The AIIP platform has released its token, which is similar to cryptocurrency exchange tokens (although AIIP itself is not an exchange). AIIP token holders have access to several benefits when using the AIIP platform, including the ability to host tokens in the platform’s shared trading pools.
• The AIIP token offers a 3x increase in funds and a full AI-powered prediction.
• AI is essential when predicting the trend of a token or coin. A pre-sale event will enable you to predict trends using AI successfully.
• Moreover, you will have the ability to withdraw and reinvest funds whenever you want.
https://promo.aiip.io/
In many machine learning (ML) applications of the real world, such as language understanding, computer vision, and neural machine translation, scaling neural networks, whether it’s the amount of training data used, the model size, or the computing used, has been crucial for improving model quality. As a result, recent studies have looked into the parameters that play a key influence in the effectiveness of scaling a neural model.
Although expanding model capacity is a good way to improve model quality, it comes with a variety of systems and software engineering problems to address. For example, in order to train big models that surpass an accelerator’s memory capacity, the weights and computation of the model must be partitioned across numerous accelerators. This parallelization method increases network connection overhead, leading to device underutilization. Furthermore, a given parallelization approach, which normally necessitates a large amount of engineering effort, may not be compatible with other model designs.
Google research team introduces GSPMD (General and Scalable Parallelization for ML, Computation Graphs). The researchers describe an open-source automatic parallelization system based on the XLA compiler, to address these scalability difficulties. GSPMD can scale most deep learning network designs and has already been used to a number of deep learning models, including GShard-M4, LaMDA, BigSSL, ViT, and MetNet-2, producing state-of-the-art results in a variety of applications. GSPMD has also been included in a number of machine learning frameworks, such as TensorFlow and JAX, which both use XLA as a shared compiler.
Quick Read: https://www.marktechpost.com/2021/12/13/google-ai-introduces-gspmd-a-largely-automated-parallelization-system-for-machine-learning-computation-graphs/
Paper: https://arxiv.org/pdf/2105.04663.pdf
Google Blog: https://ai.googleblog.com/2021/12/general-and-scalable-parallelization.html
Hey folks, I am so happy that there is a subreddit for discussing productivity and improving oneself every time to be productive. It has been very essential to focus on your daily activities and organize a plan to work deeply on things that matter to you the most at this point in time, especially during the internet age, where distraction is becoming like a normal thing, and being unproductive is very easy. Keeping this in mind, I am creating an automated time blocking program in Python for each day. It's halfway done, namely database management, showing the current activity and its period and the next task in the status bar of your computer. Also, your work can be divided into Deep Work and Shallow Work . The creation of a generic schedule is automated because it's easy to do. Currently, the plans for the next day have to be inserted manually, but I am trying to use Machine Learning, and try to make that part also automatic. You have any ideas or features that you'd like to see, I am very happy to welcome them. If I remain successful, the project would be out on mid or end February,
Good News: The project would be open-source.
AutoDeploy - Automated Machine learning model deployment with one configuration file.
📷
AutoDeploy What is AutoDeploy?
A one liner : For the DevOps nerds, AutoDeploy allows configuration based MLOps.
For the rest :
So you're a data scientist and have the greatest model on planet earth to classify dogs and cats! :). What next? It's a steeplearning cusrve from building your model to getting it to production. MLOps, Docker, Kubernetes, asynchronous, prometheus, logging, monitoring, versioning etc. Much more to do right before you The immediate next thoughts and tasks are
- How do you get it out to your consumer to use as a service.
- How do you monitor its use?
- How do you test your model once deployed? And it can get trickier once you have multiple versions of your model. How do you perform
A/B testing?
- Can i configure custom metrics and monitor them?
- What if my data distribution changes in production - how can i monitor data drift?
- My models use different frameworks. Am i covered?
... and many more.
What if you could only configure a single file and get up and running with a single command. **That is what AutoDeploy is!**
Read our [documentation](https://github.com/kartik4949/AutoDeploy/wiki) to know how to get setup and get to serving your models.
# Feature Support.
- [x] Single Configuration file support.
- [x] Production Deployment.
- [x] Logging.
- [x] Model Monitoring.
- [x] Custom Metrics.
- [x] Visual Dashboard.
- [x] Docker.
- [x] Docker Compose.
- [x] Custom Exeption Handler.
- [x] Pydantic Validators.
- [x] Dynamic Database.
- [x] Data Drift Monitoring.
- [x] Async API Server.
- [x] Async Model Monitoring.
- [x] Production Architecture.
- [ ] Kubernetes.
- [ ] Batch Prediction.
- [ ] Preprocess configuration.
- [ ] Posprocess configuration.
Homepage: https://www.tangram.xyz
GitHub: https://github.com/tangramxyz/tangram
Hi all, I'd like to share Tangram, an all-in-one automated machine learning framework written in Rust. With Tangram, you:
- Run
tangram trainto train a model from a CSV file on the command line. - Make predictions with bindings for Elixir, Go, JavaScript, Python, Ruby, and Rust.
- Run
tangram appto start a web application where you can learn more about your models and monitor them in production.
Tangram is written entirely in Rust, from the core machine learning algorithms, to the bindings for each programming language, to the back end and front end of the web application.
Machine learning can be a bit unfathomable for most people. What machine learning/AI algorithms are there that can be reasonably worked out on paper? Is there a way to understand an ANN by getting 10 people together exchanging numbers and adjusting state? Is there a way to do machine learning without a machine?
