Training Machine Learning models for your projects
Machine Learning models are a powerful tool that can help your business utilize all the capabilities of your cloud infrastructure in order to minimize the expenses on your IT operations and maximize the positive user experience from interacting with your products or services. The challenges here involve selecting the best ML model for your particular project, building a highly-scalable infrastructure for it to run on, training the model correctly and running it in production cost-efficiently. As you can see, all of these are no easy tasks and require some decent ML & AI expertise in order to be done right.
From Amazon Sagemaker to IBM Doctor Watson, there are various cloud-based Machine Learning systems and platforms. They have detailed knowledgebases and configuration guides, so everyone can launch hem for their products. The problem is, no project is the same, and while the general principles still apply, every ML & AI solution must be configured from scratch to run correctly. The approximate workflow of ML model operations is as follows:
- Preliminary data testing and validation. The only way to enable any ML model to deliver the results you want is to train it on huge data sets involving historical data. Then the model will be able to determine the normal patterns you need it to identify and will be ready to be deployed in production. However, these data sets must be prepared correctly, which involves some diligent data testing.
The data within the sets must be checked for completeness and consistency, must be deduplicated and sorted by timestamp to remove the unneeded repeats, must be normalized to ensure uniformity, etc. As a result, the data will be prepared for the transformation stage of the ETL process. - Apache Hadoop processing. Map-reduce feature from Apache Hadoop is one of the most powerful tools in the inventory of any data scientist. The data within the set is mapped — marked for processing by Hadoop nodes — and reduced — the result becomes available after processing. This allows getting the data ready for visualization out of the mounds of semi-structured and unstructured data — but it requires an error-proof configuration of the Hadoop cluster. While this can be done theoretically, it is something rather fantastic to get sorted out correctly from the start, as the Hadoop cluster configuration takes 10 pages and 4 hours of time to complete manually.
- Output visualization. Once the output is processed, the data scientist must ensure the results are coherent, relevant to the project goals and correspond to the information within the HDFS.
Obviously, Natural Language Processing workflow will be quite different from Optical Character Recognition training and will use different tools, even if it uses the same logic. Thus said, it is important to understand which model is best suited for delivering the project results you expect and how to implement this model most cost-efficiently.
IT Svit has obtained in-depth knowledge of the best practices of ML & AI operations. We can help your business select the most fitting ML model for your project, train it and run it in production. If this is what you currently need — contact the IT Svit team, we would be glad to assist!
