7 Good But Less Popular Open Source AI Platforms and Tools

in this video I'm going to show you seven open source AI platforms that can improve your workflow I'm not going to talk about open AI because if you have clicked on this video I think you're familiar with that platform and you're here for the rest for the same reason I'm skipping Google AI platform and Microsoft Azure because it's just too obvious another disclaimer this video is for Tech newbies so if you're a software engineer or a programmer maybe use the timestamps in the video to check out the interesting parts for you let's start with clarifying what is open source AI platforms those are tools or environments for creating artificial intelligence systems think of Open Source AI platform as a set of cooking utensils recipes ingredients and Community Kitchen and I'm gonna explain now the cooking utensils are the tools that the platform provides those might be functions for building neural networks which are the main technology behind modern AI or tools for training those networks on data and libraries that help you handle the data more efficiently the recipes are just is the pre-built models and algorithms available within the platform ingredients is the data provided that you can use to train your AI and Community Kitchen is just the nature of Open Source AI platforms so when we're talking about open source AI platforms we're talking about software tools that help you build your AI systems and the recipes for those are available openly to everybody to use learn from and adapt hopefully that made sense now let's go to the platforms this is a full body platform that supports multiple programming languages and helps you craft and execute novel AI models you have robust libraries and quiet responsive Community Support however the learning curve can be a bit steep especially for beginners and to give examples of two cool functions on this platform the first one obviously will be the neural network training tensorflow provides extensive support for training both convolutional and recurrent Network trainings in simple language those neural networks are systems based on how our brain functions to solve complex problems such as identifying objects in an image another cool function is the model deployment tensorflow models can be easily deployed to various platforms this includes mobile and Edge devices through cloud-based systems in simpler language again this means putting your models in use so once you have built this neural network model you can then put it in let's say mobile app this platform is a playground for open source AI projects development if you are comfortable with python pytorch is your Heaven the downside it can be quite heavy and it consumes a lot of memory and it can be slower especially when it comes to training large models to give example of three cool things about pytorch is first the dynamic computation graph basically this is the think about this platform it gives you a lot of flexibility in building your AI models you can think about it as if you're building with Lego blocks so on one hand you have a lot of flexibility and control but on the other hand you also have guidelines to follow if you need them then we have the integration with python if you already know Python language this is the heaven platform for you because by using python libraries there's more clarity to you in building your models and you definitely have very good Community Support in ecosystem because it's backed up by phase books AI research lab so you have a lot of tutorials pre-trained models tools that basically make the platform rather easier to learn and implement scikit is an open source machine learning library which is a good moment to remind ourselves that AI is about teaching machines to do human-like tasks it is a Powerhouse for data processing model fitting model selection and evaluation but if you are into deep learning this might not be the easiest platform because it doesn't offer direct support for it and correct me if I'm wrong in the comments because these things are changing so fast that I always worried that my research will be outdated by the time I actually post the video so on one hand scikit-learn doesn't give you the support for deep learning the way tensorflow or Pi torch to but on the other hand it's very flexible and Powerful library for traditional machine learning for example it gives you a data pre-processing which means It prepares your data before you start analyzing it examples of those functions are feature extraction normalization and data transformation a cooler thing or more important thing here is the model evaluation think about it as checking your work so before you have built your AI models the platform gives you quite handy tools to check how well it's working and some of the functions that are used for the model evaluation are easier for me to understand because of my science degree such as recall Precision R2 Square MSE for aggression but other functions are more specific for machine learning so again it's a very good platform but it's more Niche down as a platform Apache mxnet is so popular that probably didn't need to mention it for the same logic I didn't mention open AI but I really wanted to include it here for the AI open source reference because it's really powerful Apache mxnet is a flexible and efficient library for deep learning the main point of Apache is its capability to scale and distribute across multiple gpus and multiple machines for example it can be used to train simple linear regression models to large neural networks on multi-gpu and multi-machine setups the documentation is not as comprehensive as tensorflow or pytorch but with great capabilities come grade demand I would say meaning that you have to know a bit more before you can start benefiting from that platform which is ironic because one of the main benefits of Apache mxnet is the flexible programming model because a platform supports both symbolic and imperative coding which gives flexibility to optimize computation and simplify coding respectively and if I have to put this in simple words it means that you can choose how to work so the platform really gives you freedom in different ways depending on your preferences which ultimately makes it easier to build your AI models because if you have preferences and if you have a style Apache mxnet seems to be the platform to go opencv is a popular open source AI platform and if you are into computer vision this is your go to platform it is very adaptable because its library is written in C however as a specialized tool it might not cover all AI development needs but again that's not necessarily a problem depending on your needs two main functions to mention here image processing opencv can perform operations on images such as color space transformation geometric transformation or image thresholding in simpler words this means change the colors and the shapes of your images or identify objects with your images more interesting function will be recognizing faces and objects so it is a cool platform if you want to build AI models for security systems next one is H20 AI which is my favorite platform as a newbie it claims to be the fastest is most accurate AI platform out there so if you have more experience and you're watching this and you think that this claim is not justified please leave a comment however really when you get on the platform so many things of the tedious part of learning tedious part of building a model are automated the H20 AI platform provides capabilities for conducting statistical analysis and predictive modeling in a scalable and fast Manner and it supports the most widely used machine learning algorithms they also offer a product called driverless AI which is a more advanced automated machine learning platform it is designed to handle many of the time-consuming tasks involved in machine learning one setback that I experience at the time I was researching it was that it is not big on customization and it doesn't look very pretty but again well are we here for the looks or for the functionality for example the platform features Automated machine learning meaning that it can automatically find the best model for your problem and in that sense it saves you a lot of time and effort which helps you build more robust AI models the other cool thing here is the explainability which helps you understand your model better it provides features to interpret why your model is doing what is doing which in my opinion definitely helps to build trust in your AI model I think it's an amazing platform for newbies to take and specifically AI now we're skipping the obvious GitHub and instead we're gonna talk about GitHub AI projects GitHub AI projects include open source AI projects for spam protection on Instagram and fake product review identification so quite Niche again at least at that time but also it's relevant depending on your needs and because it's hosted on GitHub it makes it very easy to collaborate with others on AI projects you have the active and very supportive GitHub Community you have the GitHub Integrations so you have tools you have services that are used in AI development such as Jupiter notebooks and continuous integration tools like Travis CI I would say the main benefit and problem comes from the flexibility of the platform because the quality and rehability of the projects is not always that high also newcomers to AI or coding might find the platform a bit too confusing because some of the projects have missing documentation or outdated documentation overall GitHub is a must-have platform but I would not start with this for building AI models per particularly also it's like thrift shopping you never know what you're gonna find so I'm divided on this one let me know what you think about it in conclusion I would say that from the listed platforms here tensorflow seems to be the strongest one it was developed by researchers and Engineers working on Google brain team and I really have nothing against the platform however as a newbie again I would go with H20 AI because it skips a lot of tedious steps and it gives you the chance to hop on the more interesting things faster at least in my humble opinion again I'm actually very curious about the comments on this video so I hope this video helped you understand the main idea of Open Source AI platforms if you are new to Tech and especially if you're curious about AI then probably this channel is going to be good for you so hopefully I'm gonna see you in the next video bye

Channel: Let's Ask The AI

Views: 741

Keywords: ai, chatgpt, large language models, how to use ai, how to prompt chatgpt, ai apps, artificial intelligence, open source ai, generative ai, pytorch, tensorflow, apache mxnet, github ai projects

Id: nxmATKohoJw

Length: 12min 1sec (721 seconds)

Published: Sun Jun 25 2023