What is the modern data science stack in relation to machine learning?

A data stack is a collection of technology systems that gather and store multiple data sources into a centralized place. A modern data science stack does this using the cloud, bringing together data into storage options like data warehouses or data lakes. And, a modern data science stack can be extended to enable more successful machine learning by making it easier for data scientists to focus on modeling instead of gathering and preparing data.

What makes up the modern data science stack? 

The modern data science stack typically consists of a variety of tools that:

• Ingest data
• Store data
• Transform data
• Make data available and visual for business intelligence

While some organizations choose to cobble together several data tools that integrate with each other to build their stack, platforms like Domo deliver a comprehensive, end-to-end solution in a single platform. 

With Domo, businesses can:

• Connect data, systems, and people at scale
• Transform data into insights 
• Visualize and share insights in dashboards
• Analyze, explore, and segment data quickly and collaboratively
• Predict new patterns
• Prescribe actions to improve efficiency
• Build apps for teams, customers, and partners with low/no code 
• Extend development success by monetizing data to generate value

Why is the modern data science stack important for machine learning?

Data scientists are in high demand, and yet they spend the majority of their time gathering, cleaning, and preparing data to be analyzed—instead of actually testing and analyzing the data. But, data has to be sufficiently prepared before machine learning models can be deployed. The modern data science stack automates this process to free up data scientists so they can focus on the science of learning and making predictions from data. 

With the modern data science stack, data science teams can create and test more machine learning models and avoid problems with data error and data quality. They can write queries, train a machine learning model, and publish predictions so they can be easily accessed by everyone in the company and monitor them over time. 

How does the modern data science stack work with machine learning? 

A traditional data stack employed a fairly linear approach to data collection, transformation, and analytics, but the modern data stack operates in a continuous cycle. 

• A data scientist identifies a problem or question that they want to solve or answer. 
• They collect data from various sources. 
• They prepare that data to work with a machine learning model by ensuring that it is clean and of sufficient quality.
• They train a machine learning model. 
• They validate the machine learning model. 
• They deploy the machine learning model. 
• They monitor the model’s performance, beginning the process again of collecting data and preparing it for future models. 

Throughout this process, data scientists can use platforms like Domo to visualize and articulate the story the data is telling. This story is presented in a way that makes it easy for anyone, no matter their data expertise, to understand what is happening and make informed decisions. 

With Domo, data scientists can write back their data results into the original source systems, which helps algorithms grow smarter over time. In other words, a data warehouse can be used as the single source of data storage and truth as well as the destination for predictions. This makes it simple to monitor a model’s performance, and, as more quality data is contributed to the data warehouse, every machine learning model automatically improves, too. 

Benefits of the modern data science stack.

The modern data science stack saves businesses and their data science teams valuable time and effort–not to mention boosts the bottom line. Cloud computing and automated data processes make it easy for every member of an organization to understand data and make better decisions. That means fewer silos and less duplicate work. Other benefits include:

• Eliminating the need to build and then maintain data connectors
• Faster query speeds
• Easy data visualization and reports that can be shared across departments
• Decreased reporting time
• More up-to-date reports
• Improved data reliability