Training, Validation, Test Split for Machine Learning Datasets

Meta Ray-Ban is back with new feature updates on their smart glasses. With their earlier release, they revolutionized how we interact with the world around us with access to hands-free artificial intelligence. The new AI feature updates are taking it a step further. Feature Updates The new Ray-Ban Meta smart glasses are designed to enhance user experience along with upgrading their aesthetics. Here is a brief overview of these updates: Video Calling Meta glasses now offer the convenience of hands-free video calling. With apps like WhatsApp and Messenger, users can share their perspectives with friends and family in real-time. This feature enhances communication while on the go, allowing users to stay connected without the need to hold a device. Apple Music Compatibility With the integration of Apple Music compatibility, users can enjoy their favorite playlists and podcasts directly through their smart glasses. This feature enhances the entertainment experience, providing users with access to their preferred audio content while engaged in various activities, such as running or commuting. Multimodal AI Assistant Ray-Ban Meta smart glasses incorporated multimodal AI to enhance user interaction and provide valuable assistance. Here is why this is a notable feature: Context Mapping Images taken by the Glasses Through Meta AI with Vision, the glasses can analyze images captured by the built-in camera to provide contextually relevant information. For example, when exploring a new city, users can take photos of landmarks or points of interest, and the glasses will identify and provide relevant details about them.   Llama 3 The newly introduced Llama 3 is the multimodal AI model which is integrated into the Ray-Ban Meta smart glasses. With this multimodal model, Meta offers improved functionality and performance. This AI assistant enhances the overall user experience by enabling more accurate and insightful interactions with the smart glasses. To know more about multimodal models and how to integrate one in your ML data pipelines, sign up for the upcoming webinar: Vision-Language Models: How to Leverage Google Gemini in your ML data pipelines   Aesthetic Styles In addition to feature updates, Ray-Ban Meta smart glasses introduce new styles to cater to a larger audience. These wearable AI-powered glasses include vintage-inspired designs and modern aesthetics, so that users can find a pair that compliments their personal style while enjoying the benefits of advanced technology. Meta AI's Ray-Ban Smart Glasses: Key Takeaways In conclusion, the Ray-Ban Meta smart glasses are revolutionizing wearable technology, starting their beta program in the US and Canada for early access. Featuring multimodal AI features, these glasses enable hands-free video calling, and enhanced user interaction. Truly, they are redefining our interaction with the world.👓

Phi-3 is a family of open artificial intelligence models developed by Microsoft. These models have quickly gained popularity for being the most capable and cost-effective small language models (SLMs) available. The Phi-3 models, including Phi-3-mini, are cost-effective and outperform models of the same size and even the next size across various benchmarks of language, reasoning, coding, and math. Let’s discuss how these models in detail. What are Small Language Models (SLM)? Small Language Models (SLMs) refer to scaled-down versions of large language models (LLMs) like OpenAI’s GPT, Meta’s  LLama-3, Mistral 7B, etc. These models are designed to be more lightweight and efficient both in terms of computational resources for training and inference for simpler tasks and in their memory footprint.  The “small” in SLMs refers to the number of parameters that the model has. These models are typically trained on a large corpus of high-quality data and learn to predict the next work in a sentence, which allows them to generate coherent and contextually relevant sentences. These lightweight AI models are typically used in scenarios where computational resources are limited or where real-time inference is necessary. They sacrifice some performance and capabilities compared to their larger counterparts but still provide valuable language understanding and generation capabilities. SLMs find applications in various fields such as mobile devices, IoT devices, edge computing, and scenarios that have low-latency interactions. They allow for more widespread deployment of natural language processing capabilities in resource-constrained environments. Microsoft's Phi-3 is a prime example of an SLM that pushes the boundaries of what's possible with these models, offering superior performance across various benchmarks while being cost-effective. Phi-3: Introducing Microsoft’s SLM Tech giant Microsoft launches Phi-3, a Small Language Model (SLM) designed to deliver great performance while remaining lightweight enough to run on resource-constrained devices like smartphones. With an impressive 3.8 billion parameters, Phi-3 represents a significant milestone in compact language modeling technology. Prioritizing techniques in dataset curation and model architecture, Phi-3 achieves competitive performance comparable to much larger models like Mixtral 8x7B and GPT-3.5.  