Les modèles d'apprentissage automatique (ML) dépendent fortement de données adaptées pour fournir des informations et des prévisions précises. Les données brutes doivent être prétraitées ou préparées selon une série d'étapes afin de les rendre compatibles avec le traitement par l'intelligence artificielle (IA) et l'apprentissage automatique.
Why is Data Preparation Important for Effective Machine Learning?
prise de décision non éclairée prise de décision une entreprise, car elle mobilise du temps et de l'énergie pour mettre en œuvre un plan dont les chances de réussite sont minces. L'apprentissage automatique peut aider à prendre des décisions mieux informées et fondées sur les données. Cependant, la qualité des modèles d'apprentissage automatique dépend entièrement de celle de vos données. Des données de mauvaise qualité fausseront les prévisions modèle de machine learning le modèle de machine learning . Investir dans la préparation des données améliore la qualité des données sur lesquelles s'appuient les décideurs, ce qui augmente les chances d'obtenir un résultat positif.
Data Preparation for Machine Learning
The following data preparation processes will improve the data quality used for machine learning.
Data Profiling
Understanding source data sets better through data profiling helps to formulate data preparation. Data profiling involves scanning a data source to determine its size, variability, structure, and content. The output from profiling can include identifying duplicate records, binning data values into ranges, and calculating Min, Max, Mean, Median, Mode, Standard Deviation, Sum, and Variance statistics.
Cleansing Data
Data profiling will help identify field delimiters, which the data cleansing data process will use to make the data fields and records consistent by standardizing data types and file formats.
Filtering Out Data
Knowing what questions the data will be used to answer or what correlations the machine learning model is looking for helps determine what data can be discarded to avoid skewing the model. Outlying values and unnecessary data can be removed. Any duplicate records can be deleted.
Transforming Data
When data is collected from multiple sources, many fields can be inconsistent. Date formats may vary, number fields can contain currency symbols, and numeric values can differ. Data transformation can correct these inconsistencies. Leading or trailing spaces can be made consistent. Data subject to regulations can be masked or obfuscated to protect customer privacy without impacting the results from the ML model.
Enrichment of Data
Data sets can be enriched by adding calculated values, merging related data from multiple sources, and bucketing discrete data values data into ranges. Gaps can also be filled by adding default values, extrapolating, or interpolating field values. Data from internal systems can be combined with external third-party data to add a market context.
Partitioning Machine Learning Data
When datasets are too large to be read by a single process, they can be partitioned into sub-sets and placed on different devices for faster ingestion through parallel execution. Partitioning data can be done by hashing values for random distribution or by a key value to distribute slices evenly across partitions.
Data Validation
Data validation is often the final step in data preparation and is used to assess the data quality.
Automation of Data Preparation for Machine Learning
The steps of the data preparation process can be chained into a data pipeline process using a data integration solution that can orchestrate and schedule the individual data preprocessing steps.
The Benefits of Data Preparation for Machine Learning
Some of the benefits of data preprocessing include the following:
- Preprocessed data yields better results from machine learning models.
- Prepared data is better able to support traditional business analytics.
- ML training models can reuse existing data pipelines for faster data preparation.
- Preprocessed data results in improved outcomes that increase agility and competitiveness.
- Preprocessed data is of higher quality, making it more authoritative and trusted.
- Data engineers are more productive as model training times are reduced.
Actian and Data Preparation
Actian and the Data Intelligence Platform
Actian Data Intelligence Platform is purpose-built to help organizations unify, manage, and understand their data across hybrid environments. It brings together metadata management, governance, lineage, quality monitoring, and automation in a single platform. This enables teams to see where data comes from, how it’s used, and whether it meets internal and external requirements.
Actian DataConnect provides an intelligent, low-code integration platform to address complex use cases with automated, intuitive, and reusable integrations. DataConnect includes a graphical studio for visually designing data pipelines, mapping data fields and data transformations. Data preparation pipelines can be centrally managed, lowering administration costs.
La base de donnéesActian Analytics Engine facilite l'analyse des données à haut débit grâce à sa stockage en colonnes , qui réduit au minimum le recours à des index de données préexistants. Analytics Engine prend en charge les fonctions utilisateur pouvant héberger des algorithmes d'apprentissage automatique. Analytics Engine accélère le traitement des requêtes en exploitant plusieurs processeur à partir d'une seule instruction.
Through its centralized interface, Actian supports real-time insight into data structures and flows, making it easier to apply policies, resolve issues, and collaborate across departments. The platform also helps connect data to business context, enabling teams to use data more effectively and responsibly. Actian’s platform is designed to scale with evolving data ecosystems, supporting consistent, intelligent, and secure data use across the enterprise. Request your personalized demo.
