Best ML Experiment Tracking Tools in South America

Vertex AI's ML Experiment Tracking empowers organizations to monitor and oversee their machine learning experiments, promoting clarity and reproducibility. This capability allows data scientists to document model settings, training variables, and outcomes, facilitating the comparison of various experiments to identify the most effective models. By systematically tracking experiments, businesses can enhance their machine learning processes and minimize the likelihood of mistakes. New users are welcomed with $300 in complimentary credits to delve into the experiment tracking functionalities, enhancing their model development efforts. This tool is essential for collaborative teams aiming to refine models and maintain uniform performance across different versions.

TensorFlow is a comprehensive open-source machine learning platform that covers the entire process from development to deployment. This platform boasts a rich and adaptable ecosystem featuring various tools, libraries, and community resources, empowering researchers to advance the field of machine learning while allowing developers to create and implement ML-powered applications with ease. With intuitive high-level APIs like Keras and support for eager execution, users can effortlessly build and refine ML models, facilitating quick iterations and simplifying debugging. The flexibility of TensorFlow allows for seamless training and deployment of models across various environments, whether in the cloud, on-premises, within browsers, or directly on devices, regardless of the programming language utilized. Its straightforward and versatile architecture supports the transformation of innovative ideas into practical code, enabling the development of cutting-edge models that can be published swiftly. Overall, TensorFlow provides a powerful framework that encourages experimentation and accelerates the machine learning process.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

Amazon SageMaker is a comprehensive service that empowers developers and data scientists to efficiently create, train, and deploy machine learning (ML) models with ease. By alleviating the burdens associated with the various stages of ML processes, SageMaker simplifies the journey towards producing high-quality models. In contrast, conventional ML development tends to be a complicated, costly, and iterative undertaking, often compounded by the lack of integrated tools that support the entire machine learning pipeline. As a result, practitioners are forced to piece together disparate tools and workflows, leading to potential errors and wasted time. Amazon SageMaker addresses this issue by offering an all-in-one toolkit that encompasses every necessary component for machine learning, enabling quicker production times while significantly reducing effort and expenses. Additionally, Amazon SageMaker Studio serves as a unified, web-based visual platform that facilitates all aspects of ML development, granting users comprehensive access, control, and insight into every required procedure. This streamlined approach not only enhances productivity but also fosters innovation within the field of machine learning.

Neptune.ai serves as a robust platform for machine learning operations (MLOps), aimed at simplifying the management of experiment tracking, organization, and sharing within the model-building process. It offers a thorough environment for data scientists and machine learning engineers to log data, visualize outcomes, and compare various model training sessions, datasets, hyperparameters, and performance metrics in real-time. Seamlessly integrating with widely-used machine learning libraries, Neptune.ai allows teams to effectively oversee both their research and production processes. Its features promote collaboration, version control, and reproducibility of experiments, ultimately boosting productivity and ensuring that machine learning initiatives are transparent and thoroughly documented throughout their entire lifecycle. This platform not only enhances team efficiency but also provides a structured approach to managing complex machine learning workflows.

Manage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders.

TensorBoard serves as a robust visualization platform within TensorFlow, specifically crafted to aid in the experimentation process of machine learning. It allows users to monitor and illustrate various metrics, such as loss and accuracy, while also offering insights into the model architecture through visual representations of its operations and layers. Users can observe the evolution of weights, biases, and other tensors via histograms over time, and it also allows for the projection of embeddings into a more manageable lower-dimensional space, along with the capability to display various forms of data, including images, text, and audio. Beyond these visualization features, TensorBoard includes profiling tools that help streamline and enhance the performance of TensorFlow applications. Collectively, these functionalities equip practitioners with essential tools for understanding, troubleshooting, and refining their TensorFlow projects, ultimately improving the efficiency of the machine learning process. In the realm of machine learning, accurate measurement is crucial for enhancement, and TensorBoard fulfills this need by supplying the necessary metrics and visual insights throughout the workflow. This platform not only tracks various experimental metrics but also facilitates the visualization of complex model structures and the dimensionality reduction of embeddings, reinforcing its importance in the machine learning toolkit.