I'm guessing that a lot of systems are too complex/only work at scale.. But it would be interesting at least to de-automate at least part of a machine learning system. Kids learn sorting algorithms like bubblesort at school by standing in a line and following an algorithm. Is anything like this possible with ML algorithms?
A team of researchers from Google Brain introduces a new way of programming automated machine learning (AutoML) based on symbolic programming. The team also introduces a new python library called PyGlove that demonstrates the new paradigm’s promising results.
At present, Neural networks are extensively being adopted in various fields. However, determining the correct neural network architecture for a particular task becomes time and resource-consuming because of the increasing complexity in their architecture. Complicated architectures also lead to difficulty in hyperparameter selection. Automated Machine Learning (AutoML) is highly encouraged for automating these selection choices. In May 2017, Google coined AutoML for its neural architecture search (NAS). The approach is widely accepted in the ML research community to automate network hyperparameter and architecture choices.
AutoDeploy What is AutoDeploy?
A one liner : For the DevOps nerds, AutoDeploy allows configuration based MLOps.
For the rest :
So you're a data scientist and have the greatest model on planet earth to classify dogs and cats! :). What next? It's a steeplearning cusrve from building your model to getting it to production. MLOps, Docker, Kubernetes, asynchronous, prometheus, logging, monitoring, versioning etc. Much more to do right before you The immediate next thoughts and tasks are
- How do you get it out to your consumer to use as a service.
- How do you monitor its use?
- How do you test your model once deployed? And it can get trickier once you have multiple versions of your model. How do you perform
A/B testing?
- Can i configure custom metrics and monitor them?
- What if my data distribution changes in production - how can i monitor data drift?
- My models use different frameworks. Am i covered?
... and many more.
What if you could only configure a single file and get up and running with a single command. **That is what AutoDeploy is!**
Read our [documentation](https://github.com/kartik4949/AutoDeploy/wiki) to know how to get setup and get to serving your models.
# Feature Support.
- [x] Single Configuration file support.
- [x] Production Deployment.
- [x] Logging.
- [x] Model Monitoring.
- [x] Custom Metrics.
- [x] Visual Dashboard.
- [x] Docker.
- [x] Docker Compose.
- [x] Custom Exeption Handler.
- [x] Pydantic Validators.
- [x] Dynamic Database.
- [x] Data Drift Monitoring.
- [x] Async API Server.
- [x] Async Model Monitoring.
- [x] Production Architecture.
- [ ] Kubernetes.
- [ ] Batch Prediction.
- [ ] Preprocess configuration.
- [ ] Posprocess configuration.
Homepage: https://www.tangram.xyz
GitHub: https://github.com/tangramxyz/tangram
Hi all, I'd like to share Tangram, an all-in-one automated machine learning framework. With Tangram, you:
- Run
tangram trainto train a model from a CSV file on the command line. - Make predictions with bindings for Ruby or a number of other programming languages.
- Run
tangram appto start a web application where you can learn more about your models and monitor them in production.
This makes it easy to add machine learning to your applications written in Ruby without having to learn the python machine learning ecosystem, set up a separate service to serve your models, and set up your own monitoring solution.
Check it out and let us know what you think!
Homepage: https://www.tangram.xyz
GitHub: https://github.com/tangramxyz/tangram
Hi all, I'd like to share Tangram, an all-in-one automated machine learning framework with bindings for Elixir. With Tangram, you:
- Run
tangram trainto train a model from a CSV file on the command line. - Make predictions with bindings for Elixir or a number of other programming languages.
- Run
tangram appto start a web application where you can learn more about your models and monitor them in production.
This makes it easy to add machine learning to your applications written in Elixir without having to learn the python machine learning ecosystem, maintain a separate service to serve your models, and set up your own monitoring solution.
Check it out and let us know what you think!
Homepage: https://www.tangram.xyz
GitHub: https://github.com/tangramxyz/tangram
Hi all, I'd like to share Tangram, an all-in-one automated machine learning framework. With Tangram, you:
- Run
tangram trainto train a model from a CSV file on the command line. - Make predictions with bindings for Go or a number of other programming languages.
- Run
tangram appto start a web application where you can learn more about your models and monitor them in production.
This makes it easy to add machine learning to your applications written in Go without having to learn the python machine learning ecosystem, set up a separate service to serve your models, and set up your own monitoring solution.
Check it out and let us know what you think!
Homepage: https://www.tangram.xyz
GitHub: https://github.com/tangramxyz/tangram
Hi all, I'd like to share Tangram, an all-in-one automated machine learning framework written in Rust. With Tangram, you:
- Run
tangram trainto train a model from a CSV file on the command line. - Make predictions with libraries for Elixir, Go, JavaScript, Python, Ruby, and Rust.
- Run
tangram appto start a web application where you can learn more about your models and monitor them in production.
Check it out and let us know what you think!
Homepage: https://www.tangram.xyz
GitHub: https://github.com/tangramxyz/tangram
Hi all, I'd like to share Tangram, an all-in-one automated machine learning framework. With Tangram, you:
- Run
tangram trainto train a model from a CSV file on the command line. - Make predictions in Node.js with the @tangramxyz/tangram npm package, or bindings for a number of other languages.
- Run
tangram appto start a web application where you can learn more about your models and monitor them in production.
This makes it easy to add machine learning to your Node.js applications without having to learn the python machine learning ecosystem, set up a separate service to serve your models, and set up your own monitoring solution.
Check it out and let us know what you think!
Tangram makes it easy for teams without machine learning experts to train, deploy, and monitor machine learning models. Read more about it below:
https://bit.ly/3rlNzkh
