Machine Learning Workflows

Phases of ML worflow

Machine learning workflows outline the necessary processes for a specific machine learning implementation. These workflow steps can be grouped into four core packages:

1. Data Collection for Machine Learning:
One of the foundational steps in a machine learning project workflow is data collection. The depth and breadth of data gathered dictate the potential applications and reliability of your project. Collecting data starts with determining your sources, after which you amalgamate information from these sources into a cohesive dataset. This could involve broadcasting data from IoT sensors, harnessing open-source datasets, or creating a data lake from varied media or files.

2. Pre-processing of Data:
Post data acquisition, the next step is data pre-processing. This phase revolves around cleaning, validating, and transforming raw data into a usable format. While data from a single source might simplify this process, pulling data from multiple sources necessitates ensuring consistency in data types, accuracy, and the removal of potential duplicates.

3. Creating Datasets:
Here, the processed data is segmented into three distinct datasets:

• Training: This dataset trains the algorithm, teaching it the nuances of data analysis. The parameters in this subset dictate the model's classifications.
• Validating: This set gauges the model’s accuracy. Fine-tuning of model parameters occurs using this dataset.
• Testing: Aimed at appraising the model's overall performance, this dataset is engineered to identify potential system flaws.

4. Refinement and Training:
With the datasets in place, the model training commences. This process involves feeding the training data to your system. Post-training, the validation dataset plays a pivotal role in refining the model. This could mean tweaking or eliminating certain variables and adjusting hyperparameters to reach the desired accuracy level.

Evaluation of Machine Learning

After identifying a fitting set of hyperparameters and fine-tuning the model's accuracy, the testing phase is embarked upon. The test dataset ensures that the models utilize relevant features in their computations. Depending on the feedback, there's room to revisit model training for accuracy augmentation, tweak output parameters, or roll out the model.

Drawbacks of ML Workflows

Machine learning workflows, with their multifaceted steps, harbor complexities and uncertainties. Managing these workflows brings forth challenges such as:

• Data Cleanliness:
  Dirty data marked by incorrect or missing fields necessitates additional cleaning processes to mold the data to fit the ML workflow's format.
• Quality and Availability of Ground-Truth Data:
  Given that ML models largely hinge on predictions using input data, the ground-truth data, pivotal for training and model performance evaluation, must be impeccable. High-quality ground-truth data ensures the ML model can make reliable predictions in a real-world setting. However, annotating this data can be resource-intensive and expensive, especially for intricate tech tasks.
• Concept Drift:
  Predictive models often operate under the assumption that relationships between input and output variables are static over time. However, with many models built on historical data, they might not account for shifts in these underlying dynamics. Such changes can skew predictions, prompting the need to retrain the model with recent data to capture the evolving dynamics.
• Tracking Learning Time:
  The time taken to train a model iteration determines how many trials can be run with varied model versions. It's pivotal to monitor both the model's accuracy and the training duration for each model configuration. This aids in balancing the trade-offs between training time and model accuracy.