Performance Evaluation Phi-3's performance is assessed through rigorous evaluation against academic benchmarks and internal testing. Despite its smaller size, Phi-3 demonstrates impressive results, achieving 69% on the MMLU benchmark and 8.38 on the MT-bench metric. Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone When comparing the performance of Phi-3 with GPT-3.5, a Large Language Model (LLM), it's important to consider the tasks at hand. For many language, reasoning, coding, and math benchmarks, Phi-3 models have been shown to outperform models of the same size and those of the next size up, including GPT-3.5. Phi-3 Architecture Phi-3 is a transformer decoder architecture with a default context length of 4K, ensuring efficient processing of input data while maintaining context awareness. Phi-3 also offers a long context version, Phi-3-mini-128K, extending context length to 128K for handling tasks requiring broader context comprehension. With 32 heads and 32 layers, Phi-3 balances model complexity with computational efficiency, making it suitable for deployment on mobile devices. Microsoft Phi-3: Model Training Process The model training process for Microsoft's Phi-3 has a comprehensive approach: High-Quality Data Training Phi-3 is trained using high-quality data curated from various sources, including heavily filtered web data and synthetic data. This meticulous data selection process ensures that the model receives diverse and informative input to enhance its language understanding and reasoning capabilities. Extensive Post-training Post-training procedures play a crucial role in refining Phi-3's performance and ensuring its adaptability to diverse tasks and scenarios. Through extensive post-training techniques, including supervised fine-tuning and direct preference optimization, Phi-3 undergoes iterative improvement to enhance its proficiency in tasks such as math, coding, reasoning, and conversation. Reinforcement Learning from Human Feedback (RLHF) Microsoft incorporates reinforcement learning from human feedback (RLHF) into Phi-3's training regime. This mechanism allows the model to learn from human interactions, adapting its responses based on real-world feedback. RLHF enables Phi-3 to continuously refine its language generation capabilities, ensuring more contextually appropriate and accurate responses over time. If you are looking to integrate RLHF into your ML pipeline, read the blog Top Tools for RLHF to find the right tools for your project.   Automated Testing Phi-3's training process includes rigorous automated testing procedures to assess model performance and identify potential areas for improvement. Automated testing frameworks enable efficient evaluation of Phi-3's functionality across various linguistic tasks and domains, facilitating ongoing refinement and optimization. Manual Red-teaming In addition to automated testing, Phi-3 undergoes manual red-teaming, wherein human evaluators systematically analyze model behavior and performance. This manual assessment process provides valuable insights into Phi-3's strengths and weaknesses, guiding further training iterations and post-training adjustments to enhance overall model quality and reliability. Advantages of Phi-3: SLM Vs. LLM Small Language Model (SLM), offers several distinct advantages over traditional Large Language Models (LLMs), highlighting its suitability for a variety of applications and deployment scenarios. Resource Efficiency: SLMs like Phi-3 are designed to be more resource-efficient compared to LLMs. With its compact size and optimized architecture, Phi-3 consumes fewer computational resources during both training and inference, making it ideal for deployment on resource-constrained devices such as smartphones and IoT devices. Size and Flexibility: Phi-3-mini, a 3.8B language model, is available in two context-length variants—4K and 128K tokens1. It is the first model in its class to support a context window of up to 128K tokens, with little impact on quality. Instruction-tuned: Phi-3 models are instruction-tuned, meaning that they’re trained to follow different types of instructions reflecting how people normally communicate. Scalability: SLMs like Phi-3 offer greater scalability compared to LLMs. Their reduced computational overhead allows for easier scaling across distributed systems and cloud environments, enabling seamless integration into large-scale applications with high throughput requirements. Optimized for Various Platforms: Phi-3 models have been optimized for ONNX Runtime with support for Windows DirectML along with cross-platform support across GPU, CPU, and even mobile hardware. While LLMs will still be the gold standard for solving many types of complex tasks, SLMs like Phi-3 offer many of the same capabilities found in LLMs but are smaller in size and are trained on smaller amounts of data. For more information about the Phi-3 models, read the technical report Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.   