Data Analytics

AutoML is democratizing and improving AI

In today's time the usability and adoption of AI have seen drastic growth, and this is the reason why the data-driven industrial sector is working hard to make AutoML more advanced and innovative.
DataChannel Research
3 min to read

In the last few years, the need for Automated Machine Learning has seen a tremendous rise in different fields. Areas like healthcare, transportation, financial services, and others are using machine learning systems to automate almost all their processes so that they can focus on more critical issues. Now, if we talk about artificial intelligence, then it involves a lot of manual effort and trials for building predictive models with the highest accuracy. With the right AutoML capabilities, it becomes easier to build a model with less or no additional effort at all.

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Many data-driven companies are now integrating machine learning and AI to get the benefits of both the platforms in their day-to-day operations. AutoML helps in enhancing the capabilities of AI. To get you a better understanding of how AutoML is democratizing and improving AI, we are listing down the tasks that are efficiently addressed by AutoML:

  • The Feature Work: Big data sets require proper labeling and columns so that useful insights can be developed from them. But, sometimes its become a little tricky to figure out which columns in your set of data is right to predict the label’s value. This thing can hamper your organizational productivity level. But, with the right machine learning systems, you can easily pick the right columns and convert text-based values to numbers along with imputing missing values.

          The AutoML platform will help the data scientist to specify the features they want and will also analyze the data set to suggest the columns that can serve well           along with providing space for their modification.

  • Selection of the Algorithm: The task involves determining the specific algorithm of the right type, within the library. Choosing the algorithm is based on the structure of your data and the prediction you are trying to make. AutoML assists in picking an appropriate algorithm by comparing different algorithms on the basis of your requirements.
  • Hyperparameter Tuning: For controlling the configuration of the algorithm and the way it is applied to the data, certain parameters can be set, and an array of values can be accepted for each of them (although in a few cases you can go for default values). Setting hyperparameter values is crucial, and machine learning helps in the automatic tuning of hyperparameters.
  • Finding an accurate model: Machine learning automates the process of feature work, algorithm selection, and hyperparameter tuning as well as generates one combination of each for building models. The models are then tested for determining the best one. With AutoML, you can configure the metrics to determine the models as per your specific requirements. AutoML also aids in the training of the models and then display a leaderboard of the model that is accurate. Once the entire training process is completed, the highly-rated model is the most accurate one and mostly selected by the users.
  • Ensembling: AutoML will take the responsibility of creating and assembling a set of models so that they can behave like a single model. Ensembles are more accurate in comparison to single models.
  • Deployment of model: What’s the use of creating a model if it is never used. This is the time when the model required to be deployed to production and then monitored as well as managed to maintain its accuracy and efficiency.

         Model monitoring involves keeping a strict eye on the model by running a new set of data against it to check whether the accuracy is maintaining itself or not.

  • Retaining the model: AutoML systems will retain models that are accurate. The systems efficiently handle the entire process of model creation to its retaining. This is advanced stuff that is bringing the concept of constant deployment to AI.

Automating the tedious task of AI, AutoML helps in reducing the complexity of the tasks mentioned above. Machine learning is an app of the present time that makes AI the hero of every enterprise. The data scientists are benefiting from AutoML as now they can move on to complex tasks by automating other operations. The AutoML democratize and improves AI to help different sectors of the economy to grow at an exceptional rate.

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