Automated machine learning (autoML) is the process of applying tools to data to apply the machine learning process to a real-world problem. Applying machine learning to a new dataset is a complicated process, and autoML systems provide tools and customization options. Machine learning is now deployed across many kinds of applications, and more often than not, we are seeing machine learning used to make recommendations and predictions across multiple use cases.
Machine learning could potentially save time and money and help companies gain a competitive advantage in the marketplace through faster responses. AutoML-enabled tools can create models that are improved and customized. Most of all, autoML makes machine learning available to people without any real specialization.
A lot of autoML tools out there offer a vast open source community comprised of thousands of data scientists and organizations that actively contribute to the pool. Through their tools, business users without expertise in machine learning models can gain deeper insights from data and assist in the identification of the most suitable model. Through these insights, the focus is shifted from developing and trying different models to identifying the best and most fitting one.
The interfaces are designed to enable the training of a large number of models and the production of numerous results. These tools are useful for advanced users who will be able to generate a large number of results. These results will lead to the identification and eventual adoption of suitable models.
AutoML uses the neural networks for your tagged data through its existing, trained neural network. That network is trained on other data to optimize your results and guide you in the process so that you end up with better results. The neural architecture of the technology identifies the appropriate network layers for language pair translation, natural science classification and image classification. Again, users without in-depth knowledge in data science or machine learning are able to develop and train advanced models.
There are several techniques available. For example, transfer learning and neural architecture search are the core techniques. The core transfer learning technique enables smaller datasets to use the data of the earlier models to generate results. Transfer learning and neural architecture are the opposite and are applied differently. For neural architecture, users start from scratch, making it difficult to work with small datasets. On the other hand, transfer learning uses pre-existing datasets and works conveniently for smaller datasets by learning from pre-trained datasets.
Once models are created, statistical feedback can improve performance and create even better results. Converting raw data into usable, real-time predictions through one interface from start to finish is becoming a reality. All of the information and results are stacked in one place, with easy access to comparison and selection.
Users are allowed to customize and refer to open-source libraries and enable a larger team to work together. This way, more people have access to the data and can analyze the results concurrently. Users can access a number of functions and derive numerous results based on access to the libraries and datasets.
These platforms enable differentiation on accuracy, speed and explainable predictions. It creates an ensemble with the models that performed best together to increase accuracy.
I encourage you to experiment with these tools. Some companies do provide a community edition, some have SaaS pricing with startup-friendly models and some have better enterprise support. I recommend evaluating a few tools and doing a proof of concept before deciding on a tool that works best with your data and pricing model.
Most machine learning processes require very highly qualified data scientists and engineers to sanitize data, train it and predict and iterate on this process. AutoML platforms are working on improving the process to simplify the skills needed and enable automation of certain steps. Ultimately, this could make datasets consumable for the machine learning process and allow practitioners to easily select algorithms and optimization techniques to extract the best predictive performance against the chosen model.
