Weights & Biases

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Streamline ML workflows end-to-end with Weights & Biases, the MLOps platform trusted by the world's leading AI teams.

How Weights & Biases can help you:

Track experiments and manage ML workflows efficiently.
Version and iterate on datasets to enhance model performance.
Reproduce models reliably for consistent results.
Optimize hyperparameters to achieve superior accuracy.
Automate ML workflows to save time and resources.

Why choose Weights & Biases: Key features

Comprehensive experiment tracking .
Collaborative dashboards for team projects.
Interactive data visualization tools .
Model versioning and lifecycle management.
Support for any ML infrastructure, with flexible deployment options.

Who should choose Weights & Biases:

ML practitioners seeking to automate and simplify workflows.
Teams requiring collaboration and efficiency in ML projects.
Researchers aiming for reproducibility and scalability in ML experiments.
Enterprises looking for robust ML lifecycle management.

About Weights & Biases

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https://wandb.com/

Release Date

March 2024

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Scalable AI: LLMOps Principles and Best Practices - DZone

Join the DZone community and get the full member experience. Join For Free Organizations are fully adopting Artificial Intelligence (AI) and proving that AI is valuable. Enterprises are looking for valuable AI use cases that abound in their industry and functional areas to reap more benefits. Organizations are responding to opportunities and threats, gain improvements in sales, and lower costs. Organizations are recognizing the special requirements of AI workloads and enabling them with purpose-built infrastructure that supports the consolidated demands of multiple teams across the organization. Organizations adopting a shift-left paradigm by planning for good governance early in the AI process will minimize AI efforts for data movement to accelerate model development. In an era of rapidly evolving AI, data scientists should be flexible in choosing platforms that provide flexibility, collaboration, and governance to maximize adoption and productivity. Let's dive into the workflow automation and pipeline orchestration world. Recently, two prominent terms have appeared in the artificial intelligence and machine learning world: MLOps and LLMOps. MLOps (Machine Learning Operations) is a set of practices and technology to standardize and streamline the process of construction and deployment of machine learning systems. It covers the entire lifecycle of a machine learning application from data collection to model management. MLOps provides a provision for huge workloads to accelerate time-to-value. MLOps principles are architected based on the DevOps principles to manage applications built-in ML (Machine Learning). The ML model is created by applying an algorithm to a mass of training data, which will affect the behavior of the model in different environments. Machine learning is not just code, its workflows include the three key assets Code, Model, and Data. Figure 1: ML solution is comprised of Data, Code, and Model These assets in the development environment will have the least restrictive access controls and less quality guarantee, while those in production will be the highest quality and tightly controlled. The data is coming from the real world in production where you cannot control its change, and this raises several challenges that need to be resolved. For example: To resolve these types of issues, there are combined practices from DevOps, data engineering, and practices unique to machine learning. Figure 2: MLOps is the intersection of Machine Learning, DevOps, and Data Engineering - LLMOps rooted in MLOps Hence, MLOps is a set of practices that combines machine learning, DevOps, and data engineering, which aims to deploy and maintain ML systems in production reliably and efficiently. The recent rise of Generative AI with its most common form of large language models (LLMs) prompted us to consider how MLOps processes should be adapted to this new class of AI-powered applications. LLMOps (Large Language Models Operations) is a specialized subset of MLOps (Machine Learning Operations) tailored for the efficient development and deployment of large language models. LLMOps ensures that model quality remains high and that data quality is maintained throughout data science projects by providing infrastructure and tools. Use a consolidated MLOps and LLMOps platform to enable close interaction between data science and IT DevOps to increase productivity and deploy a greater number of models into production faster. MLOps and LLMOps will both bring Agility to AI Innovation to the project. LLMOps tools include MLOps tools and platforms, LLMs that offer LLMOps capabilities, and other tools that can help with fine-tuning, testing, and monitoring. Explore more on LLMOps tools. MLOps and LLMOps have two different processes and techniques in their primary