{"id":256246,"date":"2023-10-16T11:28:36","date_gmt":"2023-10-16T11:28:36","guid":{"rendered":"https:\/\/imarticus.org\/?p=256246"},"modified":"2024-07-15T10:20:56","modified_gmt":"2024-07-15T10:20:56","slug":"engineering-and-modelling-data-for-ml-driven-systems","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/engineering-and-modelling-data-for-ml-driven-systems\/","title":{"rendered":"Engineering and Modelling Data for ML-Driven Systems"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">A key component of data-driven research and engineering is designing and modelling data for ML-driven systems. Understanding the significance of developing and modelling data for ML-driven systems is crucial, given the expanding use of machine learning (ML) in many industries.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A subset of artificial intelligence (AI) known as machine learning involves teaching computer experts to learn from data and form conclusions or predictions. Since ML-driven systems are built and trained on data, the ML model and algorithm must also be adjusted when the underlying data changes. To <\/span><span style=\"font-weight: 400;\">become a data analyst<\/span><span style=\"font-weight: 400;\">, enrol in a <\/span><strong><a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics\/\">data science course<\/a><\/strong><span style=\"font-weight: 400;\"> and obtain a <\/span><span style=\"font-weight: 400;\">data analytics certification course<\/span><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2><strong>Data Engineering<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Data engineering is designing, creating, and maintaining the infrastructure and systems that enable businesses to gather, store, process, and analyse vast amounts of data. Data engineers are responsible for building and managing the pipelines that carry data from multiple sources into a data warehouse, where <strong><a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics\/\">data scientists and analysts<\/a><\/strong> can convert and analyse it.<\/span><\/p>\n<h3><strong>Techniques for Data Cleaning and Preprocessing<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">Data cleaning and preprocessing are key techniques in data engineering that comprise detecting and rectifying flaws, inconsistencies, and missing values in the data. Some typical techniques for data cleaning and preprocessing include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Removing duplicates<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Handling missing values<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Standardising data types<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Normalising data Handling outliers<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Feature scaling<\/span><\/li>\n<\/ul>\n<h3><strong>Tools for Data Engineering<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">There are numerous tools available for data engineering, and the most often used ones vary depending on the firm and the particular demands of the project. Some of the most prominent data engineering tools include:<\/span><\/p>\n<p><b>Python:<\/b><span style=\"font-weight: 400;\"> It is a powerful and easy-to-use programming language commonly employed for data engineering projects.<\/span><\/p>\n<p><b>SQL:<\/b><span style=\"font-weight: 400;\"> A language used for managing and accessing relational databases.<\/span><\/p>\n<p><b>Apache Spark:<\/b><span style=\"font-weight: 400;\"> A distributed computing solution that can rapidly process enormous volumes of data.<\/span><\/p>\n<p><b>Amazon Redshift:<\/b><span style=\"font-weight: 400;\"> A cloud-based data warehousing system that can handle petabyte-scale data warehouses.<\/span><\/p>\n<p><b>PostgreSQL:<\/b><span style=\"font-weight: 400;\"> An open-source relational database management system.<\/span><\/p>\n<p><b>MongoDB:<\/b><span style=\"font-weight: 400;\"> A NoSQL document-oriented database.<\/span><\/p>\n<p><b>Apache Kafka<\/b><span style=\"font-weight: 400;\">: A distributed streaming infrastructure that can manage enormous volumes of real-time data.<\/span><\/p>\n<p><b>Apache Airflow:<\/b><span style=\"font-weight: 400;\"> A programmatic writing, scheduling, and monitoring platform.<\/span><\/p>\n<p><b>Talend:<\/b><span style=\"font-weight: 400;\"> An open-source data integration platform.<\/span><\/p>\n<p><b>Tableau:<\/b><span style=\"font-weight: 400;\"> A data visualisation programme that can connect to multiple data sources and build interactive dashboards.<\/span><\/p>\n<h2><strong>Data Modelling<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Data modelling is developing a visual representation of a software system or sections of it to express linkages between data. It entails building a conceptual representation of data objects and their connections. Data modelling often comprises numerous processes, including requirements collecting, conceptual design, logical design, physical design, and implementation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Data modelling helps an organisation use its data efficiently to satisfy business demands for information. Data modelling tools aid in constructing a database and enable the construction and documenting of models representing the structures, flows, mappings and transformations, connections, and data quality. Some standard data modeling tools are ER\/Studio, Toad Data Modeler, and Oracle SQL Developer Data Modeler.<\/span><\/p>\n<p><strong>There are several types of data models used in data modelling. Here are the most common ones:<\/strong><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-264802 size-large\" src=\"https:\/\/imarticus.org\/blog\/wp-content\/uploads\/2023\/10\/types-of-data-models-1024x576.jpg\" alt=\"types of data models\" width=\"1024\" height=\"576\" srcset=\"https:\/\/imarticus.org\/blog\/wp-content\/uploads\/2023\/10\/types-of-data-models-1024x576.jpg 1024w, https:\/\/imarticus.org\/blog\/wp-content\/uploads\/2023\/10\/types-of-data-models-300x169.jpg 300w, https:\/\/imarticus.org\/blog\/wp-content\/uploads\/2023\/10\/types-of-data-models-768x432.jpg 768w, https:\/\/imarticus.org\/blog\/wp-content\/uploads\/2023\/10\/types-of-data-models.jpg 1344w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Relational data model:<\/b><span style=\"font-weight: 400;\"> This paradigm groups data into &#8220;relations&#8221; tables organised in rows and columns. All the rows or &#8220;tuples&#8221; have a series of connected data values, and the table name and column names or characteristics explain the data.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Hierarchical data model:<\/b><span style=\"font-weight: 400;\"> This model represents one-to-many relationships in a tree-like structure. It is useful for displaying data with a clear parent-child connection.