Achieving Next-Level Value From AI By Focusing On The Operational Side Of Machine Learning Forbes
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Achieving Next-Level Value From AI By Focusing On The Operational Side Of Machine Learning - Forbes
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Deep Learning | NVIDIA Developer
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Deep learning is a subset of AI and machine learning that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation, and others.
Deep learning differs from traditional machine learning techniques in that they can automatically learn representations from data such as images, video or text, without introducing hand-coded rules or human domain knowledge. Their highly flexible architectures can learn directly from raw data and can increase their predictive accuracy when provided with more data.
Deep learning is commonly used across apps in computer vision, conversational AI and recommendation systems. Computer vision apps use deep learning to gain knowledge from digital images and videos. Conversational AI apps help computers understand and communicate through natural language. Recommendation systems use images, language, and a users interests to offer meaningful and relevant search results and services.
Deep learning has led to many recent breakthroughs in AI such as Google DeepMinds AlphaGo, self-driving cars, intelligent voice assistants and many more. With NVIDIA GPU-accelerated deep learning frameworks, researchers and data scientists can significantly speed up deep learning training, that could otherwise take days and weeks to just hours and days. When models are ready for deployment, developers can rely on GPU-accelerated inference platforms for the cloud, embedded device or self-driving cars, to deliver high-performance, low-latency inference for the most computationally-intensive deep neural networks.
Developing AI applications start with training deep neural networks with large datasets. GPU-accelerated deep learning frameworks offer flexibility to design and train custom deep neural networks and provide interfaces to commonly-used programming languages such as Python and C/C++. Every major deep learning framework such as PyTorch, TensorFlow, JAX and others, are already GPU-accelerated, so data scientists and researchers can get productive in minutes without any GPU programming.
For AI researchers and application developers, NVIDIA Hopper and Ampere GPUs powered by tensor cores give you an immediate path to faster training and greater deep learning performance. With Tensor Cores enabled, FP32 and FP16 mixed precision matrix multiply dramatically accelerates your throughput and reduces AI training times.
For developers integrating deep neural networks into their cloud-based or embedded application, Deep Learning SDK includes high-performance libraries that implement building block APIs for implementing training and inference directly into their apps. With a single programming model for all GPU platform - from desktop to datacenter to embedded devices, developers can start development on their desktop, scale up in the cloud and deploy to their edge devices - with minimal to no code changes.
NVIDIA provides optimized software stacks to accelerate training and inference phases of the deep learning workflow. Learn more on the links below.
For developers looking to build deep learning applications, NVIDIA Pretrained AI models eliminate the need of building models from scratch or experimenting with other open source models that fail to converge. These models are pretrained on high quality representative datasets to deliver state-of-the-art performance and production readiness for a variety of use cases like computer vision, speech AI, robotics, natural language processing, healthcare, cybersecurity, and many others.
Deep learning frameworks offer building blocks for designing, training and validating deep neural networks, through a high level programming interface. Every major deep learning framework such as PyTorch, TensorFlow, and JAX rely on Deep Learning SDK libraries to deliver high-performance multi-GPU accelerated training. As a framework user, its as simple as downloading a framework and instructing it to use GPUs for training. Learn more about deep learning frameworks and explore these examples to getting started quickly.
Tensor Core Optimized Model Scripts
Deep learning frameworks are optimized for every GPU platform from Titan V desktop developer GPU to data center grade Tesla GPUs. This allows researchers and data scientist teams to start small and scale out as data, number of experiments, models and team size grows. Since Deep Learning SDK libraries are API compatible across all NVIDIA GPU platforms, when a model is ready to be integrated into an application, developers can test and validate locally on the desktop, and with minimal to no code changes validate and deploy to Tesla datacenter platforms, Jetson embedded platform or DRIVE autonomous driving platform. This improves developer productivity and reduces chances of introducing bugs when going from prototype to production.
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Deep Learning | NVIDIA Developer
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Machine learning operations offer agility, spur innovation – MIT Technology Review
The main function of MLOps is to automate the more repeatable steps in the ML workflows of data scientists and ML engineers, from model development and training to model deployment and operation (model serving). Automating these steps creates agility for businesses and better experiences for users and end customers, increasing the speed, power, and reliability of ML. These automated processes can also mitigate risk and free developers from rote tasks, allowing them to spend more time on innovation. This all contributes to the bottom line: a 2021 global study by McKinsey found that companies that successfully scale AI can add as much as 20 percent to their earnings before interest and taxes (EBIT).
Its not uncommon for companies with sophisticated ML capabilities to incubate different ML tools in individual pockets of the business, says Vincent David, senior director for machine learning at Capital One. But often you start seeing parallelsML systems doing similar things, but with a slightly different twist. The companies that are figuring out how to make the most of their investments in ML are unifying and supercharging their best ML capabilities to create standardized, foundational tools and platforms that everyone can use and ultimately create differentiated value in the market.
In practice, MLOps requires close collaboration between data scientists, ML engineers, and site reliability engineers (SREs) to ensure consistent reproducibility, monitoring, and maintenance of ML models. Over the last several years, Capital One has developed MLOps best practices that apply across industries: balancing user needs, adopting a common, cloud-based technology stack and foundational platforms, leveraging open-source tools, and ensuring the right level of accessibility and governance for both data and models.
ML applications generally have two main types of userstechnical experts (data scientists and ML engineers) and nontechnical experts (business analysts)and its important to strike a balance between their different needs. Technical experts often prefer complete freedom to use all tools available to build models for their intended use cases. Nontechnical experts, on the other hand, need user-friendly tools that enable them to access the data they need to create value in their own workflows.
To build consistent processes and workflows while satisfying both groups, David recommends meeting with the application design team and subject matter experts across a breadth of use cases. We look at specific cases to understand the issues, so users get what they need to benefit their work, specifically, but also the company generally, he says. The key is figuring out how to create the right capabilities while balancing the various stakeholder and business needs within the enterprise.
Collaboration among development teamscritical for successful MLOpscan be difficult and time-consuming if these teams are not using the same technology stack. A unified tech stack allows developers to standardize, reusing components, features, and tools across models like Lego bricks. That makes it easier to combine related capabilities so developers dont waste time switching from one model or system to another, says David.
A cloud-native stackbuilt to take advantage of the cloud model of distributed computingallows developers to self-service infrastructure on demand, continually leveraging new capabilities and introducing new services. Capital Ones decision to go all-in on the public cloud has had a notable impact on developer efficiency and speed. Code releases to production now happen much more rapidly, and ML platforms and models are reusable across the broader enterprise.
Open-source ML tools (code and programs freely available for anyone to use and adapt) are core ingredients in creating a strong cloud foundation and unified tech stack. Using existing open-source tools means the business does not need to devote precious technical resources to reinventing the wheel, quickening the pace at which teams can build and deploy models.
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Machine learning operations offer agility, spur innovation - MIT Technology Review