As Google Cloud Prediction API is deprecated (https://cloud.google.com/prediction/docs/end-of-life-faq), does ml-engine provide a similar black-box?
Google Cloud ML Engine is managed TensorFlow and supports higher level APIs (see Datalab notebooks for regression and image classification - runnable in Datalab). Compared to Prediction API, there are some capability differences between the data types and some user experience delta that is being addressed in the near term.
Note that TensorFlow and ML Engine allow you a greater degree of freedom to select and tune the model & much larger scale than a blackbox - albeit with some added complexity at present. That too will be addressed soon.
Dinesh Kulkarni
Product Manager, Google Cloud ML & Datalab
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I am looking at Google AutoML Vision API and Google Vision API. I know that if you use Google AutoML Vision API that it is a custom model because you train ML models based on your own images and define your own labels. And when using Google Vision API, you are using a pretrained model...
However, I am wondering if it is possible to use my own algorithm (one which I created and not provided by Google) and using that instead with Vision / AutoML Vision API ? ...
Sure, you can definitely deploy your own ML algorithm on Google Cloud, without being tied up to the Vision or AutoML API.
Two approaches that I have used many times for this same use case:
Serverless approach, if your model is relatively light in terms of computational resources requirement - Deploy your own custom cloud function. More info here.
To be more specific, the way it works is that you just call your cloud function, passing your image directly (base64 or pointing to a storage location). The function then automatically allocates all required resources (automatically), run your custom algorithm to process the image and/or run inferences, send the results back and vanishes (all resources released, no more running costs). Neat :)
Google AI Platform. More info here
Use AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and to use your model to make predictions about new data.
In doubt, go for AI Platform, as the whole pipeline is nicely lined-up for any of your custom code/models. Perfect for deployment in production as well.
I did lots of search, but I cannot understand what the difference between google ai platform and ml engine.
It seems that both of them can be used for training and deploying models.
Other words like google-cloud-automl, google ai hub are also very confusing.
What are the differences between them? Thanks
The short answer is: there isn't. In 2019 "ML Engine" was renamed to "AI Platform" and in time some services changed and expanded. To see what has changed, check the release notes, starting from around April. "Around", as they haven't left much trace that ML Engine ever existed.
Here's one of pull requests to "Rename Cloud ML Engine to AI Platform" for Python samples.
Cloud ML Engine = AI Platform Training + AI Platform Prediction (It was just a name change). Used for training and deploying ML models.
AI Platform Training: Bring your own code and submit Training jobs using supported ML frameworks such as TensorFlow, scikit-learn, XGBoost, Keras, etc.
AI Platform Prediction: Host your Model and use AI Platform Prediction to infer target values for new data.
Google Cloud Auto ML = You don't need to code, bring your dataset and GCP automatically picks the best model for you.
Different products:
Vision
Video Intelligence
Natural Language
Translation
Tables.
Google AI Hub = It is a Catalog: Discover Notebooks, Models and Pipelines.
Edit: Now AI Platform is called Vertex AI
Correct, the previous ML Engine service is now under Cloud AI Platform portfolio of products and provides end-to-end platform to build, run, and manage ML projects.
Please follow the instructions on how to use the service here.
I understand both are built over Jupyter noteboooks but run in cloud. Why do we have two then?
Jupyter is the only thing these two services have in common.
Colaboratory is a tool for education and research. It doesn’t require any setup or other Google products to be used (although notebooks are stored in Google Drive). It’s intended primarily for interactive use and long-running background computations may be stopped. It currently only supports Python.
Cloud Datalab allows you to analyse data using Google Cloud resources. You can take full advantage of scalable services such as BigQuery and Machine Learning Engine to analyse, manipulate and visualise data. You can use it with Python, SQL, and JavaScript.
Google Colaboratory is free. But, you are limited to one spec of cpu/ram/disk/gpu.
Google Datalab is paid. You pay for whatever specs you want.
The notebook interface is also a bit different between the two.
As we know, Google Cloud Speech API is in Beta now.
Will it be safe to use it in a application on production server?
I was also searching for the applications which is using Google Cloud Speech API, So far I have found the following,
VoiceBase, Hyperconnect, InterActiveTel
Does anyone know of any other applications that could give us more confidence in using it on production server?
The official definition of GCP launch stages, such as Beta, can be found in our documentation here.
Beta is the point at which we are ready to open a release for any customer to use. There are no SLA or technical support obligations in a Beta release, and charges may be waived in some cases. Products will be complete from a feature perspective, but may have some open outstanding issues. Beta releases are suitable for limited production use cases.
Emphasis is mine: Limited production. Ultimately, it is going to come down to your risk appetite.
As of Tuesday, April 18, the Cloud Speech API has reached General Availability, meaning all features are open to developers and are to be considered stable.
Voicebase provides more than just speech recognition and it is currently used in production by large customers. Take a look at some of the features
http://voicebase.readthedocs.io/en/v2-beta/index.html
I have to write a report about Cloud Computing and QoS, and I need information about the tools that Providers provide customers to measure the performances of services. Does anyone know if Google provides any such tool?
I apologize for any grammatical errors :)
Thank you for support
It really depends on what you mean by "performance", because there are many aspects, factors, etc. that can all be considered related to performance, including compute, storage, network, boot times, price/performance, etc.
Since your question is generic, you might want to read a couple of references below to get an overview of the types of performance measurements that can be performed and what tools, frameworks, etc. are generally used.
In no particular order, here are some references to tools and APIs from Google:
gsutil (storage) performance diagnostics
Measuring network performance with Resource Timing API
benchmark suites from third parties:
AMPLab Big Data Benchmark
Intel Cloud Object Storage Benchmark
and benchmark results from third parties:
Google Compute Engine benchmarking by Scalr
The Cloud Performance Dashboard: A Quick Market Overview
By the numbers: How Google Compute Engine stacks up to Amazon EC2
MongoDB on Google Compute Engine – tips and benchmarks
Performance analysis of GAE and AWS
Google Compute Engine Performance Test with RightScale and Apica
Measuring and Comparing the Performance of 5 Cloud Platforms
Cassandra performance benchmark by Stackdriver
DataStax Enterprise Testing on Google Compute Engine
Google Compute Engine vs Amazon EC2 Part 2: Synthetic CPU and Memory Benchmarks