Machine learning, the study of computer algorithms, plays a vital role in businesses’ day-to-day operations, such as product recommendations, online customer support, online fraud detection, and more. And with the growing application of machine learning (ML), automation has become a must to improve model accuracy, expedite ML processes, and make them more efficient, which could take months without automation.
Essentially, automated machine learning refers to automating the building of machine learning models, such as training data configuration and selection, multiple model training, model performance evaluation, and ML model selection.
As such, companies and organizations with little domain knowledge can hugely benefit from machine learning automation. So, how can you automate your ML workflow? In this article, you’ll gain insights and tips on automating your machine learning workflow to benefit your organization at an optimal level.
1. Build Machine Learning Pipelines
Data scientists build ML models following machine learning pipelines. However, an inefficient ML pipeline can affect producing ML models at scale. For this reason, focus on building a streamlined ML process by streamlining or standardizing their workflow.
Building a seamless process involves automating your ML workflow using Machine Learning Operations technology. MLOps refers to a combination of processes and best practices that provides a centralized way to scale and automate ML application deployment in production environments. It aids in eradicating any obstacles or friction between IT, data science teams, and DevOps.
You can go to this website to learn more about building machine learning pipelines.
2. Determine What Tasks To Automate
Not all ML processes are suitable for automation. However, many ML steps and processes are iterative and are ideal for automation, such as model training.
To help you start, here are some of the ML steps and processes you can automate or optimize:
Examples of hyperparameters include activations functions, learning rate, pooling size, and hidden layers and units. Hyperparameters are defined values before an ML model undergoes training.
By optimizing hyperparameters, you can improve your ML models by using the right tool for grid search, random search, and other search algorithms.
- Model Selection
This process of choosing the right candidate ML model depends on model complexity, performance, resources available, and maintainability. Model selection determines the model development pipeline’s structure using more extensive filtering with the same goal as hyperparameter optimization.
- Feature Selection
This process refines the number of predictor variables in an ML model, which affects its ability to understand, train, and run. Once feature selection is automated, it can use different algorithmic methods, like a filter, embedded, or wallpaper, and the feature with the lowest error rate and is most relevant is selected.
You can also automate transfer learning, network architecture search, data pre-processing, and advanced pre-processing involving data encoding, cleaning, and verification.
3. Use A Guided Application Blueprint
Using guided automation applications can help automate the development of machine learning models for material handling, malware filtering, other business processes. More so, it can help build a generic classification model, automate data preparation, model evaluation, feature engineering, model deployment, and more. However, creating a generic classification ML model can be more complex.
The solution to this ML problem is using an interactive application’s blueprint. It can help create machine learning classification models quickly and automatically. The main concept of using a guided automation application blueprint includes the following steps:
- Data uploading
- Defining application settings via human interaction
- Training and optimizing automated ML model based on previously defined settings
- Downloading models
4. Use Automated ML Frameworks
Data from the real world can be inconsistent and inaccurate. So, it’s important to clean, structure, and format data to make it ML model-ready and drive relevant insights. However, data scientists spend too much time cleaning and organizing data, occupying 60% of their workload. The solution to this problem is using automated machine learning software or frameworks.
Tasks like cleaning and organizing data can be easily automated by building ML pipelines using Automated machine learning (AutoML) frameworks.
Furthermore, the following are some of the advantages of using AutoML frameworks:
- Handle Missing Values: AutoML frameworks can also detect missing values and implement data imputation techniques. In this way, the algorithm doesn’t ignore all crucial data points.
- Handle Outliers: Outliers or anomalous data points can negatively affect an ML model’s test results. Using AutoML software can help remove outliers.
You can now automate machine learning workflow using the tips shared above for your organization or enterprise to benefit greatly. Build a machine learning pipeline for your data team and use the right tools to help you attain your ML goals. In that way, you can reduce the need to use knowledge-based resources to train and implement ML models.
Author Bio: Amanda Nelson is currently a professional content writer.