Machine Learning Infrastructure as a Service

Machine learning Infrastructure as a Service (ML IaaS) is an infrastructure delivery model that provisions virtualized or bare-metal infrastructure resources that are performance optimized for compute-intensive ML and DNN workloads . The ML IaaS market is characterized by core capabilities, including hardware-accelerated, high-performance compute platforms, usually augmented by accelerator technologies like GPU, FPGA or custom processors like Google TPU. Due to the unique nature of hardware-involved DNN frameworks (such as TensorFlow, pyTorch, Caffe and MxNet), they need to be reconfigured and integrated with appropriate libraries to take full advantage of ML IaaS capabilities.

Amazon EC2 P3 Instances

Amazon Web Services (AWS)

Cloud GPU

Google

IBM bare-metal GPU (x86 based servers)

SENIOR SOFTWARE ENGINEER

Google cloud GPU offer a robust and versatile platform for accelerating high-performance computing tasks, levaraging cutting-edge NVIDIA technologies, these GPU excel in range of application, including:
Machine Learning: Training and deploying sophisticated AI models, including deep learning neural networks with unparalled speed and efficiency
Data Analytics: Accelerating data-intensive workloads, such as large-scale data processing, analytics and visualization

BPA/RPA Developer & Data Analyst

Google Cloud GPU is a great tool to process data that require GPUs for faster processing like images, videos and etc. In our company, we create and improve datasets with ML and with help of Googe Cloud GPU and we choose this one because it's very cost-effective and blazing fast. For example, we use several Nvidia Tesla v100 from Google Cloud GPU for just $X per GPU per hour which is very cheap compared owing one which costs about $Y plus electricity and maintenance costs. Also, Tensorflow and Keras work seamlessly with Google which is GREAT!

Apprentice BI Developer

GCP is great as they offer everything within the one platform! We have recently implemented GCP within our business, as you can imagine this has been a very long process as there is a lot involved. Google have offered a lot of training and resource materials to help each member of our team be fully competent. I mainly use the BI tools, and have found this very simple and easy to learn. This provides top dashboards with easy access to all users.  

Trainee

Great tool provided by a company that you can trust. Allows you to process large amounts of data with good performance

Coordinator

Scale up your projects with cloud GPU service, it's from Google, so it has the best support there is, also, there is documentation for everything we need.

Network & Server Engineer

Cloud GPU has the best features, storage capacity, very high secure management and user friendly. We can stored our documents in very secure way. 

Data Engineer

Google cloud GPUs are competitively priced and easily integrable to the VMs. Compared to other competitors they offer a wide range of NVIDIA GPUs which we can choose as per our need. I have been using them to train deep neural network models and I am overall satisfied with their service. However, due to the high demand at times, I have experienced resource limitations and being unable to start a stopped VM.

Product Development Engineer

For those who are looking forward to experiment with various machine learning algorithms and looking for a cost effective way to do that, I recommend them using cloud GPU provided by google. google is giving $300 worth cloud service for free per google account which is really impressive . we will able to use any GPU from these NVIDIA Tesla K80, P100, P4, T4, and V100 . as a newcomer I found that it very useful for doing computation intensive task such as training a neural network for my project. I am also got really impressed about the google colaboratoy GPU. it let us set us setup a cloud instance quickly and do what we want using GPU and save the trained model in our google drive itself. the only thing is that the session has  a time limit .The session expires after  90 minutes if we close the browser . anyway it let us using GPU for completely free which is an appreciatable thing 

Associate Software Developer

Dataproc cluster, SQL and Big Query have been a good experience to work. The UI of GCP is commendable as it really provides smooth mobility and understanding of the complete product and makes things easy to work.

Sr. Soft Engineer

Google cloud Very well managed services product that have used till. it is very easy to use.
i use google cloud products dataproc,google storage,Stacke driver for logging,big table,big query, google console to monitor all services

IT MANAGER

Excellent team, great experience. Very good and stable services. 

Cloud GPU vs IBM bare-metal GPU (x86 based servers)

Cloud GPU

Ratings breakdown

Likes and Dislikes

Top Alternatives

Categories

IBM bare-metal GPU (x86 based servers)

Ratings breakdown

Likes and Dislikes

Top Alternatives

Categories