Shoc Platform
Alternatives
0 PH launches analyzed!
Shoc Platform
Serverless ML & HPC Workloads, Simplified
8
Problem
Users managing machine learning and high-performance computing workloads face complex infrastructure setup, manual scaling, and inefficient cost management with traditional cloud solutions, leading to operational overhead and wasted resources.
Solution
A serverless platform for ML and HPC workloads that allows users to deploy code without infrastructure management, automatically scale resources, and pay-per-use. Example: Run distributed training jobs with one-click deployment.
Customers
Data scientists, machine learning engineers, and computational researchers in tech companies or academic institutions who need scalable compute without DevOps overhead.
Unique Features
Specialized serverless architecture optimized for batch ML/HPC jobs, abstracting away cluster orchestration while supporting frameworks like PyTorch and TensorFlow out-of-the-box.
User Comments
Saves weeks of cloud configuration time
Finally a Heroku-like experience for HPC
Cost dropped 40% vs. managed Kubernetes
Missing some niche ML libraries
Needs better job monitoring tools
Traction
Launched 6 months ago; used by 120+ teams (disclosed in PH comments); integrates with AWS/GCP; founder has 2.3K LinkedIn followers
Market Size
Global machine learning infrastructure market projected to reach $96.7 billion by 2025 (MarketsandMarkets), with cloud-managed services growing at 28% CAGR.
TIR AI/ML Platform
Build, Train and Deploy high performance AI/ML solutions
2
Problem
Users need to manage fragmented tools for different AI/ML development stages (data preparation, model training, deployment), leading to integration complexity, workflow inefficiency, and delayed deployment cycles.
Solution
End-to-end AI/ML platform enabling users to build, train, and deploy models in a unified environment, with pre-built templates, automated workflows, and cloud integration (e.g., AWS/GCP).
Customers
Data scientists, AI engineers, and DevOps teams at mid-to-large enterprises or startups needing scalable AI solutions without infrastructure headaches.
Alternatives
View all TIR AI/ML Platform alternatives →
Unique Features
Unified lifecycle management (data ingestion to deployment), one-click model optimization, and hybrid cloud compatibility with E2E Clouds’ infrastructure.
User Comments
Reduces deployment time by 50%
Simplifies collaboration across teams
Lacks advanced customization for niche use cases
Cost-effective compared to AWS SageMaker
Steep learning curve for non-technical users
Traction
Launched on ProductHunt in 2024; parent company E2E Clouds serves 15,000+ clients globally, though TIR-specific metrics (users/MRR) are undisclosed.
Market Size
The global machine learning market is projected to reach $528.10 billion by 2030 (Grand View Research, 2023).
Extreme-ML
Use AI / ML for Fueling Growth !
8
Problem
The current situation involves users struggling with developing machine learning models, data visualization, and data analysis using traditional tools. These methods can be time-consuming and inefficient, leading to delays in gaining insights and making data-driven decisions.
Developing machine learning models, data visualization, and data analysis using traditional tools
Solution
Extreme-ML offers a comprehensive platform that combines multiple capabilities: machine learning model development, data visualization, and data analysis. Users can leverage this platform to speed up model development, generate accurate insights, and enhance their data-driven decision-making.
comprehensive platform that combines machine learning model development, data visualization, and data analysis
Customers
Data Scientists, Machine Learning Engineers, and Business Analysts in tech companies and organizations focused on utilizing data for strategic decisions. They are typically professionals in tech-savvy roles who actively seek to streamline their workflows and enhance productivity.
Data Scientists, Machine Learning Engineers, and Business Analysts
Unique Features
The unique aspect of Extreme-ML lies in its ability to seamlessly integrate machine learning model development with data visualization and analysis in one platform, allowing for rapid and precise insights.
User Comments
Users appreciate the integration of multiple functionalities in a single platform.
The tool is recognized for its speed and efficiency in model development.
There is a positive reception towards how it improves data-driven decision-making.
Some users find value in the improved visualization capabilities.
Overall, user feedback highlights the platform's usefulness in enhancing analytic processes.
