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  • How Open Source Can Benefit AI Development

    How Open Source Can Benefit AI Development

    Enterprises are increasingly reliant upon open source software. A full 95% of IT leaders say open source tools are key to their enterprise infrastructure. Simultaneously, we’ve witnessed a sharp increase in the adoption of artificial intelligence (AI) and machine learning (ML). Most IT divisions want to take advantage of AI in some form, and open source technologies will likely be vital to these efforts.

    I recently met with Moses Guttmann, co-founder and CEO of ClearML, to understand the benefits of adopting open source software within the sphere of artificial intelligence. To Guttmann, open source has many benefits—the visibility it grants can equate to helpful feedback from the developer community, and transparency can inform new feature development and identify potential security gaps. There are also countless open source tools to help organizations develop and deploy ML models.

    Below, we’ll consider the benefits and potential drawbacks of incorporating OSS in AI development and explore ways to optimize the use of AI within an organization.

    Benefits of Using Open Source for AI

    AI has evolved in leaps and bounds. What was pure sci-fi a few years ago has become a reality through ChatGPT and other AI-powered platforms. The capabilities of AI are awe-inspiring, but Guttmann believes it still requires fine-tuning to move from entertainment purposes into a valid business driver.

    For example, custom ML models are difficult to construct from scratch, and generic APIs aren’t always that customized to the use case at hand to be valuable. Instead, Guttman foresees more and more organizations downloading open pre-trained models and retraining or customizing them to meet their specific needs.

    For software providers, there are many benefits to adopting an open source strategy. The visibility it creates helps evolve a project with feedback from the community, said Guttmann. This, in turn, helps organizations build better, more stable products. For example, ClearML adopts plenty of OSS to power their user interface, backend and other components, in addition to providing a popular open source CI/CD framework for ML workflows. “We’re true believers in the power of community and like to work with open source and contribute back to these projects,” said Guttman.

    Use Cases for Open Source in AI Development

    Open source software is powering innovation in many ways. And when it comes to AI, there are numerous open source projects to help accelerate AI development. Such projects encompass pre-trained models, data-structuring tools, frameworks to train models and AI deployment pipelines. For example, many open source machine learning frameworks can help engineers build and train their models from scratch. These free-to-use tools include TensorFlow, Keras, PyTorch, H2O, XGBoost and others.

    However, training models from scratch is time-intensive and complicated, said Guttmann. Instead, another option is to use pre-trained models accessible on GitHub or other repositories. Some popular examples are PaddleNLP and Natural Language Toolkit for natural language processing, OpenCV for machine vision and DeepFaceLab for deepfakes. There are also many open source Python packages that can structure and clean data for ML model training purposes.

    Open source software can also be applied to streamline the ML DevOps pipeline itself. AI requires intense processing and is typically deployed to its own computing environments. Infrastructure management around running and training AI is a promising area where open source could assist, said Guttmann. “Automating this process with an abstraction layer to allow data science and ML engineers to access product-grade hardware is a must,” said Guttmann.

    Potential Downsides of Open Source Usage

    Yet, of course, there are some potential downsides to using open source software within the field of AI. Supply chain attacks are still a persistent threat, and numerous open source software risks can present themselves. These include bad actors compromising a legitimate package, name confusion attacks, unmaintained software and untracked dependencies. License restrictions could also potentially block the inclusion of OSS in certain projects.

    Thankfully, the benefits of OSS appear to outweigh these concerns. Automated vulnerability scanning techniques can help detect known issues. It also helps that 89% of technology leaders see enterprise open source as more secure or as secure as proprietary software, according to a Red Hat report.

    Tips For Getting the Most From AI

    Open source is powering digital innovation on many fronts, including AI. But, simply looping in OSS isn’t the only aspect of fully leveraging AI—another will be the operationalization of ML management. The right automation infrastructure in place could enable the agility necessary to continuously retrain models. And since there are potential gaps in the CI/CD process, Guttmann recommended separating AI/ML from your normal CI/CD for security reasons.

    Guttmann also believes that, in general, companies should be more transparent around ML models, including sharing details behind their creation and visibility regarding the datasets fed into them. Overlooking this could lead to ethical concerns. Furthermore, limiting the “hallucinations” generative AI produces is another huge gap that must be bridged, said Guttmann. This could be solved by simply allowing the system to respond with “I don’t know,” instead of confidently spouting nonsense.

