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Prêt à passer en production avec Hasty.ai ?

Table of Contents

  1. Introduction
  2. The Challenge of Visionaire Technology
  3. Factors Hindering Migration from MVP to Production
    1. Custom Data Set Building and Maintenance
    2. Lack of Feedback in Machine Learning Approaches
    3. Absence of Tooling and Best Practice Processes
  4. Introducing Hasty: A Solution for Visionaire Challenges
    1. Training Models and Automating Manual Work
    2. Feedback-driven Approach for ML Engineers
    3. Steep Learning Curve for Models
  5. Transforming Machine Learning Production Flow
    1. Limitations of Waterfall Development
    2. Iterative Process with Hasty
  6. Maximizing Feedback and Actionability
  7. Simplifying the Frankensuit of Tools
    1. Fast and Easy Project Setup in Hasty
    2. Privacy and External Storage Options
    3. Uploading Images and Annotations
  8. Automation Levels in Hasty
    1. Level 1: Labeling Single Objects
    2. Level 2: Automating Image Leveling
    3. Level 3: Batch Processing and Labeling
  9. Accelerating Labeling with Automation Features
    1. Dexter: Creating Complex Masks
    2. Grab Cut: Fine-tuning Mask Suggestions
  10. Upgrading to Level 2 Automation
    1. Custom Model Training and Updating
    2. Confidence Interval Adjustment for Best Results
    3. Fine-tuning Masks and Annotations
  11. Tracking Performance Improvements
  12. Transition to Fully Automated Labeling
    1. Batch Processing with Custom Models
    2. Ensuring Quality with Automatic Labeling
    3. AI-powered Error Finder Feature
  13. Retaining Ownership and Licensing Models
  14. Exploring Model Optimization in Hasty
    1. Access to Model in Hasty
    2. Trying Different Architectures and Parameters
    3. Model Zoo for Comparison and Optimization
  15. Driving Data-centric and Agile ML Development
  16. Conclusion

🚀 Accelerating Machine Learning Production with Hasty

Machine learning technology has a promising future, but many teams often find themselves stuck in a state of "pilot purgatory" as they struggle to transition from Minimum viable Product (MVP) to full-Scale production. The challenges they face include the daunting task of building and maintaining custom data sets, the lack of Timely feedback on machine learning approaches, and the absence of proper tooling and best practice processes.

To address these challenges, Hasty offers a comprehensive solution that streamlines the entire machine learning production flow. By providing efficient model training and automation, feedback-driven approaches for machine learning engineers, and a steep learning curve for models, Hasty aims to bridge the gap between concept and deployment.

The Challenge of Visionaire Technology

Visionaire technology, while promising, often fails to live up to its hype. One of the major obstacles faced by machine learning teams is the amount of effort required to Create and maintain a custom data set for advanced applications. This daunting task can seem insurmountable and hinder the smooth transition from MVP to production.

Additionally, machine learning teams often receive feedback on their models only after deployment, which is far too late to make significant adjustments. This lack of timely feedback further compounds the challenges faced during migration. Moreover, the absence of proper tooling and best practice processes for building, maintaining, and deploying machine learning applications adds additional roadblocks to the development process.

Introducing Hasty: A Solution for Visionaire Challenges

Hasty is designed to address the challenges faced in the production of machine learning models. With Hasty, teams can train models while simultaneously labeling data, automating up to 90% of the manual work involved. This not only saves time but also provides valuable feedback to machine learning engineers, allowing them to adjust and validate their approaches.

The key AdVantage of Hasty is the steep learning curve it offers to models. By leveraging the symbiotic relationship between model training and data labeling, Hasty ensures that the models receive continuous, iterative learning, resulting in more robust solutions at a faster pace.

Transforming Machine Learning Production Flow

The traditional machine learning production flow often follows a waterfall development approach, which proves to be inadequate in adapting to new information and feedback. Similar to software development, this approach hinders the incorporation of new insights derived from feedback.

Hasty introduces an iterative process that allows practitioners to bounce between tasks as needed, ensuring flexibility and adaptability to feedback loops. This iterative approach acknowledges that Relevant feedback can arise from unexpected sources and provides a process to evaluate and incorporate it effectively.

Maximizing Feedback and Actionability

Inefficient processes and inadequate tooling prevent machine learning engineers from effectively utilizing feedback. Hasty addresses these issues by streamlining the process and automating complex tasks, enabling practitioners to take timely action on feedback as and when it arises.

The ability to control, track, and act upon relevant feedback is crucial to the success of machine learning projects. Hasty's iterative process aligns with the unpredictability of feedback sources, empowering teams to make informed decisions Based on real-time Data Insights.

Simplifying the Frankensuit of Tools

Machine learning engineers often rely on a patchwork of tools to navigate the complex landscape of model development. This results in unnecessary time spent on tool maintenance instead of focusing on building the actual application.

