Data as a Service for Machine Learning

Data as a Service (DaaS) for Machine Learning streamlines the process of preparing data for ML models by providing dependable, clean, and organized data on-demand through APIs. Data science teams may concentrate on developing models rather than juggling data because DaaS solutions automate data collection, processing, and standardization. However, MLaaS (Machine Learning as a Service) democratizes AI by making it more affordable and accessible by giving users access to pre-trained models and machine learning tools.

Below are some things to consider when it comes to data as a service for machine learning:

How it works

• Data Collection: A DaaS provider gathers data from multiple sources like APIs, databases, and third-party datasets. 
• Data Processing: The raw data is cleansed, validated, and transformed into a structured format. Providers often use ML to identify and correct inconsistencies, and may perform feature engineering and standardization. 
• Storage: The processed data is stored in secure cloud databases, data warehouses, or data lakes. 
• Delivery: Users access the data on demand through various methods, including APIs, direct integrations, or downloadable files. 

Benefits for machine learning

• Faster development: Automating data preparation frees up data scientists to concentrate on building and refining models. 
• Higher data quality: DaaS provides cleaner, more consistent data that is essential for training high-quality ML algorithms. 
• Scalability: Cloud-based infrastructure allows for easy scaling to accommodate large datasets. 
• Reduced costs: Offloading data management and infrastructure to a third party can be more cost-effective than managing it in-house. 
• Breaking down silos: DaaS can provide a unified view of data, breaking down the silos that often exist between different departments. 

Potential considerations

• Data privacy and security: While providers have high standards, there is always a risk factor when data is in transit, which may be a concern for highly sensitive data.
• Latency: Transferring large datasets can be slow, potentially affecting real-time applications.
• Vendor dependency: Your solution’s availability is dependent on the vendor’s performance and uptime.

What Type of Data Does Machine Learning Need?

Data can come in many forms, but machine learning models rely on four primary data types. These include numerical data, categorical data, time series data, and text data.

Numerical data

Numerical data, or quantitative data, is any form of measurable data such as your height, weight, or the cost of your phone bill. You can determine if a set of data is numerical by attempting to average out the numbers or sort them in ascending or descending order. Exact or whole numbers (ie. 26 students in a class) are considered discrete numbers, while those which fall into a given range (ie. 3.6 percent interest rate) are considered continuous numbers. While learning this type of data, keep in mind that numerical data is not tied to any specific point in time, they are simply raw numbers.

Categorical data

Categorical data is sorted by defining characteristics. This can include gender, social class, ethnicity, hometown, the industry you work in, or a variety of other labels. While learning this data type, keep in mind that it is non-numerical, meaning you are unable to add them together, average them out, or sort them in any chronological order. Categorical data is great for grouping individuals or ideas that share similar attributes, helping your machine learning model streamline its data analysis.

Time series data

Time series data consists of data points that are indexed at specific points in time. More often than not, this data is collected at consistent intervals. Learning and utilizing time series data makes it easy to compare data from week to week, month to month, year to year, or according to any other time-based metric you desire. The distinct difference between time series data and numerical data is that time series data has established starting and ending points, while numerical data is simply a collection of numbers that aren’t rooted in particular time periods. 

Text data

Text data is simply words, sentences, or paragraphs that can provide some level of insight to your machine learning models. Since these words can be difficult for models to interpret on their own, they are most often grouped together or analyzed using various methods such as word frequency, text classification, or sentiment analysis.

Read Also: The Impact of Data as a Service on Data Quality

There are many locations to find machine learning data, but to get you started, we’ve put up a list of five of the most well-liked ML dataset resources:

Google’s Dataset Search

Google released their Google Dataset Search Engine in September 2018. Use this tool to view datasets across a wide array of topics such as global temperatures, housing market information, or anything else that peaks your interest. Once you enter your search, several applicable datasets will appear on the left side of your screen. Information will be included about each dataset’s date of publication, a description of the data, and a link to the data source. This is a popular ML dataset resource that can help you find unique machine learning data.

Microsoft Research Open Data

Microsoft is another technological leader who has created a database of free, curated datasets in the form of Microsoft Research Open Data. These datasets are available to the public and are used to “advance state-of-the-art research in areas such as natural language processing, computer vision, and domain specific sciences.” Download datasets from published research studies or copy them directly to a cloud-based Data Science Virtual Machine to enjoy reputable machine learning data.

Amazon datasets

Amazon Web Services (AWS) has grown to be one of the largest on-demand cloud computing platforms in the world. With so much data being stored on Amazon’s servers, a plethora of datasets have been made available to the public through AWS resources. These datasets are compiled into Amazon’s Registry of Open Data on AWS. Looking up datasets is straightforward, with a search function, dataset descriptions, and usage examples provided. This is one of the most popular ways to extract machine learning data.

UCI Machine Learning Repository

The University of California, School of Information and Computer Science, provides a large amount of information to the public through its UCI Machine Learning Repository database. This database is prime for machine learning data as it includes nearly 500 datasets, domain theories, and data generators which are used for “the empirical analysis of machine learning algorithms.” Not only does this make searching easy, but UCI also classifies each dataset by the type of machine learning problem, simplifying the process even further.

Government datasets

The United States Government has released several datasets for public use. As another great avenue for machine learning data, these datasets can be used for conducting research, creating data visualizations, developing web/mobile applications, and more. The US Government database can be found at Data.gov and contains information pertaining to industries such as education, ecosystems, agriculture, and public safety, among others. Many countries offer similar databases and most are fairly easy to find.

Additionally, machine learning eliminates uncertainty in decision-making. Machine learning algorithms can evaluate vast amounts of data to deliver thorough insights from a comprehensive picture, whereas you might be able to draw conclusions based on data averages from databases or spreadsheets. In brief, machine learning makes it possible to produce outputs with greater accuracy across an increasing number of inputs.