In machine learning, experiment tracking stores all experiment metadata in one place (database or repository). This includes all model hyperparameters, performance measurements, execution logs, model artifacts, data artifacts, etc.
There are various ways to implement logging for your experiments. A spreadsheet is one option (no one uses it anymore!). Alternatively, you can use GitHub to track your tests.
Tracking machine learning experiments has always been a critical step in ML development, but it used to be a tedious, time-consuming, and error-prone procedure.
The market for modern experiment management and tracking solutions for machine learning has developed and expanded over the last few years. There are many different options available today. Whether you’re looking for an open source or enterprise solution, a standalone experiment tracking framework, or an end-to-end platform, you’ll definitely find the right tools.
Leveraging open-source libraries or frameworks such as MLFlow, or purchasing enterprise tool platforms with features such as Weights & Biases, Comet, etc., are the easiest ways to run experiment logs. In this post, I will introduce some experiment tracking tools that are very useful for data scientists.
ML Flow
The machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry, is managed by the open-source platform MLflow. Manage and distribute models from multiple machine learning libraries to various platforms for model serving and inference (MLflow model registry). MLflow now supports packaging your ML code in a reusable and reproducible format so you can share it with other data scientists or transfer it to production. It also supports experiment tracing (MLflow tracing) (MLflow project) for recording and comparing parameters and results. Additionally, it provides a central model store for collaborative management of the entire MLflow model lifecycle, including model versioning, stage transitions, and annotations.
weights and biases
The MLOps platform for generating better models faster with experiment tracking, dataset versioning, and model management is called Weights & Biases. Weights & Biases can be installed on your private infrastructure or available in the cloud.
Comet
Comet’s machine learning platform works with your current infrastructure and tools to manage, visualize, and optimize your models. Just add two lines of code to your script or notebook and it will automatically start tracking your code, hyperparameters and metrics.
Comet is the platform for the entire lifecycle of ML Experiments. It can be used to compare code, hyperparameters, metrics, predictions, dependencies, and system metrics to analyze differences in model performance. Register your model in the model registry for easy handoff to engineering. You can also monitor your model in use with a complete audit trail from training run to deployment.
Neptune AI
ML model building metadata can be managed and recorded using the Neptune platform. Can be used to record charts, model hyperparameters, model versions, data versions, etc.
Neptune is hosted in the cloud, so you can access your experiments anytime, anywhere with no setup required. You and your team can work together to organize all your experiments in one place. Any investigation can be shared and worked on with teammates.
Before using Neptune, you need to install ‘neptune-client’. Additionally, you need to organize your project. This project makes use of the Python API for Neptune.
sacred
Sacred is a free tool for experimenting with machine learning. To get started with Sacred, you first need to design your experiment. If you are experimenting with Jupyter Notebook, you should pass ‘interactive=True’. ML model building metadata can be managed and recorded using tools.
omni board
Omniboard is Sacred’s web-based user interface. The program establishes a connection with Sacred’s MongoDB database. The measurements and logs collected in each experiment are then displayed. To see all the data that Sacred collects, you must select an observer. The default observer is called “MongoObserver”. A MongoDB database is connected and a collection is created containing all this data.
TensorBoard
Users typically start with TensorBoard because it is TensorFlow’s graphical toolbox. TensorBoard provides tools for visualizing and debugging machine learning models. You can inspect model graphs, project embeddings into a low-dimensional space, and track experimental metrics such as loss and accuracy.
TensorBoard.dev allows you to upload the results of your machine learning experiments and distribute them to everyone (TensorBoard does not have collaboration features). TensorBoard is open source and locally hosted, while TensorBoard.dev is a free service on a managed server.
Guild AI
Guild AI, a system for tracking machine learning experiments, is distributed under the Apache 2.0 open source license. Analysis, visualization, delta operations, pipeline automation, AutoML hyperparameter tuning, scheduling, parallelism, and remote training are all enabled by its capabilities.
Guild AI also comes with several integrated tools for comparing experiments such as:
- You can use Guild Compare, a curses-based tool, to view a spreadsheet-style run with flags and scalar data.
- Web-based program Guild View allows you to view runs and compare results.
- It’s a command that allows two runs, called guild diff.
Polyaxon
Polyaxon is a platform for scalable and repeatable machine learning and deep learning applications. The main goal of its designers is to reduce costs while increasing output and productivity. Model management, orchestration execution, regulatory compliance, experiment tracking, and experiment optimization are just a few of its many capabilities.