Quality Vs. Model Size Comparison In the trade-off between model size and performance quality, Phi-3 claims remarkable efficiency and effectiveness compared to larger models. Performance Parity Despite its smaller size, Phi-3 achieves performance parity with larger LLMs such as Mixtral 8x7B and GPT-3.5. Through innovative training methodologies and dataset curation, Phi-3 delivers competitive results on benchmark tests and internal evaluations, demonstrating its ability to rival larger models in terms of language understanding and generation capabilities. Optimized Quality Phi-3 prioritizes dataset quality optimization within its constrained parameter space, leveraging advanced training techniques and data selection strategies to maximize performance. By focusing on the quality of data and training processes, Phi-3 achieves impressive results that are comparable to, if not surpass, those of larger LLMs. Efficient Utilization Phi-3 shows efficient utilization of model parameters, demonstrating that superior performance can be achieved without exponentially increasing model size. By striking a balance between model complexity and resource efficiency, Phi-3 sets a new standard for small-scale language modeling, offering a compelling alternative to larger, more computationally intensive models. Quality of Phi-3 models’s performance on MMLU benchmark compared to other models of similar size Client Success Case Study Organizations like ITC, a leading business in India, are already using Phi-3 models to drive efficiency in their solutions. ITC's collaboration with Microsoft on the Krishi Mitra copilot, a farmer-facing app, showcases the practical impact of Phi-3 in agriculture. By integrating fine-tuned versions of Phi-3, ITC aims to improve efficiency while maintaining accuracy, ultimately enhancing the value proposition of their farmer-facing application. For more information, read the blog Generative AI in Azure Data Manager for Agriculture.   Limitations of Phi-3 The limitations of Phi-3, despite its impressive capabilities, are primarily from its smaller size compared to larger Language Models (LLMs): Limited Factual Knowledge Due to its limited parameter space, Phi-3-mini may struggle with tasks that require extensive factual knowledge, as evidenced by lower performance on benchmarks like TriviaQA. The model's inability to store vast amounts of factual information poses a challenge for tasks reliant on deep factual understanding. Language Restriction Phi-3-mini primarily operates within the English language domain, which restricts its applicability in multilingual contexts. While efforts are underway to explore multilingual capabilities, such as with Phi-3-small and the inclusion of more multilingual data, extending language support remains an ongoing challenge. Dependency on External Resources To compensate for its capacity limitations, Phi-3-mini may rely on external resources, such as search engines, to augment its knowledge base for certain tasks. While this approach can alleviate some constraints, it introduces dependencies and may not always guarantee optimal performance. Challenges in Responsible AI (RAI) Like many LLMs, Phi-3 faces challenges related to responsible AI practices, including factual inaccuracies, biases, inappropriate content generation, and safety concerns. Despite diligent efforts in data curation, post-training refinement, and red-teaming, these challenges persist and require ongoing attention and mitigation strategies. For more information on Microsoft’s responsible AI practices, read Microsoft Responsible AI Standard, v2.   Phi-3 Availability The first model in this family, Phi-3-mini, a 3.8B language model, is now available. It is available in two context-length variants—4K and 128K tokens. The Phi-3-mini is available on Microsoft Azure AI Model Catalog, Hugging Face, and Ollama. It has been optimized for ONNX Runtime with support for Windows DirectML along with cross-platform support across graphics processing unit (GPU), CPU, and even mobile hardware.  In the coming weeks, additional models will be added to the Phi-3 family to offer customers even more flexibility across the quality-cost curve Phi-3-small (7B) and Phi-3-medium (14B) will be available in the Azure AI model catalog, and other model families shortly. It will also be available as an NVIDIA NIM microservice with a standard API interface that can be deployed anywhere.  Phi-3: Key Takeaways Microsoft's Phi-3 models are small language models (SLMs) designed for efficiency and performance, boasting 3.8 billion parameters and competitive results compared to larger models. Phi-3 utilizes high-quality curated data and advanced post-training techniques, including reinforcement learning from human feedback (RLHF), to refine its performance. Its transformer decoder architecture ensures efficiency and context awareness. Phi-3 offers resource efficiency, scalability, and flexibility, making it suitable for deployment on resource-constrained devices. Despite its smaller size, it achieves performance parity with larger models through dataset quality optimization and efficient parameter utilization. While Phi-3 demonstrates impressive capabilities, limitations include limited factual knowledge and language support. It is currently available in its first model, Phi-3-mini, with plans for additional models to be added, offering more options across the quality-cost curve.

