Collaborative Self-Serve Advanced Analytics
with Sparkflows + Google Cloud Platform (GCP)
Perform Data Analytics, Data Exploration, and build ML Models
in minutes using the 450+ Processors in Sparkflows
Benefits of Sparkflows on Google Cloud
Enables Business Analysts
Self Serve Advanced Analytics
Return on Investment (ROI)
Enables Business Analysts to find quick value with GCP clusters.
Enables users to do Analytics and Machine Learning in minutes.
Solves your Data Science use cases 10x faster.
10x More Users
Enables 10x more users to build
Data Science use cases.
No code and low code platform
Makes it easy to build, maintain
With Sparkflows Generative-AI Platform, you can effortlessly build optimized Retrieval-Augmented Generation(RAG) applications by harnessing the power of LLM models leveraging the 400+ processors .
It facilitates the development of data science analytics, chatbots, and statistical analytics on private knowledge warehouses and diverse datasets.
The platform supports both closed-source models like GPT and open-source models like Llama 2, Falcon-40b among others from the Hugging Face repository.
LLM Powered NLP Workflows
The workflows are integrated with LLM models from Hugging Face. They are also integrated with OpenAI, Anthropic, Bedrock, Bard.
With LLM models from Hugging Face one can handle various Natural Language tasks, including Text Classification, Text Generation, Token Classification, Question Answering, Sentence Similarity, Summarization, Zero-shot classification, Translation, and Fill-Mask. It does not need any external call.
LLM Powered Analytical Apps
With Analytical Apps in Sparkflows, one can build any type of front end interface for the Business users with drag and drop. The front end interface is extremely rich and integrates with the backend Workflows and Notebooks.
With the introduction of LLM and Generative AI in Analytical Apps, it can interact with any Gen AI API seamlessly and also incorporate offline LLM models in the processing.
Our vertical solutions leverage cutting-edge LLM models, leading in generative AI technology, to achieve precise and efficient results.
Powered by these state-of-the-art models, we enable sentiment analysis, text mining, and extracting valuable information to interpret emotions and opinions in textual data. Our Generative-AI solutions cater to market analysis, brand sentiment, and product recommendation systems, optimizing dynamic pricing, customer segmentation, and personalized shopping experiences.
Fine Tune LLM
Sparkflows No code Studio facilitates fine-tuning of LLM on personal datasets to customize responses. Both open-source models built in-house or from Hugging Face or elsewhere can be incorporated and fine-tuned on private data.
Even huge models like Llama 2 (60 Billion parameters) and Falcon (40 Billion parameters) can be fine-tuned efficiently. The process is less computationally intensive than LLM training and can be accomplished on a commodity GPU in a few hours or even a CPU on a laptop.