ML experiment tracking: what you need to know and how to get started
- Edwin Kuss
- 5 min
Embarking on developing machine learning models unveils a dynamic landscape of numerous experiments. These experiments, characterized by variations in models, hyperparameters, training or evaluation data, and even subtle code modifications, create a tapestry of diverse outcomes. Picture running the same code in different environments, each with its own PyTorch or Tensorflow version, further contributing to the array of experiment results. The challenge arises as these experiments yield distinct evaluation metrics, swiftly complicating the task of keeping track of essential information. Mainly when the goal is to organize, compare, and confidently select the most promising models for production. During this complexity, experiment tracking emerges as a beacon of order and structure, providing a crucial framework to navigate and glean insights from the multitude of experiments that shape the evolution of machine learning models.
Understanding experiment tracking in machine learning
What is experiment tracking?
Experiment tracking systematically records all relevant information associated with each machine-learning experiment. The specific necessary details may vary based on the project’s unique requirements.
Critical components of experiment metadata:
Scripts and execution: Scripts employed in the experiment’s execution.
Environment configuration: Files specifying the configuration of the environment.
Data details: Training and evaluation data, such as dataset statistics and versions.
Model configurations: Configurations for the model and training parameters.
Evaluation metrics: Metrics used to evaluate the machine learning model’s performance.
Model artifacts: Model weights and any other relevant artifacts.
Performance visualizations: Visual representations like confusion matrices or ROC curves.
Example predictions: Sample predictions are particularly applicable in computer vision on validation sets.
Importance of real-time visibility: Having real-time access to certain aspects of the experiment during its execution is crucial.
Early recognition of inefficacy: Identifying early on if an experiment is unlikely to yield improved results.
Efficient resource utilization: Stopping experiments early saves resources compared to letting them run for days or weeks.
Facilitating experiment iteration: Enabling the prompt exploration of alternative approaches.
Components of an experiment tracking system:
To effectively manage experiment-related data, a robust tracking system typically consists of the following key components:
Experiment database:
A repository where all logged experiment metadata is stored for future querying.
Client library:
A collection of methods enabling seamless logging of metadata from training scripts and querying the experiment database.
Experiment dashboard:
A visual interface providing a user-friendly experience for accessing and reviewing experiment metadata.
Flexibility in implementation:
While specific implementations may vary, the general structure of these components remains consistent, ensuring a standardized approach to experiment tracking.
Navigating the ML project lifecycle
MLOps overview
MLOps seamlessly manages the entire life cycle of a machine learning (ML) project. It involves tasks ranging from coordinating distributed training to overseeing model deployment and monitoring model performance in production, with periodic re-training as needed.
The role of experiment tracking in MLOps
Experiment tracking, also known as experiment logging, is a critical component within MLOps. It specifically focuses on supporting the iterative phase of ML model development. This iterative phase explores diverse strategies to enhance the model’s performance. Experiment tracking is intricately connected with other MLOps aspects, including data and model versioning.
If, for example, we look at this tool in the Kiroframe product, then you can see that experiment tracking is built to make this process easier for engineering teams. Instead of manually keeping notes or juggling spreadsheets, every run is automatically logged with its datasets, parameters, metrics, and artifacts. This means results are not only reproducible but also easy to compare side by side through shared leaderboards. Teams can quickly see what worked, what didn’t, and why. By integrating experiment tracking with dataset management, artifact versioning, and profiling, Kiroframe enables teams to move faster, stay aligned, and make better decisions when models are ready for production.
Importance of experiment tracking
Experiment tracking proves its value even when ML models do not transition to production, as in research-focused projects. The comprehensive recording of metadata for each experiment becomes indispensable for later analysis. In today’s ML landscape, where projects involve larger datasets, more complex models, and distributed teams, careful experiment tracking has become a cornerstone of reliable research and collaboration. Without it, valuable insights are easily lost, and repeating experiments can quickly become costly and time-consuming.
Why ML experiment tracking matters
Structured approach to model development
With its structured approach, ML experiment tracking empowers data scientists to identify factors influencing model performance, compare results, and ultimately select the optimal model version.
The iterative nature of model development
The development of an ML model typically involves the following:
- Collecting and preparing training data.
- Selecting a model.
- Training it with the organized data.
Small changes in components like training data, model hyperparameters, model type, or experiment code can significantly alter model performance. Data scientists often run multiple versions of the model, making achieving the best-performing model an iterative process. Systematically tracking experiments during model development makes comparing and reproducing results from different iterations easier.
Implementing experiment tracking: Overcoming manual challenges
Effectively implementing experiment tracking requires addressing the limitations of manually recording experiment details in spreadsheets, particularly in machine learning projects with numerous and complex variables. Although manual tracking may suffice for a limited number of experiments, scalability becomes a concern when dealing with intricate variable relationships.
Fortunately, specialized tools designed for machine learning experiment tracking offer comprehensive solutions to these challenges. These tools serve as centralized hubs, providing dedicated spaces to store various ML projects and their corresponding experiments. They seamlessly integrate with different model training frameworks, automating capturing and logging all essential experiment information. Additionally, these tools feature user-friendly interfaces that facilitate the search and comparison of experiments. Incorporating visualizations further aids in the quick interpretation of results and effective communication, particularly with stakeholders without a technical background. Moreover, these tools enable the tracking of hardware consumption for different experiments.
Best practices for ML experiment tracking: a structured approach
Establishing best practices for ML experiment tracking is imperative for maximizing effectiveness. This approach involves defining the experiment’s objective, evaluation metrics (such as accuracy or explainability), and experiment variables, including different models and hyperparameters. For example, if the goal is to enhance model accuracy, specifying accuracy metrics and formulating hypotheses, such as comparing the performance of model X to model Y, becomes crucial. A structured approach ensures that experimentation is purposeful, preventing unguided trial and error, and facilitates the identification of successful experiments based on predefined criteria.
In Kiroframe, an MLOps platform, this structured approach is built into the workflow. Each run is automatically logged with its parameters, datasets, metrics, and artifacts, making comparisons straightforward and reproducible. Leaderboards allow teams to benchmark different models side by side, while metadata tracking ensures that objectives and criteria are clearly documented. By combining these capabilities, Kiroframe transforms best practices into everyday workflows, helping teams experiment more systematically and move faster from hypothesis to validated results → Try it out in Kiroframe demo