model developed on the principles of AI and ML. And the main purpose of image annotations is to develop AI and ML model.

So, you need to first understand the importance of image annotation in AI and ML, so that you can further explore untouched fields where AI is needed. Actually, to make machines perceive objects in their natural surroundings you need annotated images that you can use to train the ML algorithm learn and predict.

Detection of Objects of Interest

In machine learning or AI, you need to train the machine detect the various types of objects visible in the natural environment. Self-driving cars, robots and autonomous flying machines cannot detect such objects, unless trained with a certain process. And annotated images makes object of interest detectable to machines.

image annotation self-driving cars

Bounding box image annotation is the precise technique, makes different types of objects recognizable to machines through computer vision. It can be used to develop AI-enabled models for automotive, retail and various other fields.

Classification of Objects in Image

Object detection is the not only the objective of image annotation, instead in while classifying the objects it works in the same manner. Actually, there could be different types of objects in an image and it becomes impossible for a machine to classify them.

object classification in image annotation

For an example, there is dog and man in the same image, so both have to be classified as different objects and recognize the similar objects when shown to AI models in the real-life use. So, image annotation is the method classify such objects. However, the image annotation is used for computer vision to train the AI models.

Also Read: What Is Computer Vision In Machine Learning And AI: How It Works?

Recognize Objects and Localization

Similarly, when there are different types of objects in a single image, it becomes difficult to recognize the same, that because of similar dimension. In such cases object recognition, classification and localization are required at ground level.

object localisation in image annotation

And semantic segmentation is the most suitable technique helps to classify the objects in a single class making easier for machines to differentiate between various types of objects. For computer vision this technique objects can be configured with nested classifications, and localization for precise recognization.

Also Read: How To Label Data For Semantic Segmentation Deep Learning Models?

Supervised Machine Learning Training

Another considerable important of image annotation is it helps to create a label data sets for machine learning and AI. And for supervised machine learning, annotated images is must helps algorithms easily detect and classify the objects.

Actually, in supervised machine learning, two types of algorithms is used. First for classification which helps to classify the into desired categories. And second regression that helps to predict a value based on past data.

Validation of Machine Learning Models

Another most important objective of image annotation is while developing and AI or ML model it helps to validate the models to test for the accurate prediction. Annotated images are used to check whether model is able to detect, recognize and classify the objects precisely and predict the same with accuracy.

Also Read: How to Validate Machine Learning Models: ML Model Validation Methods

In this process, machine learning model is validated by the experienced annotators and engineers. And if annotated images will be not available, how model will detect the objects, and this process also helps to evaluate the quality of image annotation services.

As, if images are not annotated properly, algorithm will not able to relate the same from its database or past experienced gained from machine learning.

Hence, image annotation has significant role in machine learning and AI development. But quality of machine learning training data is the another aspect, should be considered to ensure your model is getting right training. As, incorrect annotated images will misguide the machine due to inaccurate feed of data into algorithm.

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Machine Learning

What Is The Difference Between Machine Learning Engineer And Data Scientist?

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data scientist vs machine learning engineer

Data scientists and machine learning engineers are two important professionals in AI filed playing a vital role in building a model. And their role in AI development is not that much different but from a technical skills perspective, there is a difference.

The core difference between data scientist and machine learning engineer is – former one, more knowledgeable in programming skills used around data. While data scientist is like a mathematician who can program using his data analysis skills.

However, their roles are complementary to each other and supportive you must know the difference between a data scientist and machine learning engineer. Below we have covered various aspects, that make them different from each other.

ML ENGINEER VS DATA SCIENTIST – DIFFERENCE

Actually, there are multiple parameters you can differentiate between two professionals. And if you are looking to hire machine learning engineer and shortlisting the data scientist find the actual difference to appoint the right candidate.

Also Read: How To Hire A Good Data Scientist: Five Easy Steps

Educational Degree Required for Data Scientist and ML Engineer

At the academic end, ML engineers both professional are graduated with highly qualified degrees and require decisive skills with extensive knowledge to perform their tasks in a highly professional manner with perfection.

