Flipboard

JULY 24, 2023

It is also called the second brain as it can store data that is not arranged according to a present data model or schema and, therefore, cannot be stored in a traditional relational database or RDBMS. It has an official website from which you can access the premium version of Quivr by clicking on the button ‘Try demo.’

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Iguazio

MAY 21, 2024

In the end, we show a demo of a chatbot that was developed with crowdsourcing. AI, Yaron Haviv, co-founder and CTO of Iguazio (acquired by McKinsey) and Guy Lecker, ML Engineer Team Lead at Iguazio, which you can watch here. Model servers (LLM, CNN, etc.) The post LLM Validation and Evaluation appeared first on Iguazio.

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AWS Machine Learning Blog

NOVEMBER 10, 2023

Creating high-performance machine learning (ML) solutions relies on exploring and optimizing training parameters, also known as hyperparameters. It provides key functionality that allows you to focus on the ML problem at hand while automatically keeping track of the trials and results. We use a Random Forest from SkLearn.

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DataRobot Blog

SEPTEMBER 13, 2022

Data scientists drive business outcomes. Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. They develop and continuously optimize AI/ML models , collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value.

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DataRobot

DECEMBER 16, 2021

and train models with a single click of a button. Advanced users will appreciate tunable parameters and full access to configuring how DataRobot processes data and builds models with composable ML. Access the full potential of your models by using DataRobot with your text data. Request a Demo.

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Iguazio

FEBRUARY 8, 2024

They are characterized by their enormous size, complexity, and the vast amount of data they process. These elements need to be taken into consideration when managing, streamlining and deploying LLMs in ML pipelines, hence the specialized discipline of LLMOps. Data Pipeline - Manages and processes various data sources.

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Iguazio

MAY 29, 2024

In the application pipeline, teams can swap: Logging inputs + responses to various data sources (database, stream, file, etc.) Additional data sources (RAG, web search, etc.) Classical ML models and LLMs If using QLORA to fine-tune, teams can swap out domain specific fine tuned adapters while using the same base model (e.g.

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phData

OCTOBER 27, 2023

Setup The demo is available in this repo. Creating an end-to-end feature platform with an offline data store, online data store, feature store, and feature pipeline requires a bit of initial setup. Creating the Feature Store This demo uses Feast as the feature store, Snowflake as the offline store, and Redis as the online store.

Machine Learning 52

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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Mlearning.ai

AUGUST 18, 2023

As an ML engineer you’re in charge of some code/model. MLOps cover all of the rest, how to track your experiments, how to share your work, how to version your models etc (Full list in the previous post. ). Not having a local model is not an excuse to throw organization, versioning and just good ol’ clean code patterns for.

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Snorkel AI

OCTOBER 18, 2023

From onboarding new customers to analyzing pictures or videos of damages for evaluation, machine learning (ML) and AI offer exciting possibilities for optimization and cost-saving across the insurance industry. Claims data is often noisy, unstructured, and multi-modal. Book a demo today.

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Iguazio

JANUARY 22, 2024

If you train a model on blogs that have toxic language or bias language towards different genders you get the same results. The result will be the inability to trust the model’s results. For example, when indexing a new version of a document, it’s important to take care of versioning in the ML pipeline.

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Snorkel AI

OCTOBER 18, 2023

From onboarding new customers to analyzing pictures or videos of damages for evaluation, machine learning (ML) and AI offer exciting possibilities for optimization and cost-saving across the insurance industry. Claims data is often noisy, unstructured, and multi-modal. Book a demo today.

AI 59

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Snorkel AI

OCTOBER 18, 2023

From onboarding new customers to analyzing pictures or videos of damages for evaluation, machine learning (ML) and AI offer exciting possibilities for optimization and cost-saving across the insurance industry. Claims data is often noisy, unstructured, and multi-modal. Book a demo today. See what Snorkel option is right for you.

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phData

MARCH 1, 2024

Sigma Computing makes organizing, displaying, and understanding clinical information easy with its intuitive interface, clear tables & visualizations, and convenient suite of data exploration options – all operating at Snowflake speed. Contact us today for a demo. What is the Clinical Cohort Creation Accelerator?

Machine Learning 52

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Iguazio

FEBRUARY 20, 2024

Building MLOpsPedia This demo on Github shows how to fine tune an LLM domain expert and build an ML application Read More Building Gen AI for Production The ability to successfully scale and drive adoption of a generative AI application requires a comprehensive enterprise approach. Let’s dive into the data management pipeline.

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Heartbeat

SEPTEMBER 7, 2023

Think of it as a meticulously organized lab notebook, but one that can handle the complexity of modern ML workflows. It provides interactive charts and visualizations to help you gain insights into your model’s behavior. This is essential for understanding which changes led to improved (or degraded) model performance.

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The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

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ODSC - Open Data Science

APRIL 6, 2023

But its status as the go-between for programming and data professionals isn’t its only power. Within SQL you can also filter data, aggregate it and create valuations, manipulate data, update it, and even do data modeling.

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Heartbeat

DECEMBER 27, 2023

The machine learning (ML) lifecycle defines steps to derive values to meet business objectives using ML and artificial intelligence (AI). Here are some details about these packages: jupyterlab is for model building and data exploration. catboost is the machine learning algorithm for model building. Flask==2.1.2

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AWS Machine Learning Blog

MAY 31, 2024

For this example, we created a bucket with versioning enabled with the name bedrock-kb-demo-gdpr. Select the uploaded file and from Actions dropdown and choose the Query with S3 Select option to query the.csv data using SQL if the data was loaded correctly. After you create the bucket, upload the.csv file to the bucket.

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AWS Machine Learning Blog

NOVEMBER 21, 2024

Make sure you’re updating the data model ( updateTrackListData function) to handle your custom fields. Start building your annotation workflow today and contribute to the next generation of AI models that push the boundaries of what’s possible in audio and video generation. val(option).text(option)); append(qualityCheck).append(qualityLabel));

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Alation

SEPTEMBER 7, 2021

Through features like agile approval, Analytics Stewardship facilitates direct communication of policies to data scientists and analysts within their day-to-day workflow. The catalog automatically suggests new business glossary terms using AI/ML and links those terms to relevant data, saving valuable time and effort for stewards.

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Flipboard

MARCH 23, 2023

While it is still very large, it is significantly smaller than models like GPT-3 while offering similar performance. Bard Google’s code name for its chat-oriented search engine, based on their LaMDA model, and only demoed once in public. The real tests will come when these models are connected to critical systems.

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Tableau

DECEMBER 1, 2021

I did not realize as Chris demoed his prototype PhD system that it would become Tableau Desktop , a product used today by millions of people around the world to see and understand data, including in Fortune 500 companies, classrooms, and nonprofit organizations. Visual encoding is key to explaining ML models to humans.

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Tableau

DECEMBER 1, 2021

I did not realize as Chris demoed his prototype PhD system that it would become Tableau Desktop , a product used today by millions of people around the world to see and understand data, including in Fortune 500 companies, classrooms, and nonprofit organizations. Visual encoding is key to explaining ML models to humans.

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Mlearning.ai

JUNE 16, 2023

Generative AI can be used to automate the data modeling process by generating entity-relationship diagrams or other types of data models and assist in UI design process by generating wireframes or high-fidelity mockups. GitHub - cirolini/chatgpt-github-actions Aims to automate code review using the ChatGPT language model.

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