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AWS SageMaker Applications

Browse applications built on AWS SageMaker technology. Explore PoC and MVP applications created by our community and discover innovative use cases for AWS SageMaker technology.

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SchoolCrusher

Improved Learning: SchoolCrusher empowers students with easy access to educational materials and interactive tools, fostering a deeper understanding of subjects. Quizzes and Assessments: Students can access a variety of quizzes and assessments tailored to their grade level and subject. These quizzes not only help in assessing your knowledge but also provide immediate feedback and explanations for incorrect answers. Homework Solvers: Stuck on a tricky math problem or a challenging physics equation? SchoolCrusher's homework solver can assist you step-by-step, guiding you to the correct solution and explaining the process along the way. Essay Writers: Writing essays can be a daunting task, but SchoolCrusher's essay writer feature makes it easier. Students can input their essay prompts, and the chatbot generates well-structured essays, complete with citations and references, saving valuable time and reducing writer's block. Subject-Specific Support: SchoolCrusher specializes in a wide range of subjects, from mathematics and science to literature and history. It offers subject-specific insights, study tips, and additional resources to enhance your understanding. Study Plans: The chatbot can help students create customized study plans based on their goals and schedules. It takes into account upcoming exams and assignments to ensure efficient time management. Language Support: SchoolCrusher is multilingual, providing support in various languages to cater to a diverse user base. Personalized Learning: With customized study plans and subject-specific support, SchoolCrusher tailors its assistance to individual needs. Teacher Efficiency: Teachers can streamline their lesson planning and assessment processes, allowing them to focus more on teaching. Accessibility: The chatbot's 24/7 availability ensures that learning and teaching resources are always within reach.

Team Tonic
SeamlessM4TAWS SageMakerOpenAILlama 2

Stock Market Sentiment Analysis

Here's a breakdown of the key components and objectives of our project: 1. **Historical Data Analysis**: our project starts by collecting and analyzing historical stock data, which includes information such as stock prices and trading volumes. 1. **Price Movement Analysis**: One aspect of your analysis involves studying price movements. This includes examining the changes in stock prices over time to identify significant increases or decreases. By analyzing price movements, you can identify dates where the market experienced significant price fluctuations. 1. **Volume Movement Analysis**: Another important aspect of your analysis is studying volume movements. Trading volume refers to the number of shares traded during a specific time period. By analyzing volume movements, you can identify dates where there was a notable increase or decrease in trading activity. Unusual volume spikes can indicate significant market moves. 1. **Event Identification**: Once you have identified dates with significant price and volume movements, your project aims to associate those dates with specific events or news that occurred. This could involve correlating market moves with company earnings releases, economic announcements, geopolitical events, or other relevant factors. By identifying events associated with market moves, you can provide context and explanations for the observed trends. 1. **Future Expectations**: In addition to analyzing historical data, our project aims to provide insights into future market behavior. By giving you the

Team X
AWS SageMaker

Autonomo

Live Streamers lack of time and opportunity for responding to every message from their fans during the live stream. And they find it hard to remember who their fans are and their last interaction with them, which makes it a weird of a relationship between the streamer and their fans. In addition to that potential sponsors can’t decide easily which streamer’s community is the right one to promote their product/service. Here's where Autonomo comes with the solution, Autonomo is a multifunctional chatbot that enhances the relationship between fans and live Streamers/Broadcasters, analyzes the community formed by fans and classifies them into a category. The Live streamer is then able to put the category its community classifies under in a marketplace where potential sponsors would search for the right one to promote their product/service. The more chat messages collected from a channel, the better it is for Autonomo to know what the community is interested in and to even answer community members with relevant answers when prompted. Autonomo leverages the advantage of vector Databases to assist LLMs and thus enhance its performance and its ability to remember.

