If you are interested in learning more about machine learning inference on the recently launched Raspberry Pi Pico microcontroller, you may be interested in a new project published to the Hackster.io Classed as an intermediate skill level project and taking approximately 60 minutes, Maslov covers the basics of setting up a Seeed Grove Shield for Pi Pico v1.0 and Edge Impulse. Edge Impulse is a platform that enables developers to easily train and deploy deep learning models on embedded devices. Check out the video below to learn more. "This is another article in know-how series, which focuses solely on a specific feature or technique and today Iíll tell you how to use neural network trained with Edge Impulse with new Raspberry Pico 2040. Also make sure to watch the tutorial video with step-by-step instructions."

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. Typically, you can use the pre-built and optimized training and inference containers that have been optimized for AWS hardware. Although those containers cover many deep learning workloads, you may have use cases where you want to use a different framework or otherwise customize the contents of your OS libraries within the container. To accommodate this, SageMaker provides the flexibility to train models using any framework that can run in a Docker container. This functionality enables you to use existing SageMaker training capabilities such as training jobs, hyperparameter tuning, and Managed Spot Training.

Deep Learning and Machine Learning models trained by many data professionals either end up in an inference.ipynb Those meticulous model architectures capable of creating awe in the real world never see the light of the day. Those models just sit there in the background processing requests via an API gateway doing their job silently and making the system more intelligent. People using those intelligent systems don't always credit the Data Professionals who spent hours or weeks or months collecting data, cleaning the collected data, formatting the data to use it correctly, writing the model architecture, training that model architecture and validating it. And if the validation metrics are not very good, again going back to square one and repeating the cycle.

The idea of a system that can learn from data, identify patterns and make decisions with minimal human intervention is exciting. Deep learning, a type of machine learning that uses neural networks is quickly becoming an effective tool to solve many different computing problems from object classification to recommendation systems. However, getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail. In this blog, we will explore how to navigate these challenges and deploy deep learning models in production in data center or cloud.

IBM on Wednesday is announcing significant updates to PowerAI, its deep learning software distribution package, making it faster for data scientists to deploy deep learning models and easier for developers to integrate computer vision into their applications. Analysts say that artificial intelligence has reached a tipping point where it's being integrated into just about every service, product, or integration, but there are still major challenges for the data scientists and developers interested in exploiting AI. Some sectors like financial services have had data scientists on staff for at least five or 10 years, but they've only recently started deploying deep learning methods. PowerAI "gives them these higher level tools that much it make easier and automated," IBM VP Sumit Gupta told ZDNet. "You still have data scientists guiding the whole process, but we're removing some of the steps."