tasks. Table 1 shows a few key tasks and a comparison between the two methodologies: Table 1: Key tasks of MLOPs and LLMOps methodologies Adapting any implications into MLOps required minimal changes to existing tools and processes. Moreover, many aspects do not change: Table 2: Key properties of LLMs and implications for MLOps An ML solution comprises data, code, and models. These assets are developed, tested, and moved to production through deployments. For each of these stages, we also need to operate within an execution environment. Each of the data, code, models, and execution environments is ideally divided into development, staging, and production. Two major patterns can be used to manage model deployment. The training code (Figure 3, deploy pattern code) which can produce the model is promoted toward the production environment after the code is developed in the dev and tested in staging environments using a subset of data. The packaged model (Figure 4, deploy pattern model) is promoted through different environments, and finally to production. Model training is executed in the dev environment. The produced model artifact is then moved to the staging environment for model validation checks, before deployment of the model to the production environment. This approach requires two separate paths, one for deploying ancillary code such as inference and monitoring code and the other "deploy code" path where the code for these components is tested in staging and then deployed to production. This pattern is typically used when deploying a one-off model, or when model training is expensive and read-access to production data from the development environment is possible. The choice of process will also depend on the business use case, maturity of the machine learning infrastructure, compliance and security guidelines, resources available, and what is most likely to succeed for that particular use case. Therefore, it is a good idea to use standardized project templates and strict workflows. Your decisions around packaging ML logic as version-controlled code vs. registered models will help inform your decision about choosing between the deploy models, deploy code, and hybrid architectures. With LLMs, it is common to package machine-learning logic in new forms. These may include: Figure 5 is a machine learning operations architecture and process that uses Azure Databricks. The field of LLMOps is quickly evolving. Here are key components and considerations to bear in mind. Some, but not necessarily all of the following approaches make up a single LLM-based application. Any of these approaches can be taken to leverage your data with LLMs. RAG LLMs use two systems to obtain external data: A good rule of thumb is to start with the simplest approach possible, such as prompt engineering with a third-party LLM API, to establish a baseline. Once this baseline is in place, you can incrementally integrate more sophisticated strategies like RAG or fine-tuning to refine and optimize performance. The use of standard MLOps tools such as MLflow is equally crucial in LLM applications to track performance over different approach iterations. Quick, on-the-fly model guidance. Evaluating LLMs is a challenging and evolving domain, primarily because LLMs often demonstrate uneven capabilities across different tasks. LLMs can be sensitive to prompt variations, demonstrating high proficiency in one task but faltering with slight deviations in prompts. Since most LLMs output natural language, it is very difficult to evaluate the outputs via traditional Natural Language Processing metrics. For domain-specific fine-tuned LLMs, popular generic benchmarks may not capture their nuanced capabilities. Such models are tailored for specialized tasks, making traditional metrics less relevant. It is often the case that LLM performance is being evaluated in domains where text is scarce or there is a reliance on subject matter expert knowledge. In such scenarios, evaluating LLM output can be costly and time-consuming. Some prominent benchmarks used to evaluate LLM performance include: A well-defined LLMOps architecture is essential for managing machine learning workflows and operationalizing models in production environments. Here is an illustration of the production architecture with key adjustments to the reference architecture from traditional MLOps, and below is the reference production architecture for LLM-based applications: Figure 7: RAG workflow using a self-hosted fine-tuned model (Image Source: Databricks) Automation of workload is variable and intensive and will help in filling the gap between the data science team and the IT operations team. Planning for good governance early in the AI process will minimize AI efforts for data movement to accelerate model development. The emergence of LLMOps highlights the rapid advancement and specialized needs of the field of Generative AI and LLMOps is still rooted in the foundational principles of MLOps. In this article, we have looked at key components, practices, tools, and reference architecture with examples such as:

