Over the last few years, Machine Learning has revolutionized the way organizations conduct their day-to-day operations. However, building Machine Learning Models has always been a strenuous task since it involves many complexities ranging from Data Collection to Data Cleaning combined with the accuracy of predictions.
Of the many critical stages in building Machine Learning Models, Data Ingestion plays a significant role in the end result of Data-Driven Initiatives for organizations. Since data comes from different sources and has quality issues, building a robust Data Ingestion Pipeline is essential to feed the desired and relevant data into your systems and obtain superior Machine Learning Models to enhance your business operations.
In this article, we will learn about Data Ingestion Machine Learning and the different types of Machine Learning. We’ll also go over five stages to build Machine Learning Models in-depth and discuss some tools which you can use to quickly build your Data Pipelines.
Table of Contents
This Data Ingestion Machine Learning guide requires a basic understanding of the Data Ingestion process and Machine Learning types and models.
What is Data Ingestion?
Data Ingestion is a process of acquiring and integrating information from different sources inside a repository. A successful Data Ingestion process, one that helps Data-Driven Businesses capitalize on data, involves prioritizing Data Sources, and aggregating data only from sources that matter.
Data Ingestion must ensure that all your files and documents are collected from reliable sources and then sent to the appropriate centralized locations like a Data Warehouse or a Data Lake for subsequent analysis. Since Data Ingestion consolidates data in a centralized repository, it improves workforce productivity as well as data accessibility for all members involved in the data-driven process.
To know about the best open source Data Ingestion tools, visit our helpful guide here: Best 6 Data Ingestion Open Source Tools in 2022.
Types of Data Ingestion
Batch-Based Data Ingestion
The process of moving data from one or more sources to a centralized repository in batches at regular times is known as Batch-based Data Ingestion. In this type of Data Ingestion, the Data Ingestion Layer can use simple schedules, trigger events, or any other logical ordering to gather data.
Enterprises use Batch-based Data Ingestion when time is not a priority, as Batch-based Data Ingestion does not include Real-time Data Processing. Batch-based Data Ingestion excels over Real-time Data Processing in scenarios when the volume of data to be processed is huge.
Real-Time Data Ingestion
The process of moving data from one or more sources to a centralized repository in real-time is a Real-time Data Ingestion process. It does not involve any batches or groups for processing or moving data.
In Real-time Data Ingestion, the data is sourced, manipulated, and loaded as soon as it is created or recognized by the Data Ingestion Layer. Compared to Batch-based Data Ingestion, the Real-time Data Ingestion process is quite expensive, as it requires human resources for maintenance and software to monitor data sources continuously. However, it might be the best solution for Analytics Systems that need continually refreshed data or for teams where fast decisions are a must.
Lambda-Architecture-Based Data Ingestion
Lambda architecture is a Data Ingestion System that combines Real-time and Batch-based Data Ingestion processes. This Data Ingestion Setup has three layers: batch, serving, and speed, where the starting two layers hold data in a batch format. The speed layer in Lambda architecture is used to classify information to be pulled up by the batch and serving layers in real time.
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What is Machine Learning?
Machine Learning is a subset of Artificial Intelligence that allows your applications to become more accurate at predicting outcomes. In other words, Machine Learning uses data and algorithms to copy the way humans learn and improve its accuracy.
Machine Learning processes use historical data as input to predict new data or output values. These processes are used to find similar behavior or patterns in the data by mostly keeping historical data as a base. However, there are Machine Learning techniques that don’t necessarily rely on historical data for predictions. The main objective of Machine Learning is to enable computers to learn automatically without any human interaction and take action accordingly.
Types of Machine Learning
Supervised Machine Learning consists of “labeled” datasets to train or supervise algorithms for classifying data or predicting outcomes. Here, the objective is to take an input variable from the user and map an output variable based on trained models.
A Supervised Machine Learning Model can measure its accuracy by using the input and output from the labeled dataset. It is divided into two types – Classification and Regression – based on the nature of the problem.
Unsupervised Machine Learning Models analyze and cluster “unlabeled” data. It consists of algorithms that detect hidden patterns in data without human interaction.
Unsupervised Machine Learning Algorithms have the ability to discover similarities and differences in data and make it suitable for analysis. These algorithms are useful for three main tasks: Clustering, Association, and Dimensionality Reduction.
Reinforcement Learning is a type of training that rewards positive actions while punishing undesirable ones. A Reinforcement Learning Agent can sense and comprehend its surroundings, act, and learn via an iterative process in general.
For successful Reinforcement Learning, developers must provide algorithms with well-defined goals and specify rewards and punishments, which is comparable to Supervised Learning in some aspects.
Data Ingestion Machine Learning Stages
In this section of Data Ingestion Machine Learning, we discuss the different stages of building Machine Learning Models.
Data Ingestion Machine Learning Stage 1: Data Collection
Data Collection is the process of gathering data from different sources. For Machine Learning, you need diverse information to eliminate bias. Although there are other ways to remove bias, obtaining various data has a significant impact too. You must ensure that data is collected from reliable sources.
