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.
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.
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.
Image Annotations Types for Training Data in Machine Learning:
- Text Annotation
- Video Annotation
- 2D Bounding Boxes
- Semantic Segmentation
- 3D Boxes or 3D Cuboids
- Polygonal Segmentation
- 3D Point Cloud Annotation
- Line or Polylines Annotation
- Landmark and Point Annotation
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.
What Is The Use And Purpose Of Video Annotation In Deep Learning?
Just like image annotation, video annotation also helps machines to recognize the objects through computer vision. Basically, the main motive of video annotation is detecting the moving objects in the videos and makes it recognizable with frame-to-frame outlining of objects to train the AI models developed with deep learning.
Use of Video Annotation
Apart from, detecting and recognizing the objects, which are also possible through image annotation, there are various reasons video annotation is used in creating the training data set for visual perception based AI models observe varied objects.
Actually, these models get trained through an algorithm to perceive the various types of objects through video annotation service. So, right here, apart from object detection, we will explain what is the use and purpose of video annotation in deep learning.
Frame-by-Frame Objects Detection
The first and most use and purpose of video annotation is capturing the object of interest frame-by-frame and making it recognizable to machines. The moving objects run on the screen annotated using the special tool for precise detection through machine learning algorithms used to train the visual perception based AI models.
Object Localization for Computer Vision
Another use of video annotation is localizing the objects in the video. Actually, there are multiple objects visible in a video and localization helps to locate the main object in an image, means the object mostly visible and focused in the frame. Actually, the main task of object localization is to predict the object in an image with its boundaries.
Object Tracking for Autonomous Vehicle
Another important use of video annotation is help visual perception AI model build for autonomous vehicle is after detecting and recognizing the objects track the varied category of objects like pedestrians, street lights, sign boards, traffic lanes, signals, cyclists and vehicles moving on the road while self-driving cars is running on the street.
Tracking the Human Activity and Poses
Another significant purpose of video annotation is again to train the computer vision based AI or machine learning model track the human activities and estimate the poses. This is mainly done in sports fields to track the actions athletes perform during the competitions and sports events helping machines to estimates the human poses.
These are various use of video annotation, and all these are done for the computer vision to train the visual perception based model through machine learning algorithms. In self-driving cars and autonomous flying drones, video annotation is mainly used to train the model for precise detection, recognition and localization of varied objects.
There are many video annotation companies providing the data labeling service for AI and machine learning. If you need a video annotation for deep learning, you can get in touch with Anolytics, that offers a world-class video annotation service to annotate the object of interest with frame-by-frame annotation at best level of accuracy.
The Main Objective of Image Annotation in Machine Learning & AI
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.
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.
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.
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.
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.
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.
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.
What Is The Difference Between Machine Learning Engineer And Data Scientist?
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.
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.
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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