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.
Reasons Why AI and ML Projects Fail Due to Training Data Issues
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.
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.
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.
What is Training and Testing Data in Machine Learning with Types?
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 Difference between Artificial Intelligence and Machine Learning?
Artificial intelligence (AI) and machine learning (ML) are nowadays one of the hottest topics in the tech towns across the globe. China is heavily investing on R&D on AI and other developed nations like US, UK and Germany are also in the race to develop AI-base robots, machines, software, cars and business applications that can work without any human intervention.
Sometimes AI and ML are used interchangeably, making a kind of confusion among people who listen about the technological developments into this field. So, before we clarify the difference between AI and Machine Learning we need to simplify with the basic definition of both to make it more easily understandable while unfolding the dissimilarities between them.
What is Artificial Intelligence?
In a very simple and layman language, “Artificial intelligence is a theory and development of computer-based systems that can works behaving like human intelligence”. It is kind of study how to train computers so they can perform with their own thinking in different situations.
AI also sometimes called machine intelligence because it is demonstrated by machines. And nowadays automobiles, gadgets and equipment in healthcare or sector are using AI-enabled devices to reduce the repetitive tasks and minimize the human efforts.
What is Machine Learning?
Machine learning is a branch or you can say a subset of artificial intelligence in the field of computer science allowing machines to learn by its own without being explicitly programmed. Actually, with the help of an algorithm, it uses a machine learning training data to learn certain patterns and behaviours of the particular action to respond accordingly.
The prime aim of ML is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly. And the process of teaching initiates with observations of data such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we feed into them.
Difference between Artificial Intelligence and Machine Learning with examples
Artificial Intelligence is a wider concept that involves the research and development of machine learning based applications. ML is the sub-field of the AI and its main aim is to increase the accuracy by learning with high-quality training data while AI works as a computer program aimed to increase the chance of success and not the accuracy.
AI is you can say a final model that performs in decision making while ML allows the system to learn new things from the data. Machine learning process basically encompasses creating self-learning algorithms, whereas AI leads to develop a system to reposed like human intelligence and behave accordingly into different circumstances.
Self-driving cars, virtual apps like Google Assistant and Crotona, Alexa and Siri are nice examples of artificial intelligence. All these models have been trained with a huge amount of high-quality data with deep learning algorithms to work flawlessly and make human work easier. Cogito is the one the well-known AI training data companies, providing high-quality datasets for machine learning in various industries like healthcare, e-commerce and retail etc.
AI-enabled robotics and machines are various other best examples you can see around you or on the internet. But remember, artificial intelligence is a broad term that represents the general concept of machines being able to carry out smart tasks, and machine learning is a specific subset of algorithms for AI that helps to learn from data and perform accordingly.
To know more about AI and ML differences watch this video:
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