Towards AI

AUGUST 7, 2024

From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams Photo by Parabol | The Agile Meeting Toolbox on Unsplash In this article, we will explore the essential VS Code extensions that enhance productivity and collaboration for data scientists and machine learning (ML) engineers.

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KDnuggets

SEPTEMBER 25, 2019

Python Libraries for Interpretable Machine Learning; Scikit-Learn: A silver bullet for basic machine learning; I wasn't getting hired as a Data Scientist. So I sought data on who is; Which Data Science Skills are core and which are hot/emerging ones?

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KDnuggets

SEPTEMBER 11, 2019

How one person overcame rejections applying to Data Scientist positions by getting actual data on who is getting hired; Advice from Andrew Ng on building ML career and reading research papers; 10 Great Python resources for Data Scientists; Python Libraries for Interpretable ML,

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Data Science 101

APRIL 29, 2019

Here is the latest data science news for the week of April 29, 2019. From Data Science 101. The Go Programming Language for Data Science Quick Video Tutorial for Find Updates in Azure Two-Minute Papers, One Pixel attack on NN. General Data Science.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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AWS Machine Learning Blog

MAY 10, 2023

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter.

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KDnuggets

SEPTEMBER 9, 2019

Also: Python Libraries for Interpretable Machine Learning; TensorFlow vs PyTorch vs Keras for NLP; Advice on building a machine learning career and reading research papers by Prof. Andrew Ng; Object-oriented programming for data scientists: Build your ML estimator.

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KDnuggets

NOVEMBER 14, 2019

Let’s take a look at traditional testing methodologies and how we can apply these to our data/ML pipelines.

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KDnuggets

SEPTEMBER 25, 2019

Learn about unexpected risk of AI applied to Big Data; Study 5 Sampling Algorithms every Data Scientist needs to know; Read how one data scientist copes with his boring