    In summary, open source could bring many benefits to bespoke AI/ML adoption. Many mature open source tools exist to enable engineers to create advanced AI and oversee the management and deployment of custom ML models. But the real test will be retaining this open creed by upholding visibility and transparency into the end creations.

  • ClearML Releases New Reports Feature to Share Real-Time Results of Machine Learning Projects and Ignite ML Collaboration Across the Enterprise

    Tel Aviv, IL, January 19, 2023 – ClearML, the leading open source, end-to-end MLOps platform, today announced it has released a new Reports feature that is now generally available. This new feature makes it easy to create and share real-time reports within ClearML as well as connect to third-party editors such as Confluence, Monday, Notion, Colab (Jupyter), and others using embed code. A report can contain charts that auto-update continuously throughout an experiment lifecycle.

    ClearML users can now easily create, collaborate on, and share reports, as well as graphs and charts, in order to summarize and explain experiments and how model versions improve,  analyze results, show experiment comparisons, discuss bugs, demonstrate progress towards milestones, and publish their work.

    “Reporting and showcasing your findings to colleagues, managers, or even your future self is a core component of any modern collaborative workflow,” said Moses Guttmann, Co-founder and CEO of ClearML. “Having one central place where you can seamlessly  report, analyze, plan, and collaborate on your work in real time makes it that much easier. ClearML-Reports connects seamlessly with existing ClearML functionality and is based on markdown, so it’s very easy to export or connect to an external reporting tool.”

    Features and Benefits of ClearML Reports

    The new ClearML Reports feature was designed with a multitude of functionality in mind, including:

    • Creation: It’s now easy to write reports using markdown language, and there is a handy markdown cheatsheet to get started. Reports can be filtered by tags or archived like datasets or experiments.
    • Visualization:  Users can embed scalars, plots, or debug samples from the ClearML experiment manager right into the markdown editor with preview updates. They can even embed descriptive statistics of their datasets or comparisons graphs from multiple experiments — what’s important is that these are not HTML exports, but are still connected to the back-end such that when new data comes in, it will be visible after a report refresh.
    • Team Collaboration: Teammates with ClearML logins can edit reports that are not published as read-only. Reports are also searchable based on their descriptions.
    • Sharing of Reports: Users can share on-platform with others who have a ClearML login and can export to PDF for a time-bound report to share with anyone else. Publishing the report will make it read-only, so it can be used as a single source of truth among colleagues. Users can easily export your reports out of ClearML as markdown or PDF.
    • External Tooling Integration: Many users and their teams each have their own workflows with their own toolchains. By not limiting the reporting functionality to only ClearML reports, users can easily integrate ClearML elements into their favorite third-party reporting tools, like Notion, Confluence, Monday, and others.

    “Reporting our findings on the weekly standups was quite manual,” said Omri Bar, AI Research Team Leader at Theator, the creator of  Surgical Intelligence. “I’d have to jump around between experiments, moving from one plot to another, narrating what was interesting about the graph in question. That’s not very easy for my colleagues to follow along with. Now I can prepare a neat report ahead of time, with all the interesting parts embedded inside, important notes right next to them, cleanly organized by topic. It has made our communication much smoother.”

    See the power of ClearML Reports for yourself by watching the overview video now.

    Get started with ClearML by using our free tier servers or by hosting your own. Read our documentation here. You’ll find more in-depth tutorials about ClearML on our YouTube channel and we also have a very active Slack channel for anyone that needs help. If you need to scale your ML pipelines and data abstraction or need unmatched performance and control, please request a demo. To learn more about ClearML, please visit: https://clear.ml/.

    About ClearML

    ClearML is a unified, open source platform for continuous machine learning (ML), trusted by forward-thinking Data Scientists, ML Engineers, DevOps, and decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide. We enable customers to build continuous ML workflows — from experiment management and orchestration through data management and scheduling, followed by provisioning and serving — to achieve the fastest time to ML production, fastest time to value, and increased performance. In this way, ClearML accelerates ML adoption across business units, helping companies reach their revenue potential and materialize their ML investments. With thousands of deployments and a vibrant, engaged community, ClearML is transforming the ML space — bridging software, machine learning, and automation. To learn more, visit the company’s website at https://clear.ml.