A ML engineer will typically more studious in computer science, while a data scientist is more involved in statistics or mathematics subjects. But let make you clear one thing, a ML engineer is a programmer also specialized in data, while a data scientist plays with the huge amount of data but he is also also a programmer.

At the educational end, once you complete your undergraduate degree, you have to choose the right path and learn more knowledge and skills in that field.

Here, if you want to become a ML engineer you have options like either continue working as an entry-level programmer or explore the opportunities into AI field and become a specialist in bid data or machine learning programmer to develop an AI model.

Also Read: What is the Difference between Artificial Intelligence and Machine Learning?

Whereas, if you are ambitious to become a data scientist, you need to gain more education as a master or doctorate degree to make your academic skills more strong and gain the capability to analyze and utilize the data for deep learning.

Skills Required for Data Scientist and ML Engineer

Both engineers required extraordinary skills to work proficiently in their respective fields. Although, few of the skills are very common necessary for both of them to analyze the huge data and utilize its crucial information. Here, we brought the key differences between the skills of these professionals listed respectively.

Skills Required for Machine Learning Engineer:

  • Strong ML Programming Skills
  • Computer Science Fundamentals
  • Probability and Statistics Modeling
  • Proficient in Python/C++/R/Java
  • Understanding of ML Algorithms
  • Natural Language Processing
  • Data Modeling and Evaluation Skills

Skills Needed for Data Scientist:

  • Data-Driven Problem Solving Skills
  • Strong Statistical and Fundamentals
  • Big Data Analysis and Interpretation
  • Data Visualization & Communication
  • Machine Learning and Deep Learning
  • Programming languages (R and Python)
  • Unstructured Data Management Techniques
  • Use big data tools like Hadoop, Hive and Pig

ML Engineer vs Data Scientist – Roles and Responsibilities

Both, a data scientist and machine learning engineer mainly hired to developed AI-enabled applications or autonomous models but they have different roles and duties while working on such projects which are clearly outlined below.

Data Scientist Roles and Responsibilities:

  • Data source identification and automated collection
  • Data Mining Using State-Of-The-Art Methods
  • Enhance Data Collection Procedure and Techniques
  • Analyze Huge Big Data To Discover Trends And Patterns
  • Identify Trends, Patterns and Correlations in Complex Data Sets
  • Create Analytical Methods and Machine Learning Models
  • Assess the Effectiveness of Old or New Data Sources
  • Evaluate the accuracy of data gathering techniques
  • Apply and Implement the popular Deep Learning frameworks
  • Responsible to Undertake Processing of Unstructured Data
  • Use machine-learning algorithms to Build the Predict Models
  • Data Visualization, Presentation and Storytelling Techniques
  • Collaborate with ML Engineer and with other Stakeholders

Roles and Responsibilities of Machine Learning Engineer:

  • Understandand Transform the Prototypes of Data Science
  • Research,Design and Frame Machine Learning Systems
  • Chooseand Implement the Right Machine Learning Algorithm.
  • Selectand Implement Right Machine Learning Algorithms.
  • Selectthe Right Training Data Sets for ML Model Development
  • UnderstandBusiness Objectives and Developing the Ml Models
  • PerformMachine Learning Model Tests and Experiments
  • PerformStatistical analysis and Fine-Tune the Testing Results
  • Verifyingdata quality, and/or ensuring it via data cleaning
  • Developthe Machine Learning Model as per the Needs.
  • Performthe Training models and tuning their hyperparameters.

The roles and responsibilities of data scientists and machine learning engineers are more or less different but there are many duties they both perform during their tasks. As they also need to work collaboratively to build a right AI model that can work with the best level of accuracy when implemented in real life-use.

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Artificial Intelligence

Reasons Why AI and ML Projects Fail Due to Training Data Issues

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Why AI & ML Projects Fail

Artificial Intelligence (AI) market is posing to become billions of dollar industry in next few years, as global spending by nations on AI is likely to touch around $35.8 billion in 2029 which reports a growth of 44% over the amount spent in year 2018.

