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.
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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: https://www.e2enetworks.com/gpu) 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.
Conclusion
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.
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