autonomo
Llama 2AWS SageMaker

RushDownAI

Introducing the cutting-edge Virtual Manager platform tailored for College Athletes, where athletes can seamlessly connect with potential brands through AI-driven conversations. This innovative site will revolutionize brand partnerships by enabling athletes to engage in natural and personalized dialogues with prospective sponsors. The Virtual Manager will utilize a sophisticated AI algorithm to craft compelling scripts that integrate the brand's projects with the athlete's unique profile. Drawing from a comprehensive database of the athlete's past games and achievements, the AI-powered script generator will seamlessly weave together a synopsis of key moments and performance highlights. By factoring in the athlete's playing style, personality traits, and values, the script will resonate authentically, ensuring a genuine alignment between the athlete and the brand. This platform transcends the traditional means of sponsorship negotiations, fostering deeper connections by showcasing the athlete's journey and achievements in a relatable manner. The Virtual Manager site for College Athletes offers a streamlined interface where athletes can review, customize, and refine the generated scripts. The AI system adapts to feedback, continually improving its ability to capture the athlete's essence and effectively communicate with brands. Brands, on the other hand, gain insights into the athlete's on-field prowess, character, and fan following, enabling them to make informed sponsorship decisions. In essence, the Virtual Manager site for College Athletes stands at the crossroads of technology, sports, and business, redefining how athlete-brand collaborations are forged. By merging AI-generated scripts, past game synopses, and individual personas, this platform ensures a dynamic and fruitful partnership between college athletes and prospective brands, enhancing the sports sponsorship landscape in an unprecedented way.

DublinByte
AWS SageMakerLangChainLlama 2

GitScale

GitScale" is a groundbreaking project that revolutionizes the field of product management. This innovative platform leverages the power of automation to generate dynamic Kanban boards, seamlessly integrating data from various sources such as CRMs, Crashalytics, Ticketing Software, Product Vision Calls, and Usage Analytics. By automating the creation of Kanbans, GitScale empowers teams to concentrate on the most critical aspect of their work – solving issues and driving product development forward. GitScale currently interfaces with HubSpot, Firebase Analytics, Zendesk, and DataDog to construct and maintain a responsive product roadmap, eradicating concerns about outdated tasks or missed scrum meetings. The project's challenges include the implementation of n-shot learning to craft semi-structured responses from OpenAI output and effectively deciphering domain knowledge encompassing company products, staff details, and customer profiles, all within a limited contextual space. Furthermore, GitScale addresses the intricate task of consolidating multiple client requirements for priority assignment while ensuring that they are appropriately segmented into individual tasks. With GitScale, product management becomes a streamlined and efficient process, ultimately enhancing productivity and outcomes.

Sirius
OpenAIGPT-3.5Generative AgentsWeaviateLangChainGPT-4AWS SageMaker

MindfulAI - AI Agents for Mental and Eye Health

Our idea is to use AI, ML and open source technologies to revolutionize the healthcare fields - specifically Mental Wellness and Eye Care using Autonomous AI Agents. These Autonomous AI Agents are using SuperAGI and deployed on AWS. We have given the choice to users on our landing page to use Eye Health or AI Therapist for their purposes. The Eye Health uses Eye Care AI Agent to track blinks of users and let them know how their eyes are and what is the blink status of their eyes using real time live webcam feed. This will help to notify users and recommend them best strategies for eye care and early stage detection and prevention from diseases like Dry Eyes Disorders. We have the AI Therapist that uses Mental Wellness AI Agent to track the moods of the user from a live webcam feed and the SuperAGI helps in replying to users based on the mood track what they should do in order to be in a good mood, this helps users to track real time their moods and get some suggestions from AI Therapist. People of all ages have the tendency to have long working hours and exposures to screens and devices. These causes issues for eyes and also mental health gets affected due to such behaviors, mental wellness and some basic guidance as initial steps for healthcare can be provided by the AI Therapist and that can help users to track their mental moods, in future we can also allow them to have chatbots and have conversations with AI Therapist and Eye Care Agent. Eye Care Agent can help in tracking blinks and how good their eye health is and can suggest if their blink count per minute is less than 12, it can suggest the users to have some time off, do exercises, take a break, engage in healthy activities and take care of themselves. Screen time and usage of users can also be tracked and it can help to suggest users what steps to be taken in terms of productivity, creative and efficient parental controls, gaining better leverage on data using insights from AI & ML

AI Agents
AWS SageMakerOpenAIVercelGPT-3.5SuperAGI

Atomic Tonic

What if just about anyone could deploy autonomous agents with one click and no coding? Deploying autonomous agents with a single click, without any coding knowledge, can revolutionize the way we interact with technology. This approach democratizes the use of autonomous agents, opening doors for people from various backgrounds to harness their power. Such accessibility can lead to a surge in innovation, as more individuals experiment with and adopt this technology. It can also serve as an invaluable educational tool, offering hands-on learning about autonomous agents. Moreover, small businesses and entrepreneurs can leverage these agents for diverse tasks without needing a technical team.

