When businesses identify a problem which can be solved by machine learning, they brief the data analysts and scientists to create a predictive analytics solution. In many cases, the turnaround period for delivering a solution is quite long.
Even for seasoned data scientists, switching machine learning models that can accurately forecast the outcomes is always challenging and time-consuming. The complex workflow involved in machine learning units have several stages. Some of the substantial measures include data acquisition, information mining, feature engineering, design selection, experimentation and prediction.
There are multiple teams that get involved in arriving at the solution. Data engineering team operates on data acquisition and preparation. Data scientists focus on experimentation and optimisation of models. DevOps teams possess the development environment, tooling, and hosting the inference versions in production.
One trend that is going to fundamentally alter the face of ML-based alternatives is AutoML. It is likely to allow business analysts and programmers to evolve devices learning units that can address complex situations.
AutoML focuses on two elements -- Statistics acquisition and prediction. All the measures which take place in between both of these phases will be abstracted from the AutoML platform. Essentially, users bring their own dataset to identify the tags, and push on a button to generate a thoroughly educated and optimized model that is ready to forecast.
When dealing with the AutoML platform, business analysts stay focused on the company problem instead of getting lost in the procedure and workflow. Most of the platforms prompt customers to manually upload the dataset and then labeling the categories. Then, most of the actions involved in preparing the information, choosing the right algorithm, optimization and hyperparameter tuning are handled behind the scenes. After a while, the system displays a REST endpoint which may be used for forecasts. This approach significantly changes the standard workflow involved with training machine learning models.
Some AutoML platforms also help exporting the fully trained model compatible with mobile devices running Android along with iOS. Developers can quickly incorporate the versions using their cellphone applications without having to understand the nuts and bolts of machine learning.
When AutoML models get exported into Docker containers, DevOps teams would have the ability to deploy them at scale for inferencing in the spheres of production. They can host the containers from scalable clusters handled by Kubernetes and DC/OS.
The business is gearing up to provide AutoML as a Service. Google Cloud AutoML, Microsoft Custom Vision and Clarifai's picture recognition service are early examples of automated ML services.
AutoML perfectly matches in between cognitive APIs and customized ML platforms. It delivers the perfect amount of customization without even forcing the developers to go through the elaborate workflow. Unlike cognitive APIs that are often considered as black boxes, AutoML exposes the same level of flexibility but with custom data along with portability.
With every platform seller attempting to democratize machine learning, AutoML is growing as the potential for artificial intelligence. It places the ability of AI in the hands of industry analysts and technology decision makers.