AUTOMATED MODELING WITH B2METRIC
The process of automating the wasteful, iterative jobs of machine learning model development is called automated machine learning, also referred to as AutoML. It permits analysts, data scientists, and developers to build ML models with high scale, efficiency, and productivity, all while sustaining model quality. B2Metric AI's main technology is AutoML. Automated machine learning is the process of automating the end-to-end process of applying machine learning to complex real-world problems. B2Metric AutoML makes machine learning available in the right sense, even to people with no major expertise in this field.
AutoML takes advantage of the strengths of both humans and computers. Humans are proficient at communication, engagement, context, and knowledge, as well as creativity and insight. Software systems and computers are excellent for repeated tasks, mathematics, data manipulation, and parallel processing. Also, they provide humans with the ability to master complex solutions.
The traditional ML model development process is resource- and labor-intensive, requiring critical domain expertise and time to produce and compare dozens of models. Apply automated ML when you want B2Metric Machine Learning to train and tune a model for you using the target metric you specify.
Manually finding the right algorithm and tuning it to fit your dataset is a challenging task. B2Metric AI technology automates algorithm selection and hyperparameter optimization on algorithms ranging from classical sci-kit-learn algorithms to complex time series algorithms. Every model built into B2Metric AI can be put into production right away. You can upload data to be stored in bundles. Monitor the performance of all deployed models from a central portal and easily refresh and replace models if data and accuracy change over time.
Automated ML replaces much of the work that is done by hand in a more traditional data science process. But if it is to be considered a fully automated machine learning solution, a platform must meet these key points: Preparing Data, Feature Engineering, Diverse Algorithms, Algorithm Selection, Training and Tuning, Ensembling, Head-to-Head Model Competitions, Human-Friendly Insights, Easy Deployment, Model Monitoring, and Management.
The success of machine learning in various applications has led to an ever-increasing demand for ML systems that can be used off the shelf by non-specialists. It aims to automate the maximum number of steps in an ML pipeline with the minimum amount of human effort without compromising the model’s performance.
