Key Factors to Keep in Mind before Selecting an AutoML Platform
Automated Machine Learning, also known as AutoML refers to the process of automating the end-to-end process of applying machine learning to real-world problems. This includes data preparation steps like missing value imputation, normalization, transformation and scaling, feature extraction and feature engineering, model selection and hyper parameter tuning.
Today, machine learning tools are helping in a wide range of applications. However, many organizations struggle when it comes to deploying its models. AutoML helps in automating the maximum number of steps in a machine learning pipeline, with a minimum amount of human effort and without compromising the model’s performance. Moreover, by adding different optimization techniques AutoML allows data scientists to be more productive and achieve similar or better results in a shorter period of time. This is also important as in traditional Machine learning pipeline, the raw datasets are not refined and therefore they are not optimized for analytics, or to be fed to a learning algorithm.
Today, we have many AutoML tools in market viz., commercial AutoML (e.g., DataRobot, Dataiku DSS, Google Cloud HyperTune) and open source AutoML (e.g. Auto-WEKA, autosklearn, H2O, TransmorgrifAI, and TPOT).
While there is no one size fits all approach, there are key attributes of the AutoML that one must pay attention to before selecting one. According to Colin Priest is the VP of AI Strategy for DataRobot these are:
• Data preprocessing: As mentioned earlier, the raw data is not often ready nor optimized for further processes. Also, each model has distinct data requirements and functions. So, one must opt for AutoML tool that can help prepare data for each different algorithm, recognizes and preps dataset, and follows best practice for data partitioning.
• Feature engineering: It is the process of using domain knowledge of the data to create features that help machine learning algorithms to learn better. To distinguish which algorithm benefits from feature engineering and which doesn’t, one must look for AutoML platform that automatically engineer new features from existing numeric, categorical, and text features.
• Algorithms Diversity: Every data is unique! So, it is obvious that every dataset will contain information that is characteristic to every organization purpose. Hence, one dataset will not cater to every algorithm needs nor vice-versa. This is quintessentially why businesses require diverse arsenal of algorithms to test against all datasets, and find out which one works best for a particular data.
• Finding the Right Algorithm in Time: In today’s fast paced world, testing every algorithm will surely cost time and other resources. To counter this, one must have AutoML tool at disposal to help discover which algorithms make sense for one’s enterprise data and runs only those.
• Train Well: Machine learning models need to be trained on any particular algorithm to make sense of the input data feed, before it could start self-learning. Not only that, during the training phase, programmers should look for speed setting, feature selection, hyper parameter tuning and more. So, one must ensure that the AutoML platform can determine which features to include and which to leave out, and which feature selection method works best for different algorithms.
• Ensembling: It is a machine learning technique that combines several base models in order to produce one optimal predictive model. For this, ensemble model helps to concatenate the decisions from multiple models to improve the overall performance.
• Compare the Best: It is quite unlikely to know in advance which algorithm performs best on a given dataset. This is why, businesses should have an AutoML platform that facilitates comparison of the results on the basis of speed and accuracy, and ranks the best algorithms based on the company needs – apart from building and training dozens of algorithms.
• Easy Interpretability: In addition to finding out which algorithms offer better results, it is also crucial to ensure that the results can be translated to the human audience, in a manner that is comprehensible and coherent. For this, the selected AutoML should be capable of explaining model decisions in a human-interpretable manner. The platform should show which features are integral for each model, illustrate the patterns tailored for each feature and clarify why a prediction is either high or low. One must also check if the AutoML platform allows for automatic yet detailed model documentation.
• Easy to Deploy: Even if the selected AutoML model presented impressive results, there is always a possibility of an organization to fall deficit in having adequate infrastructure to directly implement the trained model in a production setting. To prevent this, identify the organization’s needs and then look for appropriate model. Or else prefer an AutoML platform that possess multiple deployment option including one-click deploy that can be operated by a business person.
• Adaptable Monitoring and Management: In a dynamic digital age, it might be hard to keep up with the trends and updates. Thus, an AutoML platform should proactively identify when a model’s performance is deteriorating over time, making it easy to compare predictions to actual results, simplifying the task of training a new model on the latest data.
Today enterprises can choose AutoML tools for implementing machine learning solutions without extensive programming knowledge, saving time and resources while leveraging best data science practices. AutoML also offers agile problem-solving, automates the data storage and identifies leaky spots and misconfigurations. The latter ensures accuracy and precision in the result, thus eliminating the risk of permeating biases. Further, it helps to build production-ready models quickly, and can empower companies with the abilities to use data-driven applications that are backed by statistical models. Also, AutoML plays a key role in accelerating productivity by automating repetitive tasks – enables a data scientist to focus more on the problem rather than the models.
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