Training a model on Google’s AI Platform

Welcome to the first article in this series about doing Machine Learning stuff on the Google Cloud Platform!

We will take a look at the AI Platform. It is a subset of tools strictly related to Machine Learning, among which:

  • AI Platform Training, for training/tuning models on the cloud
  • AI Platform Prediction, to host trained models on the cloud
  • AI Pipelines, to create a step-by-step process using Kubernetes and Docker Images

and many others.

DISCLAIMER: I am not affiliated with Google in any way, I simply decided to

write these articles to share the knowledge I acquired using these tools in my

daily job.

For this first article, I’ll focus on AI Platform Training, a product to run

training jobs on the Cloud with custom code and customizable machines. I think the main advantages of using the AI Platform to train your models are:

  • you can use more powerful resources (like multiple cores or a GPU) without a lot of hassle to instantiate them
  • you can share the code with your team and reproduce the same results using a common Cloud infrastructure

#data-science #google #cloud-computing #google-ml-tutorials #machine-learning

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Training a model on Google’s AI Platform
Virgil  Hagenes

Virgil Hagenes


Google Announces General Availability Of AI Platform Prediction

Recently, the developers at Google Cloud announced the general availability of the AI Platform Prediction. The platform is based on a Google Kubernetes Engine (GKE) backend and is said to provide an enterprise-ready platform for hosting all the transformative ML models.

Emerging technologies like machine learning and AI have transformed the way most processes and industries work around us. Machine learning has brought various significant features that require predictions, such as identifying objects in images, recommending products, optimising market campaigns and more.

However, building a robust and enterprise-ready machine learning environment can include various issues like it being time-consuming, costly as well as complex. Google’s AI Platform Prediction takes into account all these issues to provide a robust environment for ML-based tasks.

In March this year, the tech giant launched the AI Platform Pipelines in beta version to ensure in delivering an enterprise-ready and a secure execution environment for the machine learning workflows.

According to the developers, this new platform is designed for various functions in machine learning models such as improved reliability, more flexibility via new hardware options such as Compute Engine machine types and NVIDIA accelerators, reduced overhead latency, and improved tail latency.m.

#google ai #google ai platform #google kubernetes #ai

Mckenzie  Osiki

Mckenzie Osiki


Inside MoveNet, Google’s Latest Pose Detection Model

Ahead of Google I/O, Google Research launched a new pose detection model in TensorFlow.js called MoveNet. This ultra-fast and accurate model can detect 17 key points in the human body. MoveNet is currently available on TF Hub with two variants — Lightning and Thunder.

While Lightning is intended for latency-critical applications, Thunder is for applications that call for higher accuracy. Both models claim to run faster than real-time (30+ frames per second (FPS)) on most personal computers, laptops and phones.

The model can be launched in the browser using TensorFlow.js architecture with no server calls needed after the initial page load or external packages. The live demo version is available here.

Currently, the MoveNet model works for the individual in the camera field-of-view. But, soon, Google Research looks to extend the MoveNet model to the multi-person domain so that developers can support applications with multiple people.

#developers corner #body movements online #body movements virtual #fitness machine learning #google i/o #google latest #google new development #google research latest #machine learning models body poses #ose detection model #remote healthcare solutions #tensorflow latest model #track body movements #wellness machine learning

Edna  Bernhard

Edna Bernhard


Google’s New AI-Enabled Flood Alert Model For India & Bangladesh

Recently, Google launched a new forecasting model that will allow doubling the lead time of its alerts. The model is claimed to provide more notice to governments and giving tens of millions of people an extra day or so to prepare.

The model supports Hindi, Bengali and seven other local languages. With the help of this new model, the tech giant will provide people with information about flood depth, such as when and how much flood-waters are likely to rise. The information is provided in various formats so that people can both read their alerts and see them presented visually.

#flood forecasting #forecasting #forecasting model #google ai #google india #ai

What Is Google’s Recently Launched BigBird

Recently, Google Research introduced a new sparse attention mechanism that improves performance on a multitude of tasks that require long contexts known as BigBird. The researchers took inspiration from the graph sparsification methods.

They understood where the proof for the expressiveness of Transformers breaks down when full-attention is relaxed to form the proposed attention pattern. They stated, “This understanding helped us develop BigBird, which is theoretically as expressive and also empirically useful.”

Why is BigBird Important?
Bidirectional Encoder Representations from Transformers or BERT, a neural network-based technique for natural language processing (NLP) pre-training has gained immense popularity in the last two years. This technology enables anyone to train their own state-of-the-art question answering system.

#developers corner #bert #bert model #google #google ai #google research #transformer #transformer model

Jon  Gislason

Jon Gislason


Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus