Machine Learning Workflows

Phases of ML Workflows

Machine learning workflows specify the processes that must be taken throughout a certain machine learning implementation. Machine learning workflow steps come in four core packages.

  • Data collection for machine learning- One of the most crucial steps in machine learning project workflow is data collection. With the level of information you acquire during data gathering, you define the promising application and reliability of your project.

To gather data, you must first establish your sources and then combine the information from those systems into a single dataset. This might include broadcasting data from IoT sensors, obtaining open-source information sets, or building a data lake out of various media or files.

  • Pre-processing of data– After gathering your data, you must pre-process it. Cleaning, validating, and converting data into a useful dataset is what pre-processing entails. If you collect information from one provider, this may be a pretty simple procedure. But, if you are collecting data from many sources, you must ensure that the data types match, that the data is similarly accurate, and that any potential duplicates are removed.
  • Creating datasets– During this step, processed data is divided into three datasets:
  1. Training– is used to train the algorithm and teach it how to analyze data. The parameters in this collection define model classifications.
  2. Validating– used to measure the model’s accuracy. The model parameters are fine-tuned using this dataset.
  3. Testing– used to evaluate the models’ performance This collection is intended to highlight any flaws in the system.
  • Refinement and training- Once you have your datasets, you may begin training your model. This entails providing your training data to your system. After training, you may use your validation dataset to enhance the model. This may entail changing or removing variables, as well as fine-tuning hyperparameters until an appropriate degree of accuracy is achieved.
  • Evaluation of machine learning- Finally, once an appropriate collection of hyperparameters has been identified and your model’s accuracy has been tuned, you may test your model. Machine learning workflow management uses your test dataset to ensure that your models employ accurate features. You may revisit model training to increase accuracy, modify output parameters, or launch the model as necessary in response to feedback.
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Drawbacks of ML Workflows

Because machine learning process flow entails a variety of complexity and uncertainties at each stage of the process, controlling them presents new problems, such as:

  • Data cleanliness: Additional data-cleaning processes are necessary for dirty data with incorrect or missing fields in order to turn the information into the format specified for the ML workflow.
  • Ground-truth data availability and quality for model evaluation: Because ML models are often taught to forecast tags using input data, the ground-truth info used to train and assess ML model performance must be of high quality. This is done in order for the learned ML model can reliably predict labels in production. Labeling ground-truth data, on the other hand, can be time-consuming and costly, especially for more complicated technological activities.
  • Concept drift: Predictive models often presume that the connection among input and output variables stays unchanged across time. Because many ML models are constructed on past data, it doesn’t compensate for any changes in the data’s underlying connections. These modifications may result in forecasts that no more accurately represent the statistical features of the output variable in the manufacturing environment, necessitating the need to retrain the Ml algorithm on more current historical data to account for changes in the data’s dynamics.

Tracking learning time: The number of trials you may run with different versions of the ML model is determined by the time it takes to educate one iteration of the ML model on a dataset. It is critical to track the accuracy of the model and training runtime for each model architecture, hyperparameter, and sample size combination so that you may use the findings to evaluate the compromises between time and model accuracy while training ML models.