Automated Machine Learning, or AutoML is the process of automating end-to-end task which requires application of ML techniques to real-life problems. AutoML platforms uses this technique and run systematic processes on raw data and applies models based on the data.
How AutoML works
Traditional ML models involves pre-processing of data, feature engineering, feature extraction and feature selection to pick the suitable model. Then we fine-tune the hyperparameters manually to achieve the optimum result. By adopting AutoML platforms, we can automate all of these processes. AutoML platforms majorly use two concepts: Transfer Learning and Neural Architecture Search.
In transfer learning, a pre-trained model with a similar dataset is applied on a new model. The last few layers of the neural network is taken out and new layers specific to the data is retrained. Due to this, AutoML can attain higher accuracy with small amount of data. This is because the pre-trained models already have the background on which the newer layers pass through.
Neural Architecture Search (NAS) is the process of automating the neural network design as per requirement.
Benefit of AutoML
First is it’s simplicity. Since the main focus of AutoML is model building, testing, deployment, thus, here itself we can reduce the complexity to a great extent.
Second, the speed. Deploying a good model requires multiple steps. Since the key steps involved in modeling is automated, teams will now be able to reduce the time needed to create functional modes. It not only saves time, but also improves on transparency.
With evolving technology, the ML models are now able to create effective analogs of specific human learning processes. It also helps in automation of key hyperparameter selection and balancing.
As AutoML deals with multiple models, thus it can provide good results even with 100 records of data. This is so because the pre-trained models already has the background which the final model gets on top of it.
Current Issues with AutoML Platforms
Though it helps in model generation, the concern arises when we have to rely on the model’s outcome. How to believe that AutoML has chosen the best fitted model. Thus, even though it eases the load and makes the whole flow lucid, we always need to verify it’s performance.
Another issue which lies with the usage of AutoML is the technical know how. The data scientists who are involved in automating the process should be fully aware of the whole workflow. Else the results from this automation might not prove desirable. Thus, the people must be having end-to-end knowledge before using this technique.
AutoML platforms can provide immense benefit to any organization embracing it. But human intervention is always necessary to improve operational output. This is so because automatically generated features will pave way for new KPIs that the teams involved can monitor and work upon. Automating tasks will lessen the burden of the experts for sure, but we cannot yet now automate all the tasks. People looking forward to AutoML platforms must be aware of what to automate and what not to.
