Almost 60% of enterprises today are using AI-enabled software for insights that help to streamlines operations, sales, and workflows. Additionally, machine learning (ML) has been adopted by 63% of enterprises for the same purpose. This should make the news because ML is the mainstay of modern artificial intelligence.
Today, an increasing number of companies are excelling in their use of machine learning (ML). This is largely due to the affordable computing resources and cloud technologies that have popped up in recent times. With these solutions, it has become easier for companies to leverage AI and ML to deal with Big Data and gain useful insights. In AI software development, it is the pretrained AI/ML datasets that are making the development process more efficient.
In software development, AI or ML models need to be trained on big datasets to make them perform the required task. This would have been a problem without pretrained AI/ML datasets. Luckily, we have a number of such datasets available, and this makes it possible to collect, process, and analyze data in real-time to speed up the AI software development cycle.
While no one can doubt the importance of AI/ML datasets in AI software development, the success of these datasets in software development is far from guaranteed. To be successful in software development, the datasets must contain a decent amount of data and should be of good quality. Additionally, they should be free from any bias. If the datasets do not have enough data, lack quality, or are biased, then the results will be poor.
To find quality AI/ML datasets, you need to look for the following characteristics:
Keeping the above in mind, you can start your search for a suitable AI/ML dataset that can aid you in AI software development.
Pretrained datasets are one of the best sources for learning an AI/ML algorithm or trying out an existing framework. They allow you to overcome many of the restrictions or computational restraints faced in building a model from scratch. These pretrained datasets can be used as a benchmark for improving existing models or testing these models against them. The possibilities are endless with pretrained AI/ML datasets and the search for them is made easy by dataset finders.
Dataset finders are like a large repository of AI/ML datasets where you search a variety of datasets housed in different places on the web. With dataset finders, it becomes easy to find an AI/ML dataset that you can use to test existing models and even improve them. Following are the two most popular dataset finders around:
Kaggle is a data science website that features various datasets from external sources. Kaggle’s master list contains several niche datasets. This includes basketball data, ramen ratings, and even some city’s pet licenses.
A favorite for many, Kaggle allows users searching for a specific dataset type to engage in discussion about the data, develop their own projects in kernels, and find some public code. Each dataset is a tiny community where you can do a lot. Within Kaggle, you will find a broad range of real-life datasets. The datasets differ in format, shape, and size. Each dataset comes with kernels which contain notebooks by different data scientists. The notebooks can be used to analyze the dataset. At times, the notebooks are available with algorithms that resolve the specific dataset’s prediction problem.
With the UCI ML repository, you can do two things: develop a self-study program and build a strong ML foundation. A dataset source that has been around for some time, the UCI ML repository can be a good first step for finding interesting datasets. However, you need to keep in mind that the datasets are contributed by users and ergo, have varying degrees of cleanliness. On the upside, no registration is needed for downloading datasets from this resource.
The repository contains hundreds of datasets from the University of California’s Donald Bren School of Information and Computer Sciences. Moreover, the datasets in the repository are classified by the type of machine learning problem. The repository contains datasets for regression, univariate and multivariate time-series datasets, and recommendation systems. Some UCI datasets are made available after cleaning so they can be used readily.
General datasets are AI/ML datasets pertaining to the public and government, and finance and economics. These datasets are a good source of financial and economic data. Additionally, they provide key information related to statistics, choices, and surveys in the public and government sphere. Following are three of the most widely used general datasets:
This website allows users to download data from several U.S government agencies. Data.Gov provides users access to a range of data, such as performance scores in school, government budgets, changes in climate, data on public safety and health, and much more.
The federal government’s Executive brand generates the datasets on the website pertaining to a wide variety of subjects. However, additional research is undertaken by users who use the data to meet their objectives.
One of the best and most interesting general datasets available today is Google Trends. This online search tool can be used to investigate and analyze data on internet search activity. Using this tool, you can find the number of times specific phrases, subjects, and keywords have been queried over a specified period.
