AutoML Technology Plays a Vital Role in Digital Workplace
The extensive role of AutoML technology in digital workplace
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. Automated ML in Azure machine learning is based on work done in the Microsoft Research division. Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. With automated machine learning users accelerate the time it takes to get production-ready ML models with great ease and efficiency.
Connection of Data with AutoML
Data is, and always will be, complex to deal with. Although AutoML goes some way to assisting in the picking of, and tuning of, an algorithm, moving from an idea to proof of concept, to pilot and finally into production requires a whole new set of tools and capabilities. This means AutoML is a useful tool to have in your tool bag, but it doesn’t replicate every stage of getting your ML models into production. John Kane is head of signal processing and machine learning at Boston-based Cogito. He explained that training neural network models is an automated process where data is continuously fed as input to the model. The model outputs predictions, given its current weights, and based on these some error term is computed, and this is then propagated back through the network to update the weights of the model. This is done continuously until some stopping criteria is met.
Reduction of Human Interaction
Although the weights of the model are updated during training, the structure (or architecture) of the model is not. That is until AutoML came along. AutoML seeks to minimize the need for human intervention in the machine learning development process. One major focus of AutoML is to optimize not just the model weights, but also the architecture during training. The intention is to automate this architecture selection process which is traditionally done by scientific practitioners or via brute force grid searching.
ML Ops and AutoML
However, other focus areas under the AutoML umbrella have received much wider adoption, in particular areas related to ML Ops. ML Ops is the term typically associated with efforts to bring rigorous software engineering, data engineering and devops practices to machine learning. ML Ops started to receive intensive research and commercial interest from around 2015 where the now famous Google paper on the “Hidden Technical Debt in Machine Learning Systems” was published at the NeurIPS conference. State-of-the-art commercial machine learning systems now adopt ML Ops best practices, which ensures that the entire workflow from raw data to selected models (and even model deployment) is fully reproducible from start to finish. It also ensures that machine learning models are properly versioned, efficiently deployed to production, and with monitors, dashboards and alarms which allow machine learning engineers to have transparency as to how their model is behaving out in the real world.