In machine learning, experiment tracking stores all experiment metadata in a single location (database or a repository). Model hyperparameters, performance measurements, run logs, model artifacts, data artifacts, etc., are all included in this.
There are numerous approaches to implementing experiment logging. Spreadsheets are one option (no one uses them anymore! ), or you can use GitHub to keep track of tests.
Tracking machine learning experiments has always been a crucial step in ML development, but it used to be a labor-intensive, slow, and error-prone procedure.
The market for contemporary experiment management and tracking solutions for machine learning has developed and increased over the past few years. Now, there is a wide variety of options available. You’ll undoubtedly discover the appropriate tool, whether searching for an open-source or enterprise solution, a stand-alone experiment tracking framework, or an end-to-end platform.
Utilizing an open-source library or framework like MLFlow or purchasing an enterprise tool platform with these features like Weights & Biases, Comet, etc., are the simplest ways to perform experiment logging. This post lists some incredibly helpful experiment-tracking tools for data scientists.
The machine learning lifecycle, encompassing experimentation, reproducibility, deployment, and a central model registry, is managed by the open-source platform MLflow. It manages and distributes models from several machine learning libraries to various platforms for model serving and inference (MLflow Model Registry). MLflow presently supports Packaging ML code in a reusable, reproducible form so that it may be shared with other data scientists or transferred to production, as well as Tracking experiments to record and compare parameters and results (MLflow Tracking) (MLflow Projects). Additionally, it provides a central model store for collaboratively managing the whole lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations.
The MLOps platform for generating better models more quickly with experiment tracking, dataset versioning, and model management is called Weights & Biases. Weights & Biases can be installed on your private infrastructure or is available in the cloud.
Comet’s machine-learning platform interfaces with your current infrastructure and tools to manage, visualize, and optimize models. Simply add two lines of code to your script or notebook to automatically start tracking code, hyperparameters, and metrics.
Comet is a Platform for the Whole Lifecycle of ML Experiments. It can be used to compare code, hyperparameters, metrics, forecasts, dependencies, and system metrics to analyze differences in model performance. Your models may be registered on the model registry for easy handoffs to engineering, and you can keep an eye on them in use with a complete audit trail from training runs through deployment.
Arize AI is a machine learning observability platform that helps ML teams deliver and maintain more successful AI in production. Arize’s automated model monitoring and observability platform allows ML teams to detect issues when they emerge, troubleshoot why they happened, and manage model performance. By enabling teams to monitor embeddings of unstructured data for computer vision and natural language processing models, Arize also helps teams proactively identify what data to label next and troubleshoot issues in production. Users can sign up for a free account at Arize.com.
ML model-building metadata may be managed and recorded using the Neptune platform. It can be used to record Charts, Model hyperparameters, Model versions, Data versions, and much more.
You don’t need to set up Neptune because it is hosted in the cloud, and you can access your experiments whenever and wherever you are. You and your team can work together to organize all of your experiments in one location. Any investigation can be shared with and worked on by your teammates.
You must install “neptune-client” before you can use Neptune. Additionally, you must organize a project. You will utilize the Python API for Neptune in this project.
Sacred is a free tool for experimenting with machine learning. To begin utilizing Sacred, you must first design an experiment. If you’re using Jupyter Notebooks to conduct the experiment, you must pass “interactive=True.” ML model construction metadata may be managed and recorded using the tool.
Omniboard is Sacred’s web-based user interface. The program establishes a connection with Sacred’s MongoDB database. The measurements and logs gathered for each experiment are then shown. You must select an observer to see all the data that Sacred gathers. The default observer is called “MongoObserver.” The MongoDB database is connected, and a collection containing all of this data is created.
Users usually begin using TensorBoard because it is the graphical toolbox for TensorFlow. TensorBoard offers tools for visualizing and debugging machine learning models. The model graph can be inspected, embeddings can be projected to a lower-dimensional space, experiment metrics like loss and accuracy can be tracked, and much more.
Using TensorBoard.dev, you can upload and distribute the outcomes of your machine-learning experiments to everyone (collaboration features are missing in TensorBoard). TensorBoard is open-sourced and hosted locally, whereas TensorBoard.dev is a free service on a managed server.
Guild AI, a system for tracking machine learning experiments, is distributed under the Apache 2.0 open-source license. Analysis, visualization, diffing operations, pipeline automation, adjustment of the AutoML hyperparameters, scheduling, parallel processing, and remote training are all made possible by its features.
Guild AI also comes with several integrated tools for comparing experiments, such as:
- You may view spreadsheet-formatted runs complete with flags and scalar data with Guild Compare, a curses-based tool.
