/ Posts / Data Wrangling for Machine Learning Projects
by N/A - Mike Mahoney
on October 16, 2019
In one of my first Data Science courses, the professor went on and on about the importance of data preparation for any machine learning project. In fact, they went on to say that the majority of machine learning projects should follow the 80/20 rule – 80% of work towards data preparation and 20% to the actual model analysis. This could not be closer to the truth as data preparation is a major time constraining aspect of machine learning.
Trifacta is a data wrangling tool that can speed up the process in data pre-processing, transformation, and cleaning bad data. With the Data Wrangling tool, data scientists and machine learning engineers are able to perform much more efficiently in this pivotal stage. Here are some ways Trifacta can help excel and enhance your next data science project.
Data Pre-Processing for Machine Learning:
Data preparation is a huge aspect in machine learning, it is crucial to the accuracy and effectiveness of any model. With automated machine learning tools such as DataRobot, machine learning models can be made in a timelier manner, yet there is still a considerable amount of effort that needs to be put into the data preparation. Subject matter experts (SMEs) spend countless hours transforming, formatting and cleansing their data. Some of the most common examples include filling values for null fields, data reduction, normalizing the data, and feature engineering.
Although there are multiple ways to perform these data preparation steps, Trifacta can perform these steps in a quick and efficient manner. Similar to the power of automated machine learning, a data wrangling tool like Trifacta turns a cumbersome and difficult chore into a simpler task, achievable by a much larger group of users and in a shorter amount of time. With Trifacta’s easy to use interface, and ability to search and execute various pre-processing steps, it is a great compliment to the efficiency and timeliness of automated machine learning tools. With just a few clicks, users in Trifacta are able to set null fields to a specific value, take out unnecessary or outlier data, and perform data transformation on fields to normalize them. Trifacta is not meant just for the data scientists or data engineers in the world. With its simplicity and straightforwardness, the tool is great for all users looking to get the most of their data.
Combining Data Sources into a Unified Source:
With the complexity involved with the majority of data science models, many are created from multiple data sources to fully understand the effect on a target and create realistic predictions. Organizations are exposed to large quantities of data stored in different places and typically data architects spend a great amount of effort consolidating and combing various data sources in order to create an optimal data model for their machine learning needs. Trifacta has the ability to connect to multiple data sources and combine them via union or join functions. Along with connecting data sources, Trifacta has a built-in artificial intelligence component (AI) which is able to recognize patterns of data fields and format the entire field as one format. This allows the data to follow a unified pattern, which is a central element in creating effective machine learning models. Trifacta is a one-stop destination for your data preparation that can combine and cleanse data from a plethora of sources.
Trifacta allows the data models you produced to be recreated or updated as new data comes along or if the data source changes altogether. This is crucial for model training because many machine learning models change or need to be revisited as more data emerges. With Trifacta, it is easy to refresh the data source used in your data preparation project to get the new data in the correct format, as well as even changing your data source entirely to recreate the new data source into the desired format.
Machine learning is considered an iterative process, as trial and error is widely used to figure out what type of model and features to use. The data preparation with machine learning should follow a similar technique. Especially with automated machine learning, the ability to try different data models is crucial to see what will lead to the best outcome. Trifacta is a versatile tool that will allow users to easily create multiple data models to see how the machine learning model’s output differ from one another. Being able to try multiple data models is a necessity with automated machine learning tools, and Trifacta allows users to iterate through multiple data models in their model building stage.
Automated machine learning is an advancement that will increase the efficiency and volume of machine learning projects. Although machine learning capabilities have improved with tools like DataRobot, data preparation is still needed to deploy effective models. Trifacta has the ability to improve data preparation for machine learning and reduce the amount of time spent in this stage. With its ability to conduct many of the transformations necessary in machine learning, combining multiple data sources into one unified data model, and being able to continue to add data seamlessly into a data preparation flow, Trifacta has emerged as vital tool that can enhance a machine learning project.
Reach out to email@example.com to get more information on how Trifacta can help with your organization’s data preparation needs and to see it in action.
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