SuperDuperDB eliminates complex MLOps pipelines, specialized vector databases – and the need to migrate and duplicate data by integrating AI at the data’s source, directly on top of your existing data infrastructure. This simplifies building and managing AI applications.
@sumanth_077
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Open-source Framework for Building AI Applications in Databases
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AI in your Database. An open-source framework for building AI Applications right in your databases:
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Daily Content Sharing on Python, Data Science, and Machine Learning
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That's a wrap! If you are interested in any of these below topics: – Python – Data Science – Machine Learning – Data Analysis – LLMs – MLOps Find me → @Sumanth_077 I'm sharing daily content over here.
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cuDF Pandas Accelerator: Overcome In-Memory Limitations
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Pandas operates in-memory, as it loads the entire dataset into the local memory of the machine it is running on. This limits its ability to handle large datasets. With cuDF’s pandas accelerator you can now bring accelerated computing to pandas workflows.
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RAPIDS cuDF Library Now Integrated in Google Colab
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Also their cuDF library will automatically know if you're running on GPU or CPU and speed up your processing. RAPIDS cuDF is now integrated directly into Google Colab. Checkout the Colab Notebook: https://
colab.research.google.com/github/rapidsa
i-community/showcase/blob/main/getting_started_tutorials/cudf_pandas_colab_demo.ipynb?ncid=ref-inor-594904
… Check out the Repo: -
NVIDIA Makes Pandas 150x Faster with Simple Code Addition
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Pandas is a powerful data analysis and manipulation library for Python!
— Sumanth (@Sumanth_077) 16 mai 2024
NVIDIA just made Pandas 150x faster with zero code changes🔥
All you have to add is just a couple of lines of code:
%load_ext cudf.pandas
import pandas as pd pic.twitter.com/bgllBZCqDTPandas is a powerful data analysis and manipulation library for Python! NVIDIA just made Pandas 150x faster with zero code changes All you have to add is just a couple of lines of code: %load_ext cudf.pandas
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Daily Content Sharing on Python Data Science and MLOps Topics
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That's a wrap! If you are interested in any of these below topics: – Python – Data Science – Machine Learning – Data Analysis – LLMs – MLOps Find me → @Sumanth_077 I'm sharing daily content over here.
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Chat with Your Data: Summarizing PDF Files in App
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Step 4: Chat with your data Finally, once the data has been ingested into the app, chat with your data. Here we are asking it to summarize the pdf file.
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Upload PDF Research Paper Data to Clarifai Vector Database
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Step 3: Upload the data, Here we are uploading the pdf file of a research paper. Once you upload the data, it will embed and index the data into your Clarifai app. Clarifai Apps acts as your vector database.
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Setting up RAG system with Clarifai Llama 3 70B model
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Step 2: Next, import the RAG class and set up the RAG system by passing your Clarifai user ID and the Llama 3 70B model URL from the Clarifai community You can find it here: https://
clarifai.com/meta/Llama-3/m
odels/llama-3-70B
… Once you sign up, you can find your user ID here: https://
clarifai.com/settings