- The web-based program Guild View allows you to view runs and compare outcomes.
- A command that will enable you to reach two runs is called Guild Diff.
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, run orchestration, regulatory compliance, experiment tracking, and experiment optimization are just a few of its numerous features.
With Polyaxon, you can version-control code and data and automatically record significant model metrics, hyperparameters, visualizations, artifacts, and resources. To display the logged metadata later, you can use Polyaxon UI or combine it with another board, such as TensorBoard.
ClearML is an open-source platform with a collection of tools to streamline your machine-learning process, and it is supported by the Allegro AI team. Deployment, Data management, orchestration, ML pipeline management, and data processing are all included in the package. All of these characteristics are present in five ClearML modules:
- The experiment, model, and workflow data are stored on the ClearML Server, which also supports the Web UI experiment manager.
- integrating ClearML into your existing code base using a Python module;
- Scalable experimentation and process replication are made possible by the ClearML Data data management and versioning platform, which is built on top of object storage and file systems.
- Use a ClearML Session to launch remote instances of VSCode and Jupyter Notebooks.
With ClearML, you can integrate model training, hyperparameter optimization, storage options, plotting tools, and other frameworks and libraries.
Everything is automated using the MLOps platform Valohai, from model deployment to data extraction. Valohai “provides setup-free machine orchestration and MLFlow-like experiment tracking,” according to the tool’s creators. Despite not having experiment tracking as its main objective, this platform does offer certain capabilities, including version control, experiment comparison, model lineage, and traceability.
Valohai is compatible with a wide range of software and tools, as well as any language or framework. It can be set up with any cloud provider or on-premises. The program has many features to make it simpler and is also developed with teamwork in mind.
An open-source, enterprise-grade data science platform, Pachyderm, allows users to control the whole machine learning cycle. Options for scalability, experiment construction, tracking, and data ancestry.
There are three versions of the program available:
- Community-built, open-source Pachyderm was created and supported by a group of professionals.
- In the Enterprise Edition, a full version-controlled platform can be set up on the user’s preferred Kubernetes infrastructure.
- Pachyderm’s hosted, and managed version is called Hub Edition.
Kubeflow is the name of the machine learning toolkit for Kubernetes. Its goal is to utilize Kubernetes’ ability to simplify scaling machine learning models. Even though the platform has certain tracking tools, the project’s main goal differs. It consists of numerous components, such as:
- Kubeflow Pipelines is a platform for deploying scalable machine learning (ML) workflows and building based on Docker containers. The Kubeflow feature that is most frequently utilized is this one.
- The primary user interface for Kubeflow is Central Dashboard.
- A framework called KFServing is used to install and serve Kubeflow models, and a service called Notebook Servers is used to create and manage interactive Jupyter notebooks.
- For training ML models in Kubeflow through operators, see Training Operators (e.g., TensorFlow, PyTorch).
A platform for corporate MLOps is called Verta. The program was created to make the entire machine-learning lifecycle easier to manage. Its main characteristics may be summed up in four words: track, collaborate, deploy, and monitor. These functionalities are all 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 various types of metadata, explore and compare experiments, ensure model reproducibility, collaborate on ML projects and accomplish much more.
Verta supports several well-known ML frameworks, including TensorFlow, PyTorch, XGBoost, ONNX, and others. Open-source, SaaS, and enterprise versions of the service are all available.
Fiddler is a pioneer in enterprise Model Performance Management. Monitor, explain, analyze, and improve your ML models with Fiddler.
The unified environment provides a common language, centralized controls, and actionable insights to operationalize ML/AI with trust. It addresses the unique challenges of building in-house stable and secure MLOps systems at scale.
SageMaker Studio is one of the AWS platform’s components. It makes it possible for data scientists and developers to build, train, and use the best machine learning (ML) models. It is the first complete development environment for machine learning (IDE). It consists of four parts: prepare, construct, train and tune, deploy, and manage. The experiment tracking functionality is handled by the third train & tune. Users can automate hyperparameter tuning, debug training runs, log, compare experiments and organize.
The DVC suite of tools, driven by iterative.ai, includes DVC Studio. The DVC studio- a visual interface for ML projects- was created to help users keep track of tests, visualize them, and collaborate with the team. DVC was initially intended as an open-source version control system for machine learning. This component is still in use to enable data scientists to share and duplicate their ML models.
Prathamesh Ingle is a Mechanical Engineer and works as a Data Analyst. He is also an AI practitioner and certified Data Scientist with an interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real-life applications