The number of SMBs and Enterprises using Machine Learning solutions is rapidly increasing. According to the recent survey by Algorithmia, around 45% of Enterprises have already become Machine Learning adopters and have their ML models in production.
How can you develop a Machine Learning model covering business tasks and how does the training process look like?
1. Business Understanding
The Machine Learning process starts with close collaboration with customer and stakeholders. Together we discuss and determine what business tasks you need to solve using Machine Learning algorithms and what kind of data we may need. It is important to understand whether ML is the best solution for a task and is there enough data to solve the challenge using ML algorithms. Remember that a well-defined task is the first step towards successful deployment.
2. Data Understanding
The second step involves the review of available data. Machine Learning model needs as much data to train as possible: you can never have too much. The more data we have, the more advanced ML model you can develop for your business. We will explore the data by visualizing trends, looking for patterns and so on. The availability of data can then accelerate the process of training: a model will identify meaningful patterns faster making more accurate future detections, predictions or whatever.
3. Data Preparation
When you are ready with enough amount of related data, you need to convert and clean data for modeling. Of course, more data allows you to have a better performance, but it needs to be clear and relevant. At this step, we will create datasets, find errors caused by data entry and exclude data that doesn’t bring any value. We prefer to keep this process iterative to develop a smarter algorithm providing quality results. It is possible to constantly increase the volume of data to improve your model and keep it up-to-date while training it for your business purposes.
4. Modeling and Training
The most challenging stage in the Machine Learning process is modeling and training. It is a key step that involves applying various models, providing them with testing datasets and selecting the most suitable parameters. We will use the prepared datasets to train ML model and track its learning progress. It should be an iterative process that increases performance and robustness every time and gets you closer to the final goal. By the way, iteration is a vital concept of Machine Learning development that helps to deploy better solutions.
5. Evaluation
When we have determined the optimal model that can cover your business needs, we can start the evaluation stage. The evaluation step allows us to understand whether the final model provides the desired output. We are more likely to use new and unseen data for testing then the training datasets. It helps us to provide reliable estimates and objective evaluation. Before tests, we can make predictions on performance metrics and then compare it with the evaluation results to better understand the effectiveness of the developed solution.
6. Deployment
When we have achieved the quality output provided by the model we can move on to the final step – implementation into your business processes. The model can be implemented as a single application or integrated with your system. The solution usually has a simple interface that allows you to easily navigate getting valuable insights for your business every day.
Exposit Machine Learning team uses the following process to deliver complex innovative solutions improving the specific business processes in Retail, Sport and other industries.
Implementation of Machine Learning can drive business
Implementation of Machine Learning can drive business value and open new opportunities for business development. Exposit specialists are ready to create a Machine Learning solution addressing your challenges and bringing competitive advantages to your business. Don’t miss a chance to discuss your custom ML solution with Kirill Lozovoi and learn more about our development process.