In modern AI-driven applications, Machine Learning Operations (MLOps) and Data Operations (DataOps) help manage machine learning and data-related operations. Their contribution through principles, practices, and tools is vital for scaling up ML and data applications.  Data Operations (DataOps) is an automated approach to streamlining and managing data at scale, so it is helpful for downstream tasks. MLOps and DataOps make collaborating easier for teams, automate tasks, manage large datasets, use sophisticated algorithms, and maintain models continuously. They also let teams focus on experimenting and coming up with new ideas. But what makes both processes effective for managing data and scaling ML projects?  It is important to note that DevOps practices have influenced both of these practices, and many approaches are borrowed or transferred from them. For instance, at their core, both rely on robust methodology and components that include version control, continuous integration/continuous deployment (CI/CD), monitoring and observability, and model governance.   Furthermore, both practices prioritize automation, collaboration, and streamlining various operations related to ML model development and data engineering and management.  This article explains the approaches involved in both practices. You will also learn the similarities and differences between both methodologies (MLOps and DataOps). MLOps Methodology MLOps largely depends on a methodology that optimizes the deployment and management of ML models in production environments. It merges machine learning (ML) with DevOps by adopting best practices from software development and operations to efficiently deploy and maintain models in production environments. You can view this methodology in three ways: Problem Definition and Data Acquisition: Identify the problem, gather the data, and design the solution. ML training and development: Train and implement proof of concept (PoC) models. Iteratively evaluate, retrain, and improve them to deliver a stable, high-performing model. Managing ML operations: Deploy, manage, and monitor models in production. This also involves automating experiments with various models, parameters, and new data. MLOps MLOps bridges model development and operations through CI/CD automation, workflow orchestration, collaboration, continuous ML training and evaluation, metadata tracking, monitoring, and feedback loops.  It helps avoid technical debt, ensures reproducibility, complies with governance, scales operations, fosters collaboration, and monitors performance.  ML Lifecycle in MLOps Another important aspect of MLOps is the ML lifecycle. An ML lifecycle is a set of procedures or methods that enables an ML practitioner to develop, deploy, and continuously maintain ML models in real-world settings.  It generally has four phases: Data Collection: This step ensures we collect and prepare data from different sources. Having a dataset from a legitimate source is vital. In addition, ensure that it is well-processed, curated, and ready for training models. Model Training: After that, data is well structured, engineered, and used to train ML models. Training is an iterative process to develop an optimal model. Deployment: Once trained, we deploy the model in real-world settings, where it can make predictions about new data in real-time. Monitoring: After deploying the model, we continuously monitor it to ensure it works well and maintains the expected performance. The monitoring process involves spotting bugs and inconsistencies in the model or changes in data patterns. Monitoring uses performance metrics to track the model's behavior and provide live feedback.  Keeping the above as the building blocks in the ML lifecycle, MLOps emphasizes continuous integration and deployment (CI/CD).  The CI/CD pipeline keeps the lifecycle streamlined and consistent. This allows ML practitioners to innovate and add new features much more quickly. CI/CD involves testing, validating, and deploying models automatically. Technically, it involves: Version Control: It keeps track of changes in code, data, and model parts. This allows you to trace the changes made over time. It also allows you to identify errors and bugs, leading to faster improvements.  Continuous Integration (CI): Automatically validates the codes to detect errors and ensure that the ML applications are production-ready.  Continuous Delivery (CD): Automates the deployment process of ML models to production environments, ensuring that models are deployed quickly and efficiently. Monitoring: This ensures the model and the complete end-to-end pipeline work well.  Now, it is essential to consider the role data plays. An ML model will only perform well if the data is consistent and accurate. For that, we need another set of practices that will allow us to engineer, curate, and analyze data appropriately and efficiently. This is where DataOps comes into the picture, and it is integrated with MLOps.  Integration of MLOps and DataOps DataOps is a process-oriented practice that ensures, maintains, and improves data quality. It is an essential tool when working with big data because it generally contains many inconsistencies and errors, along with vital information. What Is DataOps? DataOps includes various approaches, such as data engineering, quality, security, and integration. Along with these approaches, DataOps leverages principles and tools that allow data engineers and teams to curate and process consistent, well-balanced, and high-quality data for downstream tasks like analytics and ML development. The goal is to automate the data life cycle. It plays a crucial role in upholding the integrity, quality, and security of the data that acts as fuel for data-driven ML models. This ensures cleaner, high-quality data for model training.  MLOps, on the other hand, simplifies machine learning models for better management and logistics between operation teams and researchers. Integrating DataOps and MLOps leads to better-performing and more accurate model development. This enables organizations to improve the quality of production ML, increase automation, and focus on business requirements.   DataOps and MLOps share common steps, including data ingestion, preprocessing, model training, model deployment, and model monitoring. The data (pre-)processing part of MLOps focuses on moving data from the source to the ML model.  