Keepsake is a Python library that is open-source and specifically designed for managing version control in machine learning experiments and models. It allows users to automatically monitor various aspects such as code, hyperparameters, training datasets, model weights, performance metrics, and Python dependencies, ensuring comprehensive documentation and reproducibility of the entire machine learning process. By requiring only minimal code changes, Keepsake easily integrates into existing workflows, permitting users to maintain their usual training routines while it automatically archives code and model weights to storage solutions like Amazon S3 or Google Cloud Storage. This capability simplifies the process of retrieving code and weights from previous checkpoints, which is beneficial for re-training or deploying models. Furthermore, Keepsake is compatible with a range of machine learning frameworks, including TensorFlow, PyTorch, scikit-learn, and XGBoost, enabling efficient saving of files and dictionaries. In addition to these features, it provides tools for experiment comparison, allowing users to assess variations in parameters, metrics, and dependencies across different experiments, enhancing the overall analysis and optimization of machine learning projects. Overall, Keepsake streamlines the experimentation process, making it easier for practitioners to manage and evolve their machine learning workflows effectively.

Guild AI serves as an open-source toolkit for tracking experiments, crafted to introduce systematic oversight into machine learning processes, thereby allowing users to enhance model creation speed and quality. By automatically documenting every facet of training sessions as distinct experiments, it promotes thorough tracking and evaluation. Users can conduct comparisons and analyses of different runs, which aids in refining their understanding and progressively enhancing their models. The toolkit also streamlines hyperparameter tuning via advanced algorithms that are executed through simple commands, doing away with the necessity for intricate trial setups. Furthermore, it facilitates the automation of workflows, which not only speeds up development but also minimizes errors while yielding quantifiable outcomes. Guild AI is versatile, functioning on all major operating systems and integrating effortlessly with pre-existing software engineering tools. In addition to this, it offers support for a range of remote storage solutions, such as Amazon S3, Google Cloud Storage, Azure Blob Storage, and SSH servers, making it a highly adaptable choice for developers. This flexibility ensures that users can tailor their workflows to fit their specific needs, further enhancing the toolkit’s utility in diverse machine learning environments.

AI engineering can be transparent rather than opaque. With a suite of tools for tracing, assessment, prompt management, and more, HoneyHive emerges as a comprehensive platform for AI observability and evaluation, aimed at helping teams create dependable generative AI applications. This platform equips users with resources for model evaluation, testing, and monitoring, promoting effective collaboration among engineers, product managers, and domain specialists. By measuring quality across extensive test suites, teams can pinpoint enhancements and regressions throughout the development process. Furthermore, it allows for the tracking of usage, feedback, and quality on a large scale, which aids in swiftly identifying problems and fostering ongoing improvements. HoneyHive is designed to seamlessly integrate with various model providers and frameworks, offering the necessary flexibility and scalability to accommodate a wide range of organizational requirements. This makes it an ideal solution for teams focused on maintaining the quality and performance of their AI agents, delivering a holistic platform for evaluation, monitoring, and prompt management, ultimately enhancing the overall effectiveness of AI initiatives. As organizations increasingly rely on AI, tools like HoneyHive become essential for ensuring robust performance and reliability.

Visdom serves as a powerful visualization tool designed to create detailed visual representations of real-time data, assisting researchers and developers in monitoring their scientific experiments conducted on remote servers. These visualizations can be accessed through web browsers and effortlessly shared with colleagues, fostering collaboration. With its interactive capabilities, Visdom is tailored to enhance the scientific experimentation process. Users can easily broadcast visual representations of plots, images, and text, making it accessible for both personal review and team collaboration. The organization of the visualization space can be managed via the Visdom user interface or through programmatic means, enabling researchers and developers to thoroughly examine experiment outcomes across various projects and troubleshoot their code. Additionally, features such as windows, environments, states, filters, and views offer versatile options for managing and viewing critical experimental data. Ultimately, Visdom empowers users to build and tailor visualizations specifically suited for their projects, streamlining the research workflow. Its adaptability and range of features make it an invaluable asset for enhancing the clarity and accessibility of scientific data.