dzone.com

Wed, 16 Oct, 4:37 PM UTC

Feeling like your workload is too heavy? This all-in-one AI tool can help

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New Atlas

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NotebookLM: 6 Ways This AI Tool Can Transform Your Study and Work

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By combining your personal knowledge with public information, you can receive tailored advice on various topics. This feature enables you to: Whether you're seeking guidance on career development, personal growth, or specific skills, NotebookLM can provide structured, personalized support. This application is ideal for those looking to enhance their expertise or share knowledge in a structured, engaging format. NotebookLM can play a crucial role in managing your health. By compiling information on symptoms, treatments, and potential exercises, it assists more informed discussions with healthcare providers. This application enables you to: By using NotebookLM for health management, you can take a more active role in your wellness journey, making sure you have the necessary information to make informed decisions about your health. For professionals, NotebookLM offers unique advantages in career development and self-presentation. 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Geeky Gadgets

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GitHub Models: A New Feature to Test Popular LLMs

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Analytics Insight

Tue, 6 Aug, 12:00 PM UTC

Lifecycle management AI Platform released by ClearML

Artificial Intelligence Lifecycle management AI Platform released by ClearML Monday, August 26, 2024 Austin Harris ClearML has unveiled a comprehensive AI platform that streamlines the entire AI lifecycle, from development to deployment. The open-source platform supports GenAI, LLMOps, and MLOps, offering flexibility across cloud, vendor, and silicon environments. This end-to-end solution enhances efficiency, scalability, and resource management for AI teams. ClearML, the solution for unleashing AI in the enterprise, announced the launch of its expansive end-to-end AI Platform, designed to streamline AI adoption and the entire development lifecycle. This unified, open-source platform supports every phase of AI development, from lab to production, allowing organizations to leverage any model, dataset, or architecture at scale. ClearML's platform integrates seamlessly with existing tools, frameworks, and infrastructures, offering unmatched flexibility and control for AI builders and DevOps teams building, training, and deploying models at every scale on any AI infrastructure. End-to-end lifecycle management AI Platform released by ClearML With this release, ClearML becomes the most flexible, wholly agnostic, end-to-end AI platform in the marketplace today in that it is: Silicon-agnostic: supporting NVIDIA, AMD, Intel, ARM, and other GPUs Cloud-agnostic: supporting Azure, AWS, GCP, Genesis Cloud, and others, as well as multi-cloud Vendor-agnostic: supporting the most popular AI and machine learning frameworks, libraries, and tools, such as PyTorch, Keras, Jupyter Notebooks, and others Completely modular: Customers can use the full platform alone or integrate it with their existing AI/ML frameworks and tools such as Grafana, Slurm, MLflow, Sagemaker, and others to address GenAI, LLMOps, and MLOps use cases and to maximize existing investments. "ClearML's end-to-end AI platform is crucial for organizations looking to streamline their AI operations, reduce costs, and enhance innovation - while safeguarding their competitive edge and future-proofing their AI investments by using our completely cloud-, vendor-, and silicon- agnostic platform. By providing a comprehensive, flexible, and secure solution, ClearML empowers teams to build, train, and deploy AI applications more efficiently, ultimately driving better business outcomes and faster time to production at scale," said Moses Guttmann, Co-founder and CEO of ClearML. The ClearML end-to-end AI Platform encompasses newly expanded capabilities and integrates previous stand-alone products, and includes: A GenAI App Engine, designed to make it easy for AI teams to build and deploy GenAI applications, maximizing the potential and the value of their LLMs. An Open Source AI Development Center, which offers collaborative experiment management, powerful orchestration, easy-to-build data stores, and one-click model deployment. Users can develop their ML code and automation with ease, ensuring their work is reproducible and scalable. An AI Infrastructure Control Plane, helping customers manage, orchestrate, and schedule GPU compute resources effortlessly, whether on-premise, in the cloud, or in hybrid environments. These new capabilities, which were also introduced today in a separate announcement, maximize GPU utilization and provide fractional GPUs, as well as multi-tenancy and extensive billing and chargeback capabilities that offer precise cost control, empowering customers to optimize their compute resources efficiently. Accelerating GenAI adoption with an open-source platform for seamless AI, LLMOps, and MLOps development, deployment, and resource management ClearML's AI Platform enables customers to use any type of machine learning, deep learning, or large language model (LLM) with any dataset, in any architecture, at scale. AI Builders can seamlessly develop their ML code and automation, ensuring their work is reproducible and scalable. That's important, because it addresses several critical challenges faced by organizations in developing, deploying, and managing AI solutions in the most complex and demanding environments. Here's why it matters: Unified End-to-end Workflow: ClearML provides a seamless workflow that integrates all stages of AI development, from data ingestion and model training to deployment and monitoring. This unified approach eliminates the need for multiple disjointed tools, simplifying the AI adoption and development process. Superior Efficiency and ROI: ClearML's new AI infrastructure orchestration and management capabilities help customers execute 10X more AI and HPC workloads on their existing infrastructure. Interoperability: The platform is designed to work with any machine learning framework, dataset, or infrastructure, whether on-premise, in the cloud, or in a hybrid environment. This flexibility ensures that organizations can use their preferred tools and avoid vendor lock-in. Orchestration and Automation: ClearML automates many aspects of AI development, such as data preprocessing, model training, and pipeline management. This ensures full utilization of compute resources for multi-instance GPUs and job scheduling, prioritization, and quotas. ClearML empowers team members to schedule resources on their own with a simple and unified interface, enabling them to self-serve with more automation and greater reproducibility. Scalable Solutions: The platform supports scalable compute resources, enabling organizations to handle large datasets and complex models efficiently. This scalability is crucial for keeping up with the growing demands of AI applications. Optimized Resource Utilization: By providing detailed insights and controls over compute resource allocation, ClearML helps organizations maximize their GPU and cloud resource utilization. This optimization leads to significant cost savings and prevents resource wastage. Budget and Policy Control: ClearML offers tools for managing cloud compute budgets, including autoscalers and spillover features. These tools help organizations predict and control their monthly cloud expenses, ensuring cost-effectiveness, by providing advanced user management for superior quota/over-quota management, priority, and granular control of compute resources allocation policies. Enterprise-Grade Security: The platform includes robust security features such as role-based access control, SSO authentication, and LDAP integration. These features ensure that data, models, and compute resources are securely managed and accessible only to authorized users. Real-Time Collaboration: The platform facilitates real-time collaboration among team members, allowing them to share data, models, and insights effectively. This collaborative environment fosters innovation and accelerates the development process. Become a subscriber of App Developer Magazine for just $5.99 a month and take advantage of all these perks. MEMBERS GET ACCESS TO- Exclusive content from leaders in the industry - Q&A articles from industry leaders - Tips and tricks from the most successful developers weekly - Monthly issues, including all 90+ back-issues since 2012 - Event discounts and early-bird signups - Gain insight from top achievers in the app store - Learn what tools to use, what SDK's to use, and more Subscribe here Lifecycle management AI Platform released by ClearML, AI Platform, AI, Lifecycle Management, ClearML, GenAI, LLMOps, MLOps share

App Developer Magazine

Mon, 26 Aug, 6:05 PM UTC

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