Traction
The exact details regarding traction such as the number of users or revenue are not provided. However, based on its presence on Product Hunt, the product seems to be gaining visibility and engagement within the community. Additional information should be gathered directly from the platform or its updates.
Market Size
The global machine learning market size was valued at $8.43 billion in 2019 and is projected to reach $117.19 billion by 2027, growing significantly due to increasing demand for AI and data-driven decision-making.
Problem
Users new to Mobile Legends (ML) struggle to find comprehensive, up-to-date guides to understand hero roles, types, and strategies, leading to confusion and inefficient gameplay.
Solution
A web-based guide offering structured insights into ML heroes, including role classifications, hero types, and personalized recommendations to help users identify their ideal playstyle. Example: Detailed breakdowns of hero abilities and team synergy.
Customers
New Mobile Legends players, primarily teenagers and young adults in Southeast Asia, actively seeking gameplay tutorials and hero mastery tips.
Unique Features
Curated April 2025 updates, hero categorization by meta relevance, and tailored hero recommendations based on playstyle preferences.
User Comments
Simplifies hero selection
Up-to-date meta insights
Beginner-friendly explanations
Helps accelerate skill progression
Requests for multilingual support
Traction
Launched April 2025, featured on ProductHunt with traction details unspecified in provided data.
Market Size
The global mobile gaming market reached $92.6 billion in 2023, with MOBA titles like ML dominating in regions like Southeast Asia.
Fleak - AI Workflows. Simplified.
Build Serverless APIs in Minutes
533
Problem
Users need to simplify AI workflows for data teams without the hassle of managing infrastructure.
Drawbacks: Setting up and managing infrastructure for AI workflows can be time-consuming and complex, requiring specialized knowledge.
Solution
A dynamic API builder tool for building serverless APIs in minutes.
Core Features: Integrate with leading cloud data warehouses and vector databases, build complex model chaining data workflows easily.
Customers
Data teams and professionals looking to simplify AI workflows without the need for managing infrastructure.
Occupation: Data analysts, data scientists, AI engineers.
Unique Features
Seamless integration with cloud data warehouses and vector databases.
Ability to create complex model chaining data workflows effortlessly.
User Comments
Easy to use and powerful API builder tool.
Saves time and simplifies AI workflow processes.
Great for data professionals and teams.
Intuitive interface for building complex data workflows.
Impressive integration capabilities with cloud data warehouses.
Traction
Over 500k API requests processed monthly.
Currently used by 200+ data teams and professionals.
Featured on ProductHunt with positive reviews.
Market Size
$120 billion market value for AI-based workflow simplification tools.
Growing demand for serverless API solutions in the data industry.
LuxLang — ML Coding Made Simple
Smart & Pythonic Language for AI/ML Developers
4
Problem
AI/ML developers rely on traditional programming languages with manual gradient computation (autodiff) and static tensor management, leading to complex workflows and slower model iteration.
Solution
A programming language offering Pythonic syntax with dynamic tensors and built-in autodiff, enabling AI/ML developers to write efficient, production-ready code without external libraries.
Customers
Data scientists, machine learning engineers, and researchers focused on AI/ML model development, especially those requiring simplified tensor operations and automatic differentiation.
Unique Features
Pythonic syntax for familiarity, integrated autodiff for gradient computation, dynamic tensor operations, and open-source accessibility.
User Comments
Simplifies ML workflows
Reduces boilerplate code
Seamless autodiff integration
Python-like syntax eases adoption
Lacks ecosystem maturity compared to PyTorch/TensorFlow
Traction
Open-source project with 2.4k GitHub stars, 180+ upvotes on ProductHunt, and initial adoption by small AI startups.
Market Size
The global machine learning market is projected to reach $138 billion by 2026 (MarketsandMarkets, 2023).
Geoflip.io: Geospatial Data Simplified
Simplify complex spatial data processing with our API & SaaS
54
Problem
Users currently rely on expensive enterprise platforms to manage and process geospatial data like SHP, GeoJSON, or DXF.
