One thing is getting good results from a single model-training run when working on a machine learning project. It’s another thing to keep your machine learning trials well-organized and to have a method for drawing reliable conclusions from them.
Experiment tracking provides the solution to these problems. Experiment tracking in machine learning is the practice of preserving all pertinent data for each experiment you conduct.
Experiment tracking is implemented by ML teams in a variety of ways, including using spreadsheets, GitHub, or in-house platforms. However, using tools made expressly for managing and tracking ML experiments is the most efficient choice.
Following are the top tools for ML experiment tracking and management
A machine learning framework called Weight & Biases was created for model management, dataset versioning, and experiment monitoring. The primary goal of the experiment tracking component is to assist data scientists in recording each step of the model-training process, visualizing models, and comparing trials.
W&B is a tool that may be used both on-premises and in the cloud. Weights & Biases supports a wide range of various frameworks and libraries in terms of integrations, including Keras, the PyTorch environment, TensorFlow, Fastai, Scikit-learn, and more.
Data scientists can track, compare, explain, and optimize experiments and models using the Comet ML platform across the model’s entire lifecycle, from training to production. For experiment tracking, data scientists can record datasets, code changes, experimentation histories, and models.
Comet is offered to teams, individuals, academic institutions, and corporations for everyone who wants to do experiments facilitate work, and quickly visualize results. It can be installed locally or used as a hosted platform.
Machine learning researchers can configure, arrange, log, and replicate experiments using the open-source program Sacred. Although Sacred lacks an exemplary user interface, you can link it to a few dashboarding tools like Omniboard (but you can also use others, such as Sacredboard or Neptune, via integration).
Although Sacred lacks the other tools’ scalability and hasn’t been designed for team collaboration (except when combined with another tool), it has a lot of possibilities for solo investigation.
An open-source framework called MLflow aids in managing the entire machine learning lifecycle. This covers experimentation and the storage, duplication, and use of models. Tracking, Model Registry, Projects, and Models are the four components of MLflow that each stand in for one of these factors.
The MLflow Tracking component has an API and UI that enable different logging metadata (such as parameters, code versions, metrics, and output files) and afterward viewing the outcomes.
Since TensorBoard is the graphical toolkit for TensorFlow, users frequently start with it. Machine learning model visualization and debugging tools are available through TensorBoard. Users can examine the model graph, project embeddings to a lower-dimensional space, track experiment metrics like loss and accuracy, and much more.
You may upload and share the results of your machine learning experiments with anyone using TensorBoard.dev (collaboration features are missing in TensorBoard). While TensorBoard.dev is offered as a free service on a managed server, TensorBoard is open-sourced and hosted locally.
The Apache 2.0 open source license covers Guild AI, a machine learning experiment tracking system. It enables analysis, visualization, diffing operations, pipeline automation, AutoML hyperparameter tuning, scheduling, parallel processing, and remote training.
Several integrated tools for comparing experiments are also included with Guild AI, including:
- Guild Compare, a curses-based program that allows you to view spreadsheet-formatted runs complete with flags and scalar data,
- Guild View, an online application that allows you to compare outcomes and view runs,
- Using the Guild Diff command, you can contrast two runs.
A platform for scalable and reproducible deep learning and machine learning applications is called Polyaxon. It has many functions, including model management, run orchestration, regulatory compliance, and tracking and optimizing experiments. The primary objective of its creators is to maximize output and productivity while minimizing expenses.
You can automatically record important model metrics, hyperparameters, visualizations, artifacts, and resources with Polyaxon, and you can also version control code and data. You can utilize Polyaxon UI or incorporate it with another board, such as TensorBoard, to display the logged metadata later. You can choose to deploy Polyaxon on-premises or with a particular cloud service provider. Major ML and DL libraries like TensorFlow, Keras, or Scikit-learn are also supported.
The team behind Allegro AI supports ClearML, an open-source platform with a collection of tools to simplify your machine learning process. The package comprises data management, orchestration, deployment, ML pipeline management, and data processing. Five modules of ClearML exhibit all of these features:
- Python package for ClearML integration into your current code base;
- storing experiment, model, and workflow data on the ClearML Server, which also supports the Web UI experiment manager;
- ML-Ops orchestration agent ClearML Agent, which enables scalable experiment and workflow reproducibility;
- a data management and versioning platform built on top of file systems and object storage called ClearML Data;
- Launch remote instances of VSCode and Jupyter Notebooks using a ClearML Session.
Model training, hyperparameter optimization, charting tools, storage solutions, and other frameworks and libraries are all integrated with ClearML.
