Fat is Fit in AI/ML Technology

The Fatter It Goes With The AI Training Data, the Fitter It Gets with the Machine Learning Model?

Artificial intelligence (AI), which is outlined to be the simulation of human intelligence, is a future technology where machines are programmed with algorithm-coded instructions to make intelligent decisions and predictions. In contrast to natural intelligence, artificial intelligence refers to data-driven machine learning models that preserve the data to learn, plan, predict, reason, and solve problems. Honestly speaking, as we see the innovations happening around the automation industry, AI and machine learning (ML) will take over all the substantial commands and controls of human life systems in the near future.?

As the era of advanced analytics embraces machine learning, AI, and cognitive computing, conversations are centered on how businesses can use advanced analytics to gain a competitive edge. Even though businesses can adopt various strategies, they all come down to one thing: the data. With appropriate machine learning models, businesses can continuously predict changes in the marketplace so they can best predict what will happen next. Machine learning models constantly update their solutions as data is continuously added. The benefit is straightforward — if you use the most appropriate and current data sources within the framework of machine learning, you can predict the future.

Data is the Key Behind All Functional Machine Learning Models?

Machine learning uses an array of algorithms that continually analyze, describe, and predict data as it learns. Ingestion of training data allows the algorithms to create a more accurate computer vision for machine learning models. Thus, the AI-based machine learning model must be trained on the right data type. Having said it all in the discussion above, this can be concluded that the data is the key to a successful machine learning model — the fatter it goes with the AI training data, the fitter it gets with the machine learning model.?

Machine learning applications are likely to be part of your everyday life without you even realizing it. Assume yourself as a shopper making purchases online in an e-commerce store. For instance, while viewing products on an e-commerce site, you are likely directed to similar products you may find worthy of buying. These recommendations for the same line of products aren't hardcoded into the system by developers. By ingesting your browsing history and the browsing and purchase history of other shoppers, a machine-learning model serves suggestions to the site.?

While you may be amazed by the exceptional product recommendations served by the e-commerce store, you don't tend to take the clue of what is working on the backend to help you with the similar suggestions of your interests. This is the data, in fact, the AI training data (algorithm-bound instructions) that a machine learns from to make predictions based in line with the purchase history.?

Understanding Characteristics of Big Data

All businesses thriving for sustainable growth look forward to developing deep learning for vision systems and deploying a machine learning model that can adapt to changing market conditions. For this purpose, data characteristics need to be understood — understanding data leads to the right data, e.g., text or image analysis in line with the requirements for the machine learning model. It must be done in a way that enables business outcomes to be influenced with a positive mark. In other words, inferences must be made in the least possible time to satisfy customers with what they transact with a business.?

As a general rule, big data comprises large data sets that cannot be gathered, curated, managed, and processed by standard software in a reasonable amount of time. A "big data" dataset can range in size from one or more terabytes to several petabytes; Big data refers to a set of tools and technologies used to manage and analyze diverse, complex, and extremely large datasets.??

Data defined as Big Data is the type of information with such a high volume, velocity, and variety that it requires specific technologies and analytical methods for it to be utilized in a valuable way. Moreover, the characteristics of the data also define the technical expertise and tools required for computer vision, data annotation, and labeling so as to prepare the accurate AI training data for the machine learning models. Some of the characteristics of big data include:

Volume?

This context emphasizes the importance of the volume of data generated. A data set's size determines its value and potential and whether it, in the true sense, can be called Big Data. Even the term 'Big Data' itself is associated with a size and is therefore considered one of its fundamental characteristics.

Variety?

Variety is the second most significant characteristic of Big Data. While keeping the importance of this crucial category in mind, it can be inferred that data analysts are required to know the actual being of data and the resource it belongs to. Those who, in any way, belong to data processing for AI-based machine learning model deeply analyze the data to use it effectively to their advantage.

Velocity?

In this context, the term 'velocity' can be understood as a key characteristic of big data, i.e., a measure of the speed at which data generation occurs. It can simply be described as the speed at which it is processed to meet the challenges ahead on the path toward the successful accomplishment of the AI-based machine learning model.

Variability?

It's a factor that can cause problems for those analyzing the data. As a result, the process of handling and managing the data is hindered by the inconsistencies manifested by the data from time to time.?

Veracity?