Hasty simplifies this process by providing a fast and easy project setup. Users can easily define their projects, classes, and upload images through a user-friendly interface or API. Privacy concerns are addressed by offering the option to monitor external storage systems like AWS, Google Cloud, or Azure, ensuring that sensitive data remains within the user's control.

Automation Levels in Hasty

Hasty offers three levels of automation to expedite the labeling process. At Level 1, Hasty automates the labeling of single objects on an image. With Level 2, the automation extends to leveling entire images. Level 3 enables batch processing and labeling using a custom model trained with Level 2 automation.

To speed up the labeling process further, Hasty provides automation features such as Dexter and Grab Cut. Dexter allows users to create complex masks for objects with minimal effort, while Grab Cut enables fine-tuning of mask suggestions through an intuitive interface. These features reduce the time required to label images and improve model training efficiency.

Upgrading to Level 2 Automation

Custom model training and updating are key elements of Hasty's automation process. Once the initial labeling with Level 1 automation is complete, Hasty allows users to train the model using their custom data. This dynamically updated model is then used to automate the labeling process, significantly reducing the time and effort required.

Users can adjust the confidence interval to achieve the best labeling results. By accepting or editing the suggested masks, users can fine-tune their models and achieve higher accuracy in image labeling. With Hasty, the manual annotation process becomes faster and more efficient, enabling teams to process more images in a day.

Tracking Performance Improvements

Hasty provides machine learning engineers with the ability to track the performance improvements of their models. By analyzing key metrics and user behavior, teams can evaluate the effectiveness of their annotation strategies, determine the number of images required for labeling, identify challenging classes, and address issues like false positives and false negatives. With Hasty, data-driven decision-making becomes an integral part of the machine learning development process.

Transition to Fully Automated Labeling

Once the custom model has reached a satisfactory level of accuracy, Hasty enables fully automated labeling. Users can batch process complete data sets, leveraging the custom model, to label new images quickly and accurately. Hasty clearly marks and distinguishes between automatically labeled images and manually labeled ones, ensuring transparency and ease of review.

To ensure quality assurance, Hasty incorporates an AI-powered error finder feature. This advanced tool uses a carefully constructed model to detect common issues in annotations, including misclassifications, missed objects, artifacts, and poor outlines or masks. The model provides suggestions for improvement, which users can review and incorporate into the training process. Hasty ensures the continuous refinement of models based on corrections made by users.

Retaining Ownership and Licensing Models

Hasty understands the significance of ownership and licensing in the machine learning landscape. Users retain full ownership of their images and resulting data, with the option to license models exclusively from Hasty. This ensures that users have complete control over their intellectual property and provides them with the flexibility to commercialize their models as per their business requirements.

Exploring Model Optimization in Hasty

Hasty empowers machine learning engineers to optimize their models for specific use cases. Users gain access to their models within the Hasty environment, allowing them to experiment with transformations, loss functions, metrics, schedulers, different architectures, and over 100 parameters. Hasty supports a wide range of models, from simple classification models to highly complex Instant segmentation models, providing a holistic platform for model development.

With the model zoo feature, Hasty offers existing models that provide substantial annotation automation from the start of a project. Users can compare different architectures and parameters to identify the most effective approach for their specific use case. Hasty's model zoo reduces the time and effort required for model development, accelerating the machine learning production cycle.

Driving Data-centric and Agile ML Development

Hasty's innovative tooling and processes drive a shift towards data-centric and agile machine learning development. By providing an intuitive annotation environment, control features, and model playgrounds, Hasty enables teams to seamlessly create, fix, and improve data, thereby facilitating the development of increasingly robust models. The ability to switch between different tasks offers flexibility and adaptability, resulting in a faster time to market.

Conclusion

Hasty's advanced tooling, iterative process, and automation capabilities offer a transformative solution to the challenges faced in machine learning production. By streamlining the model training process, providing timely feedback to engineers, and simplifying complex tasks, Hasty enables teams to escape pilot purgatory and rapidly deploy machine learning models into production. With Hasty, the Journey from concept to fully functional production models becomes faster, more efficient, and more data-centric.

Highlights

  • Hasty accelerates the migration of machine learning models from MVP to production.
  • Custom data set building and maintenance pose significant challenges in visionaire technology.
  • Hasty's feedback-driven approach ensures timely adjustments and validation of ML approaches.
  • The iterative process in Hasty enables efficient adaptation to new insights and information.
  • Hasty simplifies the complex landscape of machine learning tools, saving time and effort.
  • Automation levels in Hasty expedite the image labeling process and improve efficiency.
  • Tracking performance improvements and metrics allows data-driven decision-making.
  • Fully automated labeling and AI-powered error finder features enhance the efficiency and accuracy of the model.
  • Hasty enables users to retain ownership of their data and license exclusive models.
  • Model optimization in Hasty empowers engineers to fine-tune models for specific use cases.
  • Hasty drives data-centric and agile machine learning development, reducing time to market.

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