One of the largest real-time datasets in the world, Google Trends provides an interesting perspective on the current interests of people searching the internet. The number of searches performed for a specific term can be compared to total searches on Google for a particular period via this tool. This is done by analyzing a percentage of Google searches.
Although Google updates the data produced by Google Trends daily, the company includes a disclaimer with the data produced that warns users of inaccuracies in the data. In Google Trends, you can inquire about five words or topics at a time. Google Trends displays the results in a graph that is generally referred to as a ‘Search Volume Index’ graph. It is possible to export its data to a .csv file. You can then open this data in a spreadsheet application like Excel.
A good source for economic and financial data, Quandl is built to serve investment professionals. It can be used to develop models that predict stock prices or economic indicators. Used by hundreds of thousands of people, Quandl’s platform contains market data from several different sources. This data is delivered via API, or directly into Excel, R, Python, and many other data tools. By delivering the data you need in a format you want, Quandl can save you a lot of your precious time and money.
Many challenging real-world problems are now being solved with machine learning algorithms. When it comes to solving real-world problems with machine learning algorithms, the most important thing is having the right data in the right format. This means that you must have access to data that corresponds with the outcomes you want to predict. Following are some machine learning datasets that can help to ensure this:
Google’s Open Images is a dataset comprising of nine million URLs and images. This is enough to train a deep neural network from scratch. The images are illustrated using labels that span more than six thousand categories. These images are made available under the ‘Creative Commons’ license.
One of the older machine learning datasets, the Multidomain Sentiment Analysis Dataset features Amazon product reviews. By using neural networks to learn domain-specific input sentences’ representations, users can come up with a framework for multi-domain sentiment analysis.
Millions of reviews spanning eighteen years are present on Amazon Reviews. You will find a variety of data here; plain-text reviews, ratings, product and user information, and more. Amazon Reviews is the go-to-dataset for Amazon product reviews and metadata. The review data can be categorized into text, ratings, and helpfulness votes. On the other hand, the product metadata on Amazon review can be categorized into image features, brand, price, category information, and descriptions.
Wikipedia Links Data is an effort to provide people around the world with Wikipedia data and encourage them to download it for their use. The dataset is huge. It contains four million articles and about 2 billion words. Users can search what they are looking for by keyword, a phrase, or even an entire paragraph. Interested users are offered copies of the available content free of cost. There are several uses of Wikipedia Links Data including personal use, mirroring, databases queries, or informal backups.
At present, Berkeley DeepDrive BDD 100k is the biggest dataset for footage of self-driving cars powered by artificial intelligence (AI). The dataset comprises of more than a hundred thousand videos. The videos are of driving experiences that span over a thousand hours. Videos of driving experiences at different times of the day and in varying weather conditions can be found here.
WPI datasets are datasets for lane, pedestrian, and traffic lights detection. The datasets are categorized into WPI Traffic Light Dataset, WPI Pedestrian Dataset, and WPI Lane Keeping Dataset. The training data for all three datasets types are collected in Worcester, MA, USA.
The final machine learning dataset on our list is the MIMIC-III dataset. A clinical dataset, MIMIC-III is developed by the MIT lab for Computational Physiology. Access to the dataset is open and contains data pertaining to health. This data is associated with forty thousand critical care patients. The dataset contains the laboratory tests, vital signs, medications, demographics, and more of these critical care patients. The dataset can be incredibly useful for those in the healthcare sector.
Becoming familiar with AI and machine learning is anything but easy. This is the reason we have so many pretrained AI/ML datasets today. However, many people have difficulty in finding a dataset for their specific machine learning problem. Nonetheless, picking and using a suitable pretrained AI/ML dataset can be a great way of getting the ML journey started.
With the right dataset in hand, you can easily learn how to solve your specific machine learning problem. In our case, this would be using the pretrained AI/ML datasets for increasing the efficiency of AI software development.
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