With Polyaxon, you can version your code and data and automatically record important model metrics, hyperparameters, visualizations, artifacts, and resources. To view the logged metadata later, you can use the Polyaxon UI or combine it with another board such as TensorBoard.
ClearML
ClearML is an open-source platform with a collection of tools that streamline the machine learning process, backed by the Allegro AI team. Deployment, data management, orchestration, ML pipeline management, and data processing are all included in the package. All of these properties are included in his 5 ClearML modules:
- Experiments, models, and workflow data are stored on the ClearML server, which also supports the Web UI Experiment Manager.
- Integrate ClearML into your existing code base using Python modules.
- Scalable experimentation and process replication are made possible by the ClearML Data data management and versioning platform built on top of object storage and file systems.
- Launch a remote instance of VSCode and Jupyter Notebook using a ClearML session.
With ClearML, you can integrate model training, hyperparameter optimization, storage options, plotting tools, and other frameworks and libraries.
Valohai
From model deployment to data extraction, everything is automated using the MLOps platform Valohai. According to the tool’s creator, Valohai “provides zero-setup machine orchestration and MLFlow-like experiment tracking.” Although it does not have experiment tracking as its primary purpose, the platform offers certain features such as version control, experiment comparison, model lineage, and traceability.
Valohai is compatible with a wide variety of software and tools, and any language or framework. Can be set up on any cloud provider or on-premises. The program has many features to make it simpler and is developed with teamwork in mind.
pachyderm
Pachyderm, an open-source, enterprise-grade data science platform, puts users in control of the entire machine learning cycle. Options for scalability, experiment building, tracking, and data ancestry.
There are three versions of the program available.
- Community-built, open-source Pachyderm is created and supported by a group of experts.
- Enterprise Edition allows you to set up a complete, versioned platform on your preferred Kubernetes infrastructure.
- The hosted and managed version of Pachyderm is called the Hub edition.
cube flow
Kubeflow is the name of a machine learning toolkit for Kubernetes. The goal is to take advantage of his Kubernetes capabilities that simplify scaling machine learning models. Although the platforms have specific tracking tools, the main goals of the projects are different. It consists of many components such as:
- Kubeflow Pipelines is a platform for deploying scalable machine learning (ML) workflows and building on Docker containers. The most frequently used Kubeflow feature is this.
- Kubeflow’s primary user interface is the Central Dashboard.
- It uses a framework called KFServing to install and serve Kubeflow models, and a service called Notebook Servers to create and manage interactive Jupyter notebooks.
- For training ML models in Kubeflow with operators, see Training Operators (TensorFlow, PyTorch, etc.).
Verta.ai
The corporate MLOps platform is called Verta. This program was created to make the entire machine learning lifecycle easier to manage. Its main characteristics can be summed up in four words: tracking, collaboration, deployment and monitoring. All of these capabilities are included in Verta’s core products: Experiment Management, Model Deployment, Model Registry, and Model Monitoring.
With the Experiment Management component, you can monitor and visualize machine learning experiments, record different types of metadata, explore and compare experiments, ensure model reproducibility, collaborate on ML projects, and much more. It can be achieved.
Verta supports several popular ML frameworks such as TensorFlow, PyTorch, XGBoost and ONNX. Open source, SaaS, and enterprise versions of this service are all available.
Fiddler is a pioneer in enterprise model performance management. Monitor, explain, analyze, and improve your ML models using Fiddler.
A unified environment provides a common language, centralized control, and actionable insights to operate ML/AI with confidence. Address the unique challenges of building stable and secure MLOps systems in-house at scale.
SageMaker Studio
SageMaker Studio is one of the components of the AWS platform. It empowers data scientists and developers to build, train, and use the best machine learning (ML) models. This is the first complete development environment for machine learning (IDE). It consists of four parts: Prepare, Build, Train and Tune, Deploy, and Manage. Experiment tracking functionality is handled by the third train & tune. Users can automate hyperparameter tuning, debug training runs, log, compare and organize experiments.
DVC studio
The DVC tool suite, powered by iterative.ai, includes DVC Studio. DVC Studio, a visual interface for ML projects, was created to help users track, visualize, and collaborate with their teams on their tests. DVC was originally intended as an open source version control system for machine learning. This component is still used today to allow data scientists to share and replicate their ML models.
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Prathamesh Ingle is a Consulting Content Writer at MarktechPost. He is a mechanical engineer and works as a data analyst. He is his AI practitioner and also a certified data scientist with an interest in AI applications.He is passionate about exploring new technologies and advancements in real-world applications