Recommended Read: Mastering Data Cleaning & Data Preprocessing.   The following section will discuss seven points highlighting similarities between MLOps and DataOps.  Similarities: MLOps and DataOps We discussed how MLOps and DataOps have much in common in the previous section. Now, let's dig into some of the shared features they both have: Automation: MLOps and DataOps automate processes to execute operations and reduce errors. Automation helps tidy up data pipelines, run ML models, and monitor them once deployed. Collaboration: These technologies emphasize the importance of teamwork. They are designed so that data scientists and engineers can collaborate to achieve a common goal. CI/CD: They both involve CI/CD practices, which means they like to get things out there quickly and update them easily. This is handy for rapidly spinning up data pipelines and training ML models. Model Cataloging and Version Control: MLOps and DataOps keep track of code, metadata, artifacts, etc. They catalog and keep data and ML model versions so everything stays consistent and can be reviewed later. Monitoring: DataOps monitors data pipelines, while MLOps monitors ML models. This helps catch bugs early on and ensures everything runs smoothly.  Governance: Both practices ensure that data is of good quality and safety and that everything follows the rules. This means complying with regulations like GDPR and HIPAA. DevOps Principles: Lastly, they draw inspiration from DevOps, which is all about teamwork, automation, and innovation.  The table below shows similarities between both practices in various aspects.  Differentiating MLOps and DataOps  Though these two fields share similar approaches, they each have their objective within the machine learning workflows.  Take DataOps, for instance. It's all about managing and delivering data. This involves improving data quality, streamlining data processes, etc. DataOps tools like Encord, Apache Airflow, Jenkins, Luigi, etc., orchestrate and automate data pipelines, perform data profiling, and check version control. Now, when it comes to MLOps, it is more about getting the ML models up and running efficiently in the real world. It also involves training, version control, monitoring, and fine-tuning performance. MLOps provides frameworks such as TensorFlow, PyTorch, or Keras to help with that. Automation tools like Neptune.ai, WandB, H2O.ai, DataRobot, etc. allow data scientists and ML engineers to monitor and track every component.  The table below compares the differences between both practices in various aspects. DataOps vs. MLOps: Which One Should You Choose? The choice between MLOps and DataOps largely depends on the specific focus and objectives of your project: Choose MLOps if: You are primarily concerned with developing, deploying, and managing production ML models, including overseeing the ML lifecycle.  Suppose you aim to perform one or all operations, such as efficiently deploying, monitoring, and maintaining ML models, focusing on aspects like version control, continuous integration/deployment, monitoring, and model governance. In that case, you should opt for MLOps. You want to streamline the ML lifecycle, automate experiments, ensure reproducibility, and scale operations efficiently. You want to scale the ML project. Because the ML lifecycle gets complicated as the size of the project scales up. The size affects how complicated your data processes are, how many models you handle, and how much automation and monitoring you need. For big projects, MLOps gives you a structured way to manage your ML models. This includes controlling versions, monitoring, and making sure they work well. Even with big and complex tasks, MLOps helps keep your models scalable, reproducible, and robust. Choose DataOps if: Your primary focus is collecting, managing, and delivering data within your organization. You aim to improve data quality, streamline processes, and optimize data delivery for downstream tasks (production models, business intelligence, analytics, etc.). You want to automate data pipelines, improve data quality, and ensure consistent, high-quality data for downstream tasks. You are working with big data or if you want to scale up the data streamlining process. Because DataOps focuses on managing data pipelines efficiently, making sure the data is good and easy to operate and access. When working with big data, you need to choose the right tools and ways to handle data input, change, and quality checks. With big data and larger projects, DataOps ensures your data pipelines can scale up, stay reliable, and keep data quality high throughout the process. DataOps Vs. MLOps: Key Takeaways In this article, we explored the various aspects of MLOps and dataOps. We studied the similarities and differences, the benefits of integrating both disciplines and which one to choose.  Integrating both can add value to the data and ML projects, as well as teams building data-intensive production ML applications.  See Also: Top 8 Use Cases of Computer Vision in Manufacturing.   Here is a summary of all that we covered in this article comparing DataOps and MLOps.  DataOps:  Focuses on improving data quality through methodologies like data engineering, quality assurance, and security measures. Aims to streamline data operations from end to end. MLOps, on the other hand: Bridges the gap between ML model development and deployment. Combines ML with DevOps, which includes designing ML-powered applications, experimentation and development, and ML operation.  Can be executed in four phases: Data Collection, Model Training, Deployment, and Monitoring.  We also covered the similarities of both MLOps and DataOps, which, in a nutshell:  Automate operations and create streamlined workflows. Focus on collaboration, workflow orchestration, monitoring, and version control. When it comes to the differences: MLOps offers tools for building, deploying, and monitoring ML models. DataOps offers tools for data engineering and managing datasets. Lastly, implementing both disciplines can be challenging due to the complexity of managing the machine learning lifecycle from experimentation to production and governing data quality and security.