Such, impressive growth shows, AI holds huge potential to attract big organizations as well small enterprises attracting them to implement AI-enabled services for better growth in the business. However, working with AI you need immense amount of meticulous data to train the model so that it can give the precise results.

Actually, to train an AI or ML model a high-quality training data is required, which is a challenging task for AI developers or machine learning engineers. As, to get the human like complex decisions from machines you need enormous volumes of accurately labeled and annotated training data through images or videos.

With the growing AI demand, data science team are under pressure to complete the projects but acquiring the training data at a large scale is the real challenge they are facing right now.

Why Do Enterprises Face Data Issue for AI Strategy?

As per the research by Dimensional Research and Aiegion survey, enterprise machine learning is just beginning, machine learning engineers or data scientist team size is smaller and the expertise of growing data science is not yet compatible to matured ML projects expertize.

And acquiring the training data is the biggest challenge for the success of an AI project. As per the survey, 96% of the AI projects fail or not started due to lack of training data technology that leads to the inability to train the ML algorithms resulting failure of the project.

Half of the AI Projects Never Get Deployed

Nowadays, big organizations or enterprises having more than 100,000 employees are more keen to implement AI strategy into their business model – but only 50% of such enterprises currently have one. The survey reinforces that AI is at nascent in the enterprise, as 70% of them firstly invested in AI/ML projects in the last 24 months.

While on the other hand, over half of the enterprises report they have undertaken fewer than four AI and ML projects. And only half of the enterprises have released AI/ML projects into the development to build a fully-functional model.

Also Read: What is Training and Testing Data in Machine Learning with Types?

And as per the survey research only, less than two-thirds of them indicated that their ML project reached the completion point that is being trained on labeled training data sets which are relatively at the initial stage in the ML project life cycle. And more revealing immaturity of ML in the enterprise, is that why half of the projects never deployed.

Survey Statistic Why AI/ML Projects Fail:

  • 78% of AI/ML Projects Shut ate some stage Before Deployment
  • 81% Admit the process of training AI with data is more difficult than they expected
  • 76% struggle by attempting to label or annotate the training data on their own.
  • 63% try to build their own labeling and annotation automation technology.

And as per the research, around 40% of failed projects reportedly stalled during training data-intensive phases like training data preparation, algorithms training model validation, scoring and post-deployment enhancement.

Top Reasons for AI Projects Failure:

  • Lack of Expertise (55%)
  • Unexpected Complications (55%)
  • Training Data Problems (36%)
  • Lack of Model Certainty (29%)
  • Deficient Budget (26%), and
  • Lack of Efficient Staff (23%)

As already bespeak, around two-thirds report that ML projects not able to progressed beyond proof of concept and algorithms development to the phase of training data. Mostly this phase is not favorable for such developments, as 80% report that training the algorithms is more challenging than the AI engineers have expected.

Reasons Why Training Algorithms Data is Challenging:

  • Notenough data
  • Datanot in a usable form
  • Biasor errors in the data
  • Don’thave the tools to label the data
  • Don’thave the people to label data

Nevertheless, less than 4% have reported that training data has presented without any problems. Almost three-quarters of the AI engineers indicated that they try to label and annotate training data on their own. While around 40% suggested they rely wholly or partially on off-the-shelf, pre-labeled data to train their AI model.

Such issues, lead to 70% companies utilizing external services for their AI or ML projects with most of them focusing on data collection, labeling and annotations. As AI and ML engineers are rare to find and also expensive, the enterprise should find out external solution service providers for critical activities like data labeling and model scoring. This evidence is enough to outsource data annotation for more improved outcomes.

Summing-up

Enterprises designate a strategic value to their machine learning initiatives and expect AI and ML shall improve their businesses aspects and would be also disruptive in their sectors.

However, AI and ML projects are still at an early stage of development at enterprises. And data science and AI engineer teams are relatively small and experienced which affects the efficiency and outcome of these projects.

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