Team Tonic
OpenAIGPT-3.5SuperAGIWeaviateAWS SageMaker

A aget like Jarvis

Overview: Shadow AI is an agent which is powered by the LLM (Large Language Model) in our case LLAMA 2 (specifically, llama-2-7b-chat model). The Agent is designed to help his boss in an efficient way for Drafting Emails Searching a Document and providing relevant information and tasks which are asked. Generating the presentation content Boss Mode: Combining all the crucial information and providing it to the boss. (Jus like Jarvis) The agent specifically provides a combination of all the stuff listed above and specifically lists them. The agent is also able generate the relevant images with the help of stable-diffusion Key Technology: AWS Sagemaker (Our agent extensively uses the AWS sagemaker) For running the llama-2-7b-chat model For running the stable-diffusion-v2-1-base AWS Lamda For communication with our models AWS API Gateway For providing the Api access AWS S3 For storing the objects and the frontend Python 3 Details: We are using AWS’s sagemaker service for running the LLM model and the stable diffusion model. Using AWS’s sagemaker we can run the model with ease. The models are then interacting with the help of AWS’s Lamda function which is also authenticated. All the object is stored at AWS’s S3 bucket and then used on the front-end

Shadow AI
Llama 2Stable DiffusionAWS SageMaker

WebML Assist

Elevate the realm of machine learning with "WebML Assist." This innovative project integrates the power of WebGPU and the capabilities of the "BabyAGI" framework to offer a seamless, high-speed experience in machine learning tasks. "WebML Assist" empowers users to build, train, and deploy AI models effortlessly, leveraging the parallel processing of GPUs for accelerated training. The platform intuitively guides users through data preprocessing, model architecture selection, and hyperparameter tuning, all while harnessing the performance boost of WebGPU. Experience the future of efficient and rapid machine learning with "WebML Assist." Technologies Used: WebGPU OpenAI APIs (GPT-3.5, GPT-4) BabyAGI Pinecone API (for task management) FineTuner.ai (for no-code AI components) Python (for backend) Redis (for data caching) Qdrant (for efficient vector similarity search) Generative Agents (for simulating human behavior). AWS SageMaker (for developing machine learning models quickly and easily build, train, and deploy). Reinforcement learning (is an area of machine learning concerned with how intelligent agents). Categories: Machine Learning AI-Assisted Task Management Benefits: "WebML Assist" brings together the capabilities of WebGPU and AI frameworks like "BabyAGI" to provide an all-encompassing solution for ML enthusiasts. Users can seamlessly transition from data preprocessing to model deployment while harnessing the GPU's power for faster training. The incorporation of AI agents ensures intelligent suggestions and efficient task management. By integrating AI, GPU acceleration, and user-friendly interfaces, "WebML Assist" empowers both novice and experienced ML practitioners to unlock the true potential of their projects, transforming the way AI models are built, trained, and deployed.

GPU Titans
medal
replit
application badge
WebGPUOpenAIBabyAGIFineTuner.aiGPT-3.5RedisQdrantGenerative AgentsAWS SageMakerReinforcement Learning

Real Time Language Translation for video calls

1. Technologies used : a. Eleven Labs Whisper : speech recognition and translation model for real time language translation b. Eleven Labs Voice AI : generates natural & life like voice that speaks out translated text almost simultaneously 2. Existing Technologies and their Limitations : a) Skype Translator : Less accurate due to complex accents => miscommunication b) Google meet's live caption : Used only for live captions , not accurate for complex language translation c) Zoom language Interpretation : Limited availability & higher cost. 3. Unique Selling Proposition - unlike existing technologies that focus on text based translation - we will provide natural life like voice translations for effective & interactive communication 4.How will we build? i. develop environment + frameworks, libraries ii. integrate whisper's speech recognition iii. implement video call functionality iv. use Voice AI to generate voice output for translated text and play it v. test our application to ensure accuracy vi. optimize app's performance and user experience vii. Deploy the app on server / cloud platform 5. Real Life Use Cases : ✅. Multilingual Business Meetings ✅ Language Exchange Programs ✅ Virtual Language Education ✅Cross cultural Collaboration ✅Global Customer Support Teams. ✅International Virtual Event