Expensive enterprise platforms
Difficulties experienced by non-technical users in handling such data
Solution
A geospatial API and SaaS platform
Effortlessly transform, process, and convert spatial data
Users can simplify geospatial workflows without needing complex or costly solutions
Customers
Developers who require geospatial data processing
Non-technical users looking for accessible geospatial data solutions
Unique Features
The ability to process multiple geospatial data formats without needing expensive platforms
User Comments
Highly effective in simplifying geospatial data processes.
Valuable for those with limited technical expertise.
Cost-efficient alternative to traditional enterprise solutions.
Streamlines complex workflows for both developers and non-developers.
Product praised for its ease of use and accessibility.
Traction
Specific quantitative data on users and revenue is not available from the provided information
Market Size
The geospatial analytics market is projected to reach $96.34 billion by 2027
Problem
Users often encounter complex texts that are difficult to understand which leads to confusion and reduced comprehension. difficult to understand
Solution
Simplify is a browser extension that converts complex text into clear, concise language, thereby making information easy to understand for everyone at a click.
Customers
The primary users are students, researchers, non-native English speakers, and professionals who frequently engage with complex texts.
Unique Features
Transforms complex text into easy-to-understand language instantly.
User Comments
Users appreciate its simplicity and effectiveness.
Some find it crucial for understanding academic papers.
It's praised for aiding non-native English speakers.
Feedback suggests it saves time and boosts productivity.
Minor suggestions for improvement on highly technical texts.
Traction
No specific traction details like MRR, users, or financing found.
Market Size
The text simplification market is hard to pinpoint, but the global language services market was valued at $49.6 billion in 2019.
ML/DL Study
Your Ultimate Roadmap for Machine Learning & Deep Learning
4
Problem
Users interested in machine learning and deep learning often face difficulty in accessing a consolidated and comprehensive learning resource. They typically have to piece together content from various books, scattered online tutorials, and courses, which is time-consuming and often lacks a structured pathway.
accessing a consolidated and comprehensive learning resource
Solution
ML/DL Study provides a comprehensive educational platform.
Curated video lectures, interactive tools, hands-on projects, and research papers—all in one place.
Users can watch video lectures, engage with interactive tools, complete hands-on projects, and access research papers to learn more effectively.
Customers
Aspiring data scientists, machine learning enthusiasts, and computer science students.
data scientists, machine learning enthusiasts, and computer science students
Typically aged between 20-35, tech-savvy, and motivated to advance their skills in machine learning and deep learning.
Unique Features
It is free, open-source, and continuously updated, providing a single, consolidated source for all major ML and DL learning components.
User Comments
Users appreciate the comprehensive and structured learning path.
The open-source nature offers flexibility.
The interactive tools are well-received.
Continuous updates keep the content current.
Some users find the hands-on projects particularly beneficial.
Traction
Over 8,000 learners worldwide have joined, indicating significant interest and engagement.
Market Size
The global artificial intelligence education market size was estimated at $2 billion in 2021, with rapid growth expected due to increasing demand for AI skills.
Problem
Businesses often struggle to predict customer behavior on their websites, leading to inefficient spending on advertisements and less effective marketing strategies.
Solution
Almeta ML is a machine learning powered tool designed to analyze and predict customer behavior on websites in real time. This allows users to target potential customers more accurately through personalized ads, tailored email campaigns, or direct offers.
Customers
Marketing professionals, business owners, and digital advertisers who need precise targeting and efficient ad spend to optimize their marketing campaigns.
Unique Features
Real-time prediction of customer purchasing likelihood, integration with major advertising platforms like Google Ads and Facebook Ads for direct targeting.
User Comments
Highly accurate predictions.
Easy integration with existing ads platforms.
Significant cost saving on ad spend.
Enhanced targeting of potential customers.
Real-time analytics aid decision making.
Traction
Launched on ProductHunt, receiving positive feedback for its effectiveness in reducing ad costs and improving ad targeting.
Market Size
The global predictive analytics market size is expected to reach $35.45 billion by 2027.