GOOGLE CLOUD SERVICES

It offers the Google Cloud AI Platform as its core DSML platform. The platform has an expanded suite of components that includes Cloud Data Fusion, Cloud AutoML, BigQuery ML, AI Platform Notebooks and TensorFlow. Google will launch its unified AI Platform in the first quarter of 2021 (after the cut-off date for evaluation in this Best Cloud Provider). Key features and services that will be released with this new platform include AutoML tables, XAI, AI platform pipelines and other MLOps services.

Google is geographically diversified and its client base spans many industries and various business functions.

Google’s Completeness of Vision is boosted by thought leadership in ML research and responsible AI, as well as by the roadmap for its unified AI Platform. The coherence of, and learning curve for, Google’s platform are key aspects to monitor in the coming year.

Strengths

• Responsible AI vision and capabilities: Google has taken a clear thought leadership position in the area of AI explainability and responsibility. Google shares and productizes its learnings on these subjects through responsible AI practices, fairness best practices, technical references and other materials.

• Research contributions and impact: Google’s leadership in AI research includes the prominent work of Google Research, Google Brain and DeepMind, as well as ongoing significant contributions to scholarship, open-source projects and communities — TensorFlow, Kubernetes/Kubeflow and Kaggle stand out.

• Consolidation, cohesion and simplification: Google has made a significant effort to reorganize and redesign not just its DSML platform, but also the way it releases software. The unified AI Platform will seek to address past issues of coherence, interoperability and ease of use. Google has also introduced simplified New Product Introduction (NPI) stages to provide more predictability and transparency about launch timelines.

Cautions

•  Transition of portfolio: Google is developing capabilities for data science professionals at a rapid pace. This means a period of transition and learning for the market in general and adopters of its unified AI Platform in particular. Google’s new product release standards and timelines will be put to the test in 2021.

• Steepness of learning curve: Although Google has made improvements in terms of accessibility and augmentation, its platform presents a steep learning curve and requires technical expertise. Supplementary tools for citizen data scientists and developers new to ML may be necessary.

• Maturing on-premises, hybrid and multicloud support: The majority of Cloud AI Platform customers operate in purely cloud environments. Some capabilities of the Cloud AI Platform change and may become more complicated in hybrid, multicloud or on-premises environments. Multicloud support is evolving, and today most customers manage data, models and ML workloads within Google Cloud. New services like BigQuery Omni for viewing data across clouds are indicative of Google’s next steps in the multicloud field.

VERTEX AI 

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform. 

• Build with the groundbreaking ML tools that power Google, developed by Google Research
• Deploy more models, faster, with 80% fewer lines code required for custom modeling
• Use MLOps tools to easily manage your data and models with confidence and repeat at scale

Train models without code, minimal expertise required

Take advantage of AutoML to build models in less time. Use Vertex AI with state-of-the-art, pre-trained APIs for computer vision, language, structured data, and conversation.

Build advanced ML models with custom tooling

Vertex AI’s custom model tooling supports advanced ML coding, with nearly 80% fewer lines of code required to train a model with custom libraries than competitive platforms.

Manage your models with confidence

Vertex AI's MLOps tools remove the complexity of model maintenance, such as Vertex AI Pipelines, to streamline running ML pipelines, and Vertex AI Feature Store to serve, and use AI technologies as ML features.

Key Features of Vertex AI

One AI platform, every ML tool you need

A unified UI for the entire ML workflow

Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. In Vertex AI, you can now easily train and compare models using AutoML or custom code training and all your models are stored in one central model repository. These models can now be deployed to the same endpoints on Vertex AI.

Pre-trained APIs for vision, video, natural language, and more

Easily infuse vision, video, translation, and natural language ML into existing applications or build entirely new intelligent applications across a broad range of use cases (including Translation and Speech to Text). AutoML enables developers to train high-quality models specific to their business needs with minimal ML expertise or effort. With a centrally managed registry for all datasets across data types (vision, natural language, and tabular).

End-to-end integration for data and AI

Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection.

Support for all open source frameworks

Vertex AI integrates with widely used open source frameworks such as TensorFlow, PyTorch, and scikit-learn, along with supporting all ML frameworks and artificial intelligence branches via custom containers for training and prediction.

DATA LABELLING WITH VERTEX AI

Vertex AI enables you to request human labeling for a collection of data that you plan to use to train a custom machine learning model. Prices for the service are computed based on the type of labeling task.

The quality of your training data strongly affects the effectiveness of the model you create, and by extension, the quality of the predictions returned from that model. The key to high-quality training data is ensuring that you have training items that accurately represent the domain you want to make predictions about and that the training items are accurately labeled.

There are three ways to assign labels to your training data items:

• Add the data items to your dataset with their labels already assigned, for example using a commercially available dataset
• Assign labels to the data items using the Cloud Console
• Request to have human labelers add labels to the data items

Vertex AI data labeling tasks let you work with human labelers to generate highly accurate labels for a collection of data that you can use to train your machine learning models.

VERTEX AI WORKBENCH 

The single development environment for the entire data science workflow. 

• Natively analyze your data with a reduction in context switching between services
• Data to training at scale. Build and train models 5X faster, compared to traditional notebooks
• Scale up model development with simple connectivity to Vertex AI services

Benefits

Easy exploration and analysis

Simplified access to data and in-notebook access to machine learning with BigQuery, Dataproc, Spark, and Vertex AI integration.

