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Machine learning development requires a lot of computing power. Specifically GPU powered computing. GPUs are super expensive on incumbents like AWS. This creates a big divide between compute rich and compute poor developers and teams. Thus becoming a bottleneck for over 5 Million ML devs.
At Q Blocks, we have figured out a new way to solve this. To bring access to the most powerful GPUs at 1/10th the cost with the reliability and scalability of a cloud.
Try Q Blocks and save significant costs for training and tuning your next ML model
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You could say a lot of things about AWS, but among the cloud platforms (and I've used quite a few) AWS takes the cake. It is logically structured, you can get through its documentation relatively easily, you have a great variety of tools and services to choose from [from AWS itself and from third-party developers in their marketplace]. There is a learning curve, there is quite a lot of it, but it is still way easier than some other platforms. I've used and abused AWS and EC2 specifically and for me it is the best.
Based on our record, Amazon AWS seems to be a lot more popular than QBlocks Cloud. While we know about 372 links to Amazon AWS, we've tracked only 1 mention of QBlocks Cloud. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Solution Using AI APIs:To address this issue, the platform integrated Amazon Personalize, an AI API from Amazon Web Services (AWS), to implement personalized recommendation features. Amazon Personalize uses machine learning algorithms to analyze user behavior and preferences, generating individualized product recommendations. The integration process involved:. - Source: dev.to / about 22 hours ago
Salesforce is a powerful customer relationship management (CRM) platform that helps organizations manage their sales, marketing, and customer service processes. However, to unlock its full potential, integrating Salesforce with other powerful platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure can provide additional functionalities, streamline processes, and enhance data... - Source: dev.to / 2 days ago
Heroku runs on top of Amazon Web Services (AWS). Key benefits for me are:. - Source: dev.to / 16 days ago
First navigate to AWS at - https://aws.amazon.com create an account and then on the dashboard search for Amazon SES, click get started and then you should be directed to a dashboard like this. - Source: dev.to / 17 days ago
AWS Account Setup: If you don't have one, you can create a free account. - Source: dev.to / 20 days ago
There's a middle way: qblocks.cloud - This platform that enables access of unused GPU servers across the globe. It works like a traditional cloud by offering scalability, security and reliability while being upto 80% low cost for AI workloads. Also, offers inbuilt Jupyterlab, AI framework, GPU driver support out of the box. Thus no time wasted in server setup. Source: over 1 year ago
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Linode - We make it simple to develop, deploy, and scale cloud infrastructure at the best price-to-performance ratio in the market.Sign up to Linode through SaaSHub and get a $100 in credit!
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