Pipeline and Process Automation
Automated ML streamlines the complete ML pipeline - data extraction, processing, and storing for scalability, efficiency, and productivity.
What is Automation of Pipelines and Processes?
ML pipeline automates ML workflow and processes by orchestrating data flow – raw data, features, model inputs, and model outputs. Alternatively, ML pipelines also help split ML workflows into reusable and modular parts that can be combined to create models.
Automated ML (AutoML) streamlines the complete ML pipeline from data extraction to data processing to storing it at a data lake or warehouse before feeding into the model. An automated pipeline is a blueprint of different components involved in ML pipeline and their association to develop the most crucial element- the model.
Automated ML pipeline provides the following benefits:
- Enhanced productivity by automating repetitive tasks
- Reduced human errors that are more likely with manual workflows
- Improved ML accessibility to all users – technical as well as non-technical business users, promoting a decentralized process
How does AutoML Help?
Given the scarcity of machine learning professionals and data scientists, automated ML pipelines and processes is a great relief. AutoML is democratizing ML processes and boosting team productivity by automating repetitive and manual ML tasks.
With traditional ML processes, teams are more engaged in data preparation, cleaning, feature engineering, model testing, and monitoring tasks. On the other hand, AutoML supports the automation of most of these tasks. It helps address these challenges:
ML teams face different challenges. For example, tune hyperparameters, predict the best algorithm, and manually test models. AutoML allows you to abstract away from manual workflows to orchestrate data transfer and evaluate the best model to solve your problem. This helps you save time from hours to minutes.
Right talent scarcity is not new to the ML domain. Automation of ML pipelines and processes helps bridge the talent gap. AutoML simplifies building and deploying ML models by automating some of the time-consuming workflows in an ML pipeline.
AutoML streamlines the steps involved in devising ML solutions to solve real-world problems. It reduces the complexities involved in developing, testing, and deploying models at scale. It empowers non-technical users with UI and APIs for better automation. AutoML helps reduce resource costs by processing multiple algorithms simultaneously.
AutoML Pipelines – Toolkits and Processes
AutoML enables data science teams to boost productivity and realize their skills with true potential. Check the list of popular AutoML toolsets and frameworks helping enterprises.
Automation of pipelines using the right AutoML tool helps enhance data science workflows and automates repetitive tasks. However, AutoML is not a complete replacement for traditional ML and data science.