The year is 2129 and children are no longer taught to read. Every necessary purchase is automated and delivered by Amazon Robots. There are four people left in the United States who still remember what it was like to drive to work, uphill, both ways. These people love to complain that nothing is human, and they dream of the good old days when machine learning just meant an entertainment application recommended a movie.
It is important to understand the process of automation, so that this vision of the future is as unrealistic in our minds as it is in reality. Creating a single ML project is a challenge and mishandling any step could destroy the whole project.
Skyl has streamlined the workflow, allowing any company to implement a scalable and complex ML project. At Skyl, we hope to demystify this process, this is how our product handles each step of the ML workflow:
Data collection- The Skyl platform is capable of collecting many forms of unstructured data, ranging from text to audio files to images. Data often comes from disparate sources, so collecting data through APIs or manually using the collaboration app is the most efficient way to collect.
Having clean data is one of the most important aspects of creating a useful ML project, because your models are only as useful as the data you input. When an organization sends inaccurate data to systems, the systems produce incorrect predictions.
Data labeling- can be done through APIs or manually through Skyl’s collaborator application. Once the data has been collected, it is important to label each part of it accurately to ensure clean data. The collaborator app allows any person added to a project to label pictures or bodies of text.
This may include, but is not limited to, tagging a picture, identifying a name in a sentence, or summarizing the sentiment in a body of text.
Data visualization- Skyl makes it easy to view your data. This includes how many data points you have as well as how many are in each category. We have implemented a feature dedicated to viewing an entire dataset for a project and how each point has been labeled. It is also possible to view individual pieces of data. In this manner, you can ensure that your data is clean and accurately labeled, so that your model can output more accurate predictions.
Feature identification- based on the categories within your dataset, you can choose which features are best suited to train your model. This can be done in any number of different combinations, using the labeled data.
Model Training- Finally, after building a feature set using Neural network architecture (deep learning) you can train your model. Skyl allows you to choose from state-of-art neural network algorithms, tune hyper-parameters and see logs for your training in real time.
Model deployment- Getting models into production can be difficult. After a model is trained Skyl provides one-click option to deploy it to production and allow inference over it using Skyl Inference API which can be integrate within your application. Model inference can also be done both realtime and batch mode using Skyl Platform
Model monitoring — Once a model has made it into production, it must be monitored in order to ensure that everything is working properly. Monitoring each machine learning model requires attention coming from many different perspectives to ensure that each aspect of the model is running accurately and efficiently.
Skyl platform model monitoring allows easy monitoring of your models. This ensures that as your machines keep predicting, they stay accurate.
If you are interested in learning more about the ML workflow, creating an ML project, or seeing more examples of ML, please visit skyl.ai or follow our twitter @skyl_ai or follow us on Linkedin! We will keep you updated on the latest ML news, and other stuff