DagsHub serves as a collaborative platform tailored for data scientists and machine learning practitioners to effectively oversee and optimize their projects. By merging code, datasets, experiments, and models within a cohesive workspace, it promotes enhanced project management and teamwork among users. Its standout features comprise dataset oversight, experiment tracking, a model registry, and the lineage of both data and models, all offered through an intuitive user interface. Furthermore, DagsHub allows for smooth integration with widely-used MLOps tools, which enables users to incorporate their established workflows seamlessly. By acting as a centralized repository for all project elements, DagsHub fosters greater transparency, reproducibility, and efficiency throughout the machine learning development lifecycle. This platform is particularly beneficial for AI and ML developers who need to manage and collaborate on various aspects of their projects, including data, models, and experiments, alongside their coding efforts. Notably, DagsHub is specifically designed to handle unstructured data types, such as text, images, audio, medical imaging, and binary files, making it a versatile tool for diverse applications. In summary, DagsHub is an all-encompassing solution that not only simplifies the management of projects but also enhances collaboration among team members working across different domains.

Streamline the entire machine learning lifecycle from start to finish. Equip developers and data scientists with diverse, efficient tools for swiftly constructing, training, and deploying machine learning models. Speed up market readiness and enhance team collaboration through top-notch MLOps—akin to DevOps but tailored for machine learning. Foster innovation on a secure and trusted platform that prioritizes responsible machine learning practices. Cater to all skill levels by offering both code-first approaches and user-friendly drag-and-drop designers, alongside automated machine learning options. Leverage comprehensive MLOps functionalities that seamlessly integrate into current DevOps workflows and oversee the entire ML lifecycle effectively. Emphasize responsible ML practices, ensuring model interpretability and fairness, safeguarding data through differential privacy and confidential computing, while maintaining oversight of the ML lifecycle with audit trails and datasheets. Furthermore, provide exceptional support for a variety of open-source frameworks and programming languages, including but not limited to MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R, making it easier for teams to adopt best practices in their machine learning projects. With these capabilities, organizations can enhance their operational efficiency and drive innovation more effectively.

Utilize Weights & Biases (WandB) for experiment tracking, hyperparameter tuning, and versioning of both models and datasets. With just five lines of code, you can efficiently monitor, compare, and visualize your machine learning experiments. Simply enhance your script with a few additional lines, and each time you create a new model version, a fresh experiment will appear in real-time on your dashboard. Leverage our highly scalable hyperparameter optimization tool to enhance your models' performance. Sweeps are designed to be quick, easy to set up, and seamlessly integrate into your current infrastructure for model execution. Capture every aspect of your comprehensive machine learning pipeline, encompassing data preparation, versioning, training, and evaluation, making it incredibly straightforward to share updates on your projects. Implementing experiment logging is a breeze; just add a few lines to your existing script and begin recording your results. Our streamlined integration is compatible with any Python codebase, ensuring a smooth experience for developers. Additionally, W&B Weave empowers developers to confidently create and refine their AI applications through enhanced support and resources.

MLflow is an open-source suite designed to oversee the machine learning lifecycle, encompassing aspects such as experimentation, reproducibility, deployment, and a centralized model registry. The platform features four main components that facilitate various tasks: tracking and querying experiments encompassing code, data, configurations, and outcomes; packaging data science code to ensure reproducibility across multiple platforms; deploying machine learning models across various serving environments; and storing, annotating, discovering, and managing models in a unified repository. Among these, the MLflow Tracking component provides both an API and a user interface for logging essential aspects like parameters, code versions, metrics, and output files generated during the execution of machine learning tasks, enabling later visualization of results. It allows for logging and querying experiments through several interfaces, including Python, REST, R API, and Java API. Furthermore, an MLflow Project is a structured format for organizing data science code, ensuring it can be reused and reproduced easily, with a focus on established conventions. Additionally, the Projects component comes equipped with an API and command-line tools specifically designed for executing these projects effectively. Overall, MLflow streamlines the management of machine learning workflows, making it easier for teams to collaborate and iterate on their models.