You need to create Data Pipelines to extract data from different sources and store it in a centralized location like Data Warehouses or a Data Lake. This repository will serve as the base where your Data Scientists can connect and use data to build Machine Learning Models.
Hevo Data Pipelines provide faster loading times and on-demand Data Transformations in your Destination. Our ETL Solution supports 100+ source connectors to migrate your data into a Data Warehouse. Try for free today!
Data Ingestion Machine Learning Stage 2: Data Preparation
The amount and quality of information utilized inside a Machine Learning Model can significantly impact your business outcome. Before handling Data Modeling and Interpretation, you have to investigate, pre-process, qualify, and change information in the Data Preparation Stage.
Getting each piece of data for each element inside a database is tough. Blank rows, numbers, or particular characters, including a questioning symbol, might indicate that information is missing. The image below illustrates an example of data that contains empty or null values.
Creating effective Machine Learning Models and subsequent algorithms requires you to ensure completeness in data. Otherwise, the repercussions of proceeding ahead without careful consideration can get bad for your business.
Providing a high-quality ETL solution can be a difficult task if you have a large volume of data. Hevo’s automated, No-code platform empowers you with everything you need to have for a smooth data replication experience.
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- Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
- Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Data Ingestion Machine Learning Stage 3: Model Selection
As we discussed, Machine Learning is about discovering trends or patterns in information using Supervised or Unsupervised Algorithms. In the model selection stage of Machine Learning, you must use your mathematics, computer programming, and business knowledge to effectively build a Machine Learning Model that would generate recommendations depending on the evidence you supply.
It is a critical stage that would decide the precision and reliability of making predictions for novel scenarios — furthermore, Machine Learning techniques aid in identifying crucial trends having good profit potential.
Data Ingestion Machine Learning Stage 4: Feature Engineering
When utilizing Machine Learning and Quantitative Modeling to create a statistical model, Feature Engineering involves leveraging subject matter expertise to select and modify the relevant key parameters in original data. Together, Feature Engineering & Data Selection aims to help Machine Learning techniques perform much better.
Data Ingestion Machine Learning Stage 5: Model Deployment
Model Deployment is the process of integrating a Machine Learning Model into an established operational environment to make Data-Driven Management Decisions. It is the last step in the Data Ingestion Machine Learning process, and it’s also one of the most time-consuming.
To ensure that your Data Model functions consistently in the organization’s operational environment, your Data Engineers, IT Departments, Software Engineers, and Business Experts must work together.
To gain the best out of Machine Learning Models, it’s critical to bring them into operation as quickly as possible so that your company can start utilizing them and make legitimate decisions. Finally, Machine Learning must add meaning to the company and have a beneficial influence. Therefore, keeping track of the framework throughout manufacturing is crucial.
Data Ingestion is the technique of obtaining raw data from one or more sources and transforming it to make it suitable for training Machine Learning Models. It is a time-consuming process, particularly if you’re doing it manually and have a lot of information from several sources. Automating this process leads to greater efficiency and guarantees that your models are based on the most up-to-date and relevant data.
As a result, a Data Ingestion Pipeline may shorten the time required to gather information for Machine Learning Model building, increasing the value of the Machine Learning investments. This reduces the expenses of running an extensive capability network and the expenses of losing momentum whenever a smaller capacity network is overcrowded.
There are several Cloud Data Ingestion Tools that you can utilize to set up your Data Ingestion. Some of these are listed below:
Azure Data Factory
Azure Data Factory natively supports Data Source Monitoring & Triggers for Data Intake Pipelines. You can modify the data and store it in an export blob bucket, which will be used for Azure Machine Learning information storage.
The Azure Data Factory Pipeline activates a training Machine Learning Pipeline that gets processed information for model training once it has been saved. Data Ingestion operations may be included in an Azure Machine Learning Pipeline phase using the Python SDK.
Recommended: Data Ingestion Azure Data Factory Simplified 101.
AWS Glue is a Cloud-hosted Data Integration Tool that makes discovering, organizing, and integrating information for analysis, Machine Learning, and application development easy.
AWS Glue provides serverless facilities which take care of provisioning, configuring, and scaling the resources needed to conduct your Data Ingestion activities. Glue has visual and code-based interfaces for discovering and extracting data from a variety of sources. With AWS Glue, you can not only collect data but also can perform transformations before feeding data to Machine Learning tasks.
Recommended: AWS Glue Workflow Made Easy: How to Create & Build in 3 Steps.
Data Ingestion is integral to building Data Pipelines for Machine Learning Models. You can leverage several Data Ingestion Tools to simplify the process of obtaining quality data through Automation. One such excellent tool is Hevo Data. This allows you to focus better on building and optimizing your Machine Learning Models and take your business to new heights of profitability.
Hevo Data, a No-Code Data Pipeline Automation Tool, makes it easy for ETL beginners to set up and run their Data Pipelines. Using Hevo, you can have Complete Visibility of the entire Data Replication activity for your Pipeline via the Pipelines Detailed View. Not only this, but Hevo also provides On-demand Transformations using which you can perform transformations on your data after loading it into the destination.
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Hevo, with its strong integration with 100+ Data Sources (40+ Free Sources), allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.
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