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Network data model:<\/b><span style=\"font-weight: 400;\"> This model is similar to the hierarchical model but allows for many-to-many relationships between nodes. It is handy for representing complex data relationships.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Entity-relationship (ER) model:<\/b><span style=\"font-weight: 400;\"> This model represents entities and their relationships to each other. It is effective for describing complex data relationships and is often used in database architecture.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Dimensional data model:<\/b><span style=\"font-weight: 400;\"> This model is used for data warehousing and business intelligence. It organises data into dimensions and metrics, allowing for easier analysis and reporting.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Graph data model:<\/b><span style=\"font-weight: 400;\"> This model represents data as nodes and edges, enabling complicated relationships to be easily expressed and evaluated.<\/span><\/li>\n<\/ul>\n<h2><strong>Machine Learning<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Machine learning is a discipline of artificial intelligence that focuses on constructing algorithms and models that allow computers to learn from data and improve their performance on a specific job. Machine learning algorithms utilise computer technology to learn straight from data without depending on a predetermined equation as a model.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Machine learning may be roughly classified into two basic types: supervised and unsupervised. Supervised learning includes training a model using known input and output data, enabling it to make predictions for future outputs. In contrast, unsupervised learning identifies latent patterns or underlying structures within incoming data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Machine learning starts with data obtained and produced to be utilised as training data. The more info, the better the tools. Machine learning is highly adapted for scenarios involving masses of data, such as photos from sensors or sales records. Machine learning is actively applied today for various purposes, including tailored ideas on social networking sites like Facebook.<\/span><\/p>\n<h2><strong>Integration of Data Engineering, Data Modelling, and Machine Learning<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">For data science initiatives to be successful, data engineering, data modelling, and machine learning must all work together. Data modelling guarantees that data is correctly structured and prepared for analysis, whereas data engineering creates the infrastructure and basis for data modelling and machine learning. Machine learning algorithms leverage data from data engineering and modelling to extract insights and value from data.<\/span><\/p>\n<p><strong>Examples of how data engineering, data modelling, and machine learning may be coupled include as follows:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data engineers&#8217; creation of data pipelines allows for the training and prediction of machine learning algorithms using data.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In addition to ensuring that the data is appropriately arranged and displayed, data modelling may be used to develop a model that accurately reflects the data utilised by machine learning algorithms.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data analysis and insight-providing capabilities of machine learning algorithms may be used to enhance data engineering and data modelling procedures.<\/span><\/li>\n<\/ul>\n<h4><strong>Conclusion<\/strong><\/h4>\n<p><span style=\"font-weight: 400;\">The success of ML-driven systems is based on the <\/span><span style=\"font-weight: 400;\">engineering and modelling of data used in these systems<\/span><span style=\"font-weight: 400;\">. While smart data modelling enables the development of strong machine-learning models that can make accurate predictions and generate insightful information, effective data engineering ensures that the data is clean, relevant, and accessible.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Imarticus Learning provides a <\/span><strong><a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics\/\">Postgraduate Program in Data Science and Analytics<\/a><\/strong><span style=\"font-weight: 400;\"> that is meant to assist learners in creating a strong foundation for a <\/span><strong><a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics\/\">career in data science<\/a><\/strong><span style=\"font-weight: 400;\"> or a <\/span><span style=\"font-weight: 400;\">career in data analytics<\/span><span style=\"font-weight: 400;\">. The <\/span><span style=\"font-weight: 400;\">data science training<\/span><span style=\"font-weight: 400;\"> curriculum is 6 months long and includes Python, SQL, data analytics, machine learning, Power BI, and Tableau. The <\/span><span style=\"font-weight: 400;\">data analytics course<\/span><span style=\"font-weight: 400;\"> also provides specific programmes to focus on various data science employment opportunities. Upon completing the <\/span><span style=\"font-weight: 400;\">data science course<\/span><span style=\"font-weight: 400;\">, learners receive a <\/span><span style=\"font-weight: 400;\">data science certification<\/span><span style=\"font-weight: 400;\"> from Imarticus Learning.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A key component of data-driven research and engineering is designing and modelling data for ML-driven systems. Understanding the significance of developing and modelling data for ML-driven systems is crucial, given the expanding use of machine learning (ML) in many industries. A subset of artificial intelligence (AI) known as machine learning involves teaching computer experts to [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":264801,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_mo_disable_npp":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[23],"tags":[2633,3638],"class_list":["post-256246","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-best-data-science-cours","tag-become-a-data-analyst"],"acf":[],"aioseo_notices":[],"modified_by":"Imarticus Learning","_links":{"self":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/256246","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/comments?post=256246"}],"version-history":[{"count":2,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/256246\/revisions"}],"predecessor-version":[{"id":264804,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/256246\/revisions\/264804"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media\/264801"}],"wp:attachment":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media?parent=256246"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/categories?post=256246"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/tags?post=256246"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}