A comprehensive platform designed for reproducible and scalable applications in Machine Learning and Deep Learning. Explore the array of features and products that support the leading platform for managing data science workflows today. Polyaxon offers an engaging workspace equipped with notebooks, tensorboards, visualizations, and dashboards. It facilitates team collaboration, allowing members to share, compare, and analyze experiments and their outcomes effortlessly. With built-in version control, you can achieve reproducible results for both code and experiments. Polyaxon can be deployed in various environments, whether in the cloud, on-premises, or in hybrid setups, ranging from a single laptop to container management systems or Kubernetes. Additionally, you can easily adjust resources by spinning up or down, increasing the number of nodes, adding GPUs, and expanding storage capabilities as needed. This flexibility ensures that your data science projects can scale effectively to meet growing demands.

Aim serves as a comprehensive tool for logging all AI-related metadata, including experiments and prompts, while offering a user interface for comparison and observation, as well as a software development kit for programmatic queries. This open-source, self-hosted solution is built to manage hundreds of thousands of tracked metadata sequences efficiently. The two primary applications of AI metadata are in the realms of experiment tracking and prompt engineering. Additionally, Aim boasts an attractive and high-performance interface that facilitates the exploration and comparison of various training runs and prompt sessions, enhancing the overall user experience in AI development. Overall, Aim stands out as a vital resource for those engaged in advanced AI projects.

With Determined, you can engage in distributed training without needing to modify your model code, as it efficiently manages the provisioning of machines, networking, data loading, and fault tolerance. Our open-source deep learning platform significantly reduces training times to mere hours or minutes, eliminating the lengthy process of days or weeks. Gone are the days of tedious tasks like manual hyperparameter tuning, re-running failed jobs, and the constant concern over hardware resources. Our advanced distributed training solution not only surpasses industry benchmarks but also requires no adjustments to your existing code and seamlessly integrates with our cutting-edge training platform. Additionally, Determined features built-in experiment tracking and visualization that automatically logs metrics, making your machine learning projects reproducible and fostering greater collaboration within your team. This enables researchers to build upon each other's work and drive innovation in their respective fields, freeing them from the stress of managing errors and infrastructure. Ultimately, this streamlined approach empowers teams to focus on what they do best—creating and refining their models.

Amazon SageMaker equips users with all necessary tools and libraries to create machine learning models, allowing for an iterative approach in testing various algorithms and assessing their effectiveness to determine the optimal fit for specific applications. Within Amazon SageMaker, users can select from more than 15 built-in algorithms that are optimized for the platform, in addition to accessing over 150 pre-trained models from well-known model repositories with just a few clicks. The platform also includes a range of model-development resources such as Amazon SageMaker Studio Notebooks and RStudio, which facilitate small-scale experimentation to evaluate results and analyze performance data, ultimately leading to the creation of robust prototypes. By utilizing Amazon SageMaker Studio Notebooks, teams can accelerate the model-building process and enhance collaboration among members. These notebooks feature one-click access to Jupyter notebooks, allowing users to begin their work almost instantly. Furthermore, Amazon SageMaker simplifies the sharing of notebooks with just one click, promoting seamless collaboration and knowledge exchange among users. Overall, these features make Amazon SageMaker a powerful tool for anyone looking to develop effective machine learning solutions.

Data Version Control (DVC) is an open-source system specifically designed for managing version control in data science and machine learning initiatives. It provides a Git-like interface that allows users to systematically organize data, models, and experiments, making it easier to oversee and version various types of files such as images, audio, video, and text. This system helps structure the machine learning modeling process into a reproducible workflow, ensuring consistency in experimentation. DVC's integration with existing software engineering tools is seamless, empowering teams to articulate every facet of their machine learning projects through human-readable metafiles that detail data and model versions, pipelines, and experiments. This methodology promotes adherence to best practices and the use of well-established engineering tools, thus bridging the gap between the realms of data science and software development. By utilizing Git, DVC facilitates the versioning and sharing of complete machine learning projects, encompassing source code, configurations, parameters, metrics, data assets, and processes by committing the DVC metafiles as placeholders. Furthermore, its user-friendly approach encourages collaboration among team members, enhancing productivity and innovation within projects.