A great deal of variation can be found in the quality of the data captured. The data having variations, in any way, from shape and size to relevance, leads to nothing but inaccurate training data and an erroneous machine learning model. Therefore, veracity is a vital characteristic as it determines the validity of AI training data and the workability of the machine learning model that uses such data.?

Complexity

A data management process is very complex, particularly if a large volume of data comes from multiple sources. The data needs to be connected, correlated, and linked together to provide the information meant to be conveyed.?

Data Sourcing

The above discussion about the key characteristics of Big Bata is worth concluding the significance of data for a successful machine learning model. Often, people focus on machine learning algorithms when discussing them. However, success is determined by good data — the more, the better. A data annotation and labeling company need to have a substantial amount of data that is to be cleaned, filtered, annotated and labeled with the appropriate metatext.?

Currently, machine learning has profound implications for a wide array of applications, including text understanding, speech recognition, health care, genomics, and image recognition. This success is strongly linked to a better computation infrastructure and sufficient training data. Even computer vision companies these days are focusing more on sourcing the right and relevant data for the development of AI training data for machine learning models. For most machine learning problems, preparing the data is the most time-consuming task. This includes preparing, cleaning, analyzing, visualizing, and engineering features. A pressing need, therefore, is to develop efficient and accurate data collection methods that can create quality training data.?

Understanding the significance of the data is critical to the success of developing AI training data and its deployment into the machine learning model. A machine learning from inaccurate training data will lead to nothing but a faulty model that will go wrong with every transaction it makes with automation or prediction. Additionally, you should count on the right data about what data should be included in your machine learning application. Your predictions will be inaccurate if your machine learning application is built using faulty data.?

Identifying the Right & Relevant Data

The business world is driven by constantly changing data from multiple sources. Data structures play an important role in the course of text & image annotation and data analysis for computer vision programs. It is also necessary to identify the data that is used to evaluate the impact on business outcomes. Data from email,? text streams, social media, images, point of sale, and machine sensors are all included in big data, which is further categorized into structured, unstructured, or semi-structured.?

Structured Data Sources

Structured data refers to the data that has a defined format and is typically stored in traditional relational databases. Most organizations that source the data from unstructured data sources use the on-premises data centers to store such data. Structured data can be categorized as sensor data, weblog data, point of sale data, weather data, financial data, and clickstream data. Such data is captured during the natural course of the process and then sourced later as and when required for preparing training data for the machine learning model.?

Unstructured Data Sources?

Unstructured data has no defined format, even though it has some implicit structure. The use of unstructured data by businesses remains vastly underutilized, and it offers the potential for great monetization. Unstructured data has risen dramatically due to cloud computing, mobile devices, and social media. Unstructured data can be in the form of text within your business, social media data, mobile data, satellite images, photographic and video data, radar data, or sonar data.?

Generating Data Manually?

There might be cases when a company working on preparing the training data for the machine learning model fails to find the existing datasets to deploy in the AI training module. This is when the manual data generating approach comes in place. There are different tools and techniques that data annotation and?

Crowdsourcing is a common method of manual construction in which human workers are assigned tasks to collect the data that eventually becomes a dataset. A synthetic dataset may also be created using automated techniques. If you already have some data and need to fill in some missing elements, then data generation can also be considered data augmentation.?

The Machine Learning Cycle

A machine learning application or an algorithm must be improved over time. A model cannot simply be trained once and left alone. Data changes, preferences change, and competitors emerge.?

Keeping your machine learning model fresh is therefore crucial as you plan to take the data ahead to produce AI training data for your next machine learning model. Although you won’t need to train the model as you did during the development process, you can not assume the machine learning model is self-sufficient when it comes to data.?

Once your machine learning model starts making predictions or you start making use of your machine learning model to automate a certain set of business or manufacturing processes, this is advised to test and evaluate the data whether the data is accurate and sufficient for the model. Also, check if the data is relevant to your machine model and do the needful to automate the process or make the right predictions. Take an overview of the training data and the performance of your machine learning model in conjunction so as to figure out if it requires more training data to make more accurate predictions.

Final Thought?

Data is the most valuable resource whether it’s Analytics, Machine Learning, or Artificial Intelligence. The success of an AI development project and how well it gets in sync with computer vision & machine learning programs call for access and availability to quality data. How the AI training data performs for developing computer vision programs depends on the quality and depth of your data. While your organization may not be at the stage where it is ready to begin building Al applications, at the very least it should be planning for sourcing the right training data for machine learning.