Back to Blog

Machine Learning 101: 8 Stages of the Machine Learning Workflow

What is end to end machine learning workflow

Machine learning, which occurs when an AI tool has the ability to not only follow rules but also improve them, has the potential to help companies enhance the customer experience and boost revenue. In fact, for companies that want to stay competitive, a focus on customer experience is key. The vast majority of organizations (87 percent) say that providing an exceptional customer experience is very important to their business. Why is this? Perhaps because 75 percent of US consumers say that customer experience is an important factor in their purchasing decisions.

For example, by using machine learning with e-commerce, you can efficiently create a seamless customer experience for online shoppers with capabilities such as:

  • Intelligent product recommendations
  • Automated product categorization
  • Review moderation
  • Image tagging
  • Personalization

Although machine learning might seem like a futuristic idea, the reality is that the future is now, and it’s easier to implement than you might think. If you are curious about how to incorporate AI into your business, getting familiar with machine learning 101 is a great place to start.

Machine Learning 101: Workflow

skyl-ml-models

Machine learning is a cyclical, iterative process that follows a defined workflow that includes these eight steps:

Collect Data 

The Skyl platform is capable of collecting many forms of unstructured data, ranging from text to audio files to images. Data often comes from various data sources, so collecting data through APIs or manually using the collaboration app are the most efficient methods.

Data preparation is one of the most important aspects of creating a useful machine learning 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.

Label Data 

Data labeling can be done through APIs or manually through Skyl’s collaborator application. Once the data has been collected, it is important to annotate unlabeled data 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.

Visualize Data 

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.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

Identify Features

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.

Train Models 

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-the-art deep learning algorithms, facilitate hyperparameter tuning, and see logs for your training in real time.

Evaluate Models

After each round of training, you can evaluate the results to see how good the model is. To do this, you can test the model against data that it has not yet seen to determine how accurate the training model is. 

Deploy Models

Getting models into production can be difficult. After a model is trained, Skyl provides a one-click option to deploy it to production and allow inference over it using Skyl Inference API, which can integrate with your application. Model inference can also be done both in real time and via batch mode using Skyl Platform.

Monitor Models 

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 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.

Try Skyl.ai for Yourself

There are a number of ways you can improve the customer experience, increase revenue, or decrease time analyzing data. If this machine learning 101 article has inspired you to dig deeper, you can watch webinars about some of the ways it can be used for various applications. If you’re ready to take a deeper dive, request a demo or get a free trial version of Skyl.ai

how-to-ensure-that-your-machine-learning-project-is-successful

    

Comments