Rapid prototyping and model development

Take advantage of the power of infinite compute with Vertex AI training for experimentation and prototyping, to go from data to training at scale.

End-to-end notebook workflows

Using Vertex AI Workbench you can implement your training, and deployment workflows on Vertex AI from one place

Key features

Fully managed compute

A Jupyter-based fully managed, scalable, enterprise-ready compute infrastructure with security controls and user management capabilities.

Interactive data and ML experience

Explore data and train ML models with easy connections to Google Cloud's big data solutions.

Portal to complete end-to-end ML training

Develop and deploy AI solutions on Vertex AI with minimal transition.

Train high-quality custom machine learning models with minimal effort and machine learning expertise.

GOOGLE VISION AI

Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more.

• Use machine learning to understand your images with industry-leading prediction accuracy
• Train machine learning models that classify images by your custom labels using AutoML Vision
• Detect objects and faces, read handwriting, and build valuable image metadata with Vision API

Benefits

Detect objects automatically

Detect and classify multiple objects including the location of each object within the image. Learn more about object detection with Vision API and AutoML Vision.

Gain intelligence at the edge

Use AutoML Vision Edge to build and deploy fast, high-accuracy models to classify images or detect objects at the edge, and trigger real-time actions based on local data. Learn more.

Reduce purchase friction

With Vision API’s vision product search, retailers can create an engaging mobile experience that enables customers to upload a photo of an item and immediately see a list of similar items for purchase.

KEY FEATURES

Two computer vision products to help you understand images

AutoML Vision

Automate the training of your own custom machine learning models. Simply upload images and train custom image models with AutoML Vision’s easy-to-use graphical interface; optimize your models for accuracy, latency, and size; and export them to your application in the cloud or to an array of devices at the edge.

Vision API

Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. Assign labels to images and quickly classify them into millions of predefined categories. Detect objects and faces, read printed and handwritten text, and build valuable metadata into your image catalog.

GOOGLE NATURAL LANGUAGE AI - NLP

Derive insights from unstructured text using Google machine learning.

• Get insightful text analysis with machine learning that extracts, analyzes, and stores text
• Train high-quality machine learning custom models without a single line of code with AutoML
• Apply natural language understanding (NLU) to apps with Natural Language API

Benefits

Insights from customers

Use entity analysis to find and label fields within a document—including emails, chat, and social media—and then sentiment analysis to understand customer opinions to find actionable product and UX insights.

Multimedia and multilingual support

Natural Language with Speech-to-Text API extracts insights from audio. Vision API adds optical character recognition (OCR) for scanned docs. Translation API understands sentiments in multiple languages.

Extract key document entities that matter

Use custom entity extraction to identify domain-specific entities within documents—many of which don’t appear in standard language models—without having to spend time or money on manual analysis.

Key Features

Three natural language solutions that work with your text

AutoML

Train your own high-quality machine learning custom models to classify, extract, and detect sentiment with minimum effort and machine learning expertise using Vertex AI for natural language, powered by AutoML. You can use the AutoML UI to upload your training data and test your custom model without a single line of code.

Natural Language API

The powerful pre-trained models of the Natural Language API empowers developers to easily apply natural language understanding (NLU) to their applications with features including sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis.

Healthcare Natural Language AI

Gain real-time analysis of insights stored in unstructured medical text. Healthcare Natural Language API allows you to distill machine-readable medical insights from medical documents, while AutoML Entity Extraction for Healthcare makes it simple to build custom knowledge extraction models for healthcare and life sciences apps—no coding skills required.

Build your deep learning project fast on Google Cloud

Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. 

Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn and more. You can also easily add Cloud GPU and Cloud TPU support.

Key Features of Deep Learning VM Image

*Broad support

Deep Learning VM Image supports the most popular and latest machine learning frameworks, like TensorFlow and PyTorch.

*Optimized for performance

To accelerate your model training and deployment, Deep Learning VM Images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers and the Intel® Math Kernel Library.

*Fast prototyping

Get started immediately with all the required frameworks, libraries, and drivers pre-installed and tested for compatibility.

*Integrated notebook experience

Deep Learning VM Image delivers a seamless notebook experience with integrated support for JupyterLab, the latest web-based interface for Project Jupyter, the de facto standard of interactive environments for running machine learning experiments.

GOOGLE DEEP LEARNING CONTAINERS 

Preconfigured and optimized containers for deep learning environments.

Build your deep learning project quickly on Google Cloud

Quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications with Deep Learning Containers. These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), Vertex AI, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.

GOOGLE TENSORFLOW ENTERPRISE 

Reliability and performance for AI applications with enterprise-grade support and managed services.

• Only offering brought to you by the creators of TensorFlow
• Scale resources across CPUs, GPUs, and record-setting Cloud TPUs
• Boost enterprise development with long-term support on specific distributions

Benefits 

Enterprise-grade support

The best of TensorFlow meets the best of Google Cloud. Ensure your TensorFlow project’s success with enterprise-ready services and support.

Cloud-scale performance

Accelerate and scale your ML workflows on the cloud with compatibility-tested and optimized TensorFlow.

Managed services

Develop and deploy TensorFlow across managed services like Vertex AI and Google Kubernetes Engine.

Key features

Long-term support

Security patches and select bug fixes for up to three years on specific TensorFlow Enterprise distributions.

Prioritized requests

Prioritized patches and bug fixes into the mainline TensorFlow code repository.

Instant cloud scale

Automatic provisioning, optimizing, and scaling of resources across CPUs, GPUs, and Cloud TPUs.