The MLOps platform Valohai automates everything, from model deployment to data extraction. According to the developers of this tool, Valohai “provides setup-free machine orchestration and MLFlow-like experiment tracking.” Although this platform does not have experiment tracking as its primary focus, it does offer specific capabilities, including experiment comparison, version control, model lineage, and traceability.
Any language or framework, as well as a wide range of programs and tools, are compatible with Valohai. It can be set up either on-premises or with any cloud provider. The program is also designed with teamwork and has numerous features to make it easier.
Pachyderm is an open-source, enterprise-grade data pipeline platform that enables users to manage a full machine learning cycle. scalability choices, experiment building, tracking, and data lineage.
There are three versions of the software available:
- Community — a free and open-source Pachyderm version created and supported by a group of professionals;
- In the Enterprise Edition, a complete version-controlled platform can be installed on the Kubernetes infrastructure of the user’s choice.
The machine learning toolbox for Kubernetes is called Kubeflow. Its objective is to use Kubernetes’ capacity to simplify scaling machine learning models. Although the platform offers certain tracking features, they are not the project’s primary objective. There are several parts to it, including:
- A framework for creating and deploying scalable machine learning (ML) workflows based on Docker containers is called Kubeflow Pipelines. It’s likely the Kubeflow feature that gets used the most;
- Central Dashboard is Kubeflow’s main user interface (UI);
- KFServing is a toolkit for deploying and serving Kubeflow models, and Notebook Servers is a service for building and administering interactive Jupyter notebooks.
- For the ML models in Kubeflow through operators, train the operators (e.g., PyTorch, TensorFlow).
Verta is a platform for business MLOps. The software was developed to make managing the complete machine learning lifecycle easier. Four words encapsulate its key features: track, collaborate, deploy, and monitor. Verta’s primary products, Experiment Management, Model Registry, Model Deployment, and Model Monitoring, all incorporate these features.
You may monitor and visualize machine learning experiments, record different types of metadata, browse and compare experiments, assure model reproducibility, work together on ML projects as a team, and do much more with the Experiment Management component.
TensorFlow, PyTorch, XGBoost, ONNX, and other well-known ML frameworks are among those supported by Verta. It is accessible as an open-source, SaaS, and enterprise service.
One component of the AWS platform is SageMaker Studio. It enables data scientists and developers to create, construct, train, and deploy superior machine learning (ML) models. It calls itself the first ML-specific integrated development environment (IDE). Its four parts are preparing, training, tuning, deploying, and managing. The third one, train & tune, takes care of the experiment tracking functionality. Users may automate hyperparameter tuning, debug training runs, log, organize, and compare experiments.
DVC Studio is a member of the iterative. Ai-powered DVC family of tools. DVC was initially designed as a machine learning-specific open-source version control system. This component is still in place to allow data scientists to share and replicate their ML models. The DVC studio, a visual interface for ML projects, was developed to assist users in tracking experiments, visualizing them, and working on them with the team.
The DVC Studio application is available both online and locally.
Use Deepkit.ai, an open-source machine learning development tool and training suite for intelligent, quick, and reproducible modern machine learning. You may manage computing servers, log your trials, and debug your models with Deepkit.ai.
Experiment Management Model Debugging Computation Management: Deepkit.ai’s main benefits
The production-grade deep learning models are tracked and managed through the open-source platform known as Trains. By just a few lines of code, any research team in the model development stage can set up and keep insightful entries on their on-premises Trains server.
Any DL/ML workflow is effortlessly integrated with Trains. It automatically archives jupyter notebooks into Python code and links experiments with training code (git commit + local diff + Python package versions).
Using the strength of Git (Source code Versioning) and DVC, the open-source data science and machine learning collaboration platform DagsHub enables you to easily construct, grow, and deploy machine learning projects (Data Version Control).
DAGsHub makes it simple to construct, distribute, and reuse machine learning and data science projects, saving data teams the time and effort of starting over each time. The following characteristics of DAGsHub set it apart from other conventional platforms:
The ability to link everything in one location with no configuration is provided by built-in remotes for programs like Git (for source code management), DVC (for data version tracking), and MLflow (for experiment tracking).
DAGsHub offers you the convenience of a lovely user experience while allowing you to track and monitor the various ML experiments carried out by numerous folks. An ML project’s trials can all be monitored and connected to the particular version of its models, code, and data!
In addition to keeping track of your experiments, DAGsHub’s intuitive visualizations and the recorded data for each experiment allow you to compare various trials side by side and comprehend the variations in performance metrics and hyperparameters.
Note: We tried our best to feature the Cool Tools, but if we missed anything, then please feel free to reach out at Asif@marktechpost.com
Prathamesh Ingle is a Consulting Content Writer at MarktechPost. He is a Mechanical Engineer and working as a Data Analyst. He is also an AI practitioner and certified Data Scientist with interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real life applications