Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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DataRobot Blog

FEBRUARY 28, 2023

By bringing the unmatched AutoML capabilities of DataRobot to the data in Snowflake’s Data Cloud, customers get a seamless and comprehensive enterprise-grade data science platform.” They can enjoy a hosted experience with code snippets, versioning, and simple environment management for rapid AI experimentation.

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AWS Machine Learning Blog

AUGUST 26, 2024

This instance will be used for various tasks such as video processing and data preparation. env_setup.cmd Prepare the sign video annotation file for each processing run: python prep_metadata.py Download the sign videos, segment them, and store them in Amazon S3: python create_sign_videos.py

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Flipboard

AUGUST 17, 2023

Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml For Prepare template , select Template is ready. Enter a stack name, such as Demo-Redshift. The environment preparation process may take some time to complete.

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Snorkel AI

NOVEMBER 1, 2023

When Vertex Model Monitoring detects data drift, input feature values are submitted to Snorkel Flow, enabling ML teams to adapt labeling functions quickly, retrain the model, and then deploy the new model with Vertex AI. See what Snorkel can do to accelerate your data science and machine learning teams. Book a demo today.

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AWS Machine Learning Blog

AUGUST 22, 2023

It serializes these configuration dictionaries (or config dict for short) to their ProtoBuf representation, transports them to the client using gRPC, and then deserializes them back to Python dictionaries. Data is split into a training dataset and a testing dataset. Details of the data preparation code are in the following notebook.

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AWS Machine Learning Blog

AUGUST 14, 2023

Often, to get an NLP application working for production use cases, we end up having to think about data preparation and cleaning. This is covered with Haystack indexing pipelines , which allows you to design your own data preparation steps, which ultimately write your documents to the database of your choice.

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Heartbeat

JUNE 5, 2023

MLFlow From data preparation through application deployment, MLFlow is an open-source platform that manages the whole machine learning lifecycle. Anomalib Anomalib is a Python library that helps users to detect anomalies in time-series data.

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Snorkel AI

NOVEMBER 1, 2023

When Vertex Model Monitoring detects data drift, input feature values are submitted to Snorkel Flow, enabling ML teams to adapt labeling functions quickly, retrain the model, and then deploy the new model with Vertex AI. Book a demo today. Revamped Snorkel Flow SDK Also included in the 2023.R3 See what Snorkel option is right for you.

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Explosion

NOVEMBER 16, 2021

The spaCy configuration system The spaCy project system Final thoughts The spaCy configuration system If I were to redo my NER training project again, I’ll start by generating a config.cfg file: python -m spacy init config --pipeline ner config.cfg Think of config.cfg as our main hub, a complete manifest of our training procedure.

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AWS Machine Learning Blog

SEPTEMBER 25, 2024

Solution overview In this solution, we start with data preparation, where the raw datasets can be stored in an Amazon Simple Storage Service (Amazon S3) bucket. We provide a Jupyter notebook to preprocess the raw data and use the Amazon Titan Multimodal Embeddings model to convert the image and text into embedding vectors.

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Towards AI

JANUARY 31, 2023

Here is a quick demo of how it works Let's now dive deep into how I went about the project. Data preparation The first thing I did was import the necessary libraries. I need to mount the data since the dataset is on my Google Drive. I made use of a separate Python script to perform this task. Sure, it does.

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The MLOps Blog

JUNE 27, 2023

For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc., It enables data scientists to log, compare, and visualize experiments, track code, hyperparameters, metrics, and outputs. and programmatically via the Kolena Python client.

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AWS Machine Learning Blog

JULY 5, 2023

This solution contains data preparation and visualization functionality within SageMaker and allows you to train and optimize the hyperparameters of deep learning models for your dataset. You can use your own data or try the solution with a synthetic dataset as part of this solution. Finally, you launch SageMaker Studio.

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The MLOps Blog

OCTOBER 20, 2023

My tips for working with code in notebooks are the following: Move auxiliary functions to plain Python modules Generally, importing functions defined in Python modules is better than defining them in the notebook. If a reviewer wants more detail, they can always look at the Python module directly. For one, Git diffs within.py

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Snorkel AI

AUGUST 17, 2023

The latter will map the model’s outputs to final labels and significantly ease the data preparation process. Our examples use Python, but the concepts apply equally well to other coding languages. Other writers have composed thorough and robust tutorials on using the OpenAI Python library or using LangChain.

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The MLOps Blog

MAY 9, 2023

Again, what goes on in this component is subjective to the data scientist’s initial (manual) data preparation process, the problem, and the data used. Metaflow differs from other pipelining frameworks because it can load and store artifacts (such as data and models) as regular Python instance variables.

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Heartbeat

MARCH 28, 2023

Gradio is an open-source Python library that helps you build easy-to-use demos for your ML model that you can share with other people. The ML lifecycle is an ongoing process from data preparation to deployment and monitoring of the model. Let’s move on and have a look at what Gradio is.

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AWS Machine Learning Blog

NOVEMBER 27, 2024

Solution overview The chess demo uses a broad spectrum of AWS services to create an interactive and engaging gaming experience. The following architecture diagram illustrates the service integration and data flow in the demo. The demo offers a few gameplay options. Stockfish and chess Python libraries are GPL-3.0

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Julien Simon

MARCH 31, 2025

The project uses Python and several open-source libraries, including LangChain, Chroma, and Gradio. Data Preparation The first step in building the RAG chatbot is to prepare the data. If youre interested in exploring this technology further, we encourage youto: Book a demo to see Arcee Conductor inaction.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

Several activities are performed in this phase, such as creating the model, data preparation, model training, evaluation, and model registration. Model lineage tracking captures and retains information about the stages of an ML workflow, from data preparation and training to model registration and deployment.

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AWS Machine Learning Blog

JANUARY 28, 2025

RAG retrieves data from a preexisting knowledge base (your data), combines it with the LLMs knowledge, and generates responses with more human-like language. However, in order for generative AI to understand your data, some amount of data preparation is required, which involves a big learning curve. Choose Next.

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ODSC - Open Data Science

MARCH 18, 2025

Allen Downey, PhD, Principal Data Scientist at PyMCLabs Allen is the author of several booksincluding Think Python, Think Bayes, and Probably Overthinking Itand a blog about data science and Bayesian statistics. A prolific educator, Julien shares his knowledge through code demos, blogs, and YouTube, making complex AI accessible.

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AWS Machine Learning Blog

FEBRUARY 18, 2025

The following sections further explain the main components of the solution: ETL pipelines to transform the log data, agentic RAG implementation, and the chat application. Creating ETL pipelines to transform log data Preparing your data to provide quality results is the first step in an AI project.

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