Code sapphire
OpenAIWhisperAWS SageMakerText Generation Web UI

MintAI AI

Imagine reading a complex text, say Keynes's General Theory, and struggling to comprehend the intricate concepts. Now, imagine an intelligent assistant that takes these abstract concepts and links them to real-world scenarios and current events you care about - making learning not only simpler, but also engaging and relatable. That's exactly what MintAI offers! MintAI is your AI-powered personal learning assistant, transforming the way we approach learning complex material. It bridges the gap between theory and practice, enhancing your understanding and retention of information. But, it's not just about simplifying the material - MintAI is built on a sophisticated AI that understands your unique learning style and personalizes the content accordingly. What's more? You are part of an active learning community! MintAI encourages you to upload materials, interact with the AI, and comment on real-world events with an understanding of theoretical concepts. As a result, you're not just learning - you're applying your knowledge and contributing to the learning of others. At the core of our business model is a subscription service that offers access to premium features and personalized content. This ensures a sustainable and continuous platform enhancement, making MintAI your lifelong learning partner. Join us as we revolutionize the learning experience, making it easier, more engaging, and highly personalized. Welcome to MintAI – your future of learning.

SparkO2
Vercel
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LangChainAWS SageMakerCohere EmbedAnthropic Claude

Bitcoin Transaction Fraud Classification

This is a Bitcoin Transaction Fraud Classification project Based on Elliptic Dataset provided by Elliptic Ltd. a London - based Blockchain analysis provider. Here is our Deployed Application link: https://share.streamlit.io/alihussainia/swissai/main This Project is built using Python based libraries. For data pre-processing we have used NumPy and Pandas, for data visualization we have used matplotlib and seaborn, for development we have used PyCaret and finally for web-development we have used Streamlit and StreamlitCloud. The Elliptic Dataset is a graph network of Bitcoin transactions with handcrafted features. All features are constructed using only publicly available information. Thank you for your Time and Consideration.

LateBirds
AWS SageMaker

FoodxChange

An app that lets you reduce waste by allowing the sharing of excess foodDEMO: https://share.streamlit.io/diegobiagini/saia_hackaton/main.py

The Next Convolution
AWS SageMaker

Relation between energy consumption and CO2 emission

One of the main source of greenhouse gases is heavy industry. Although industries are the driving forces of the economies, here we question how those industries and companies in them achieve to be fuel of machine called economy. In our concept we aimed to find relation between energy consumption and CO2 emission. Using this pattern we believe that an organization's environmental impact can be evaluated, which might be implicit. In the meantime, we are aware that our concept can be base for further studies, but cannot be definitive at this stage.

Data_Unicorns
AWS SageMaker

Earthquake Guardians

I decided to study the impact of earthquakes in my country (Chile) and how to mitigate it. For that I came up with an idea called "Earthquake Guardians". It implies that some people (suscribed to a specific Twitter account) will look at the messages sent of earthquake alarms and decide to confirm or deny such alarm. The account will send messages based on a deeplearning model trying to predict earthquakes by using the Grillo open database https://registry.opendata.aws/grillo-openeew/. Since earthquake prediction can be tricky, we count on these guardians to let us know if the earthquake predicted is real or not (and in the process, label the data for future training). Of course, earthquake prediction is not a new idea, but with the guardians and their help, it can create a better system with constant feedback. Also, in highly seismic areas (like Chile) the smaller earthquakes could also be detected and taken into account for future training of the model. I wanted to mix the idea of deeplearning prediction in python, with the Twitter solution like this one (https://blog.twitter.com/en_us/a/2015/usgs-twitter-data-earthquake-detection). Sadly, I didn't have enough time but to only do some EDA with the data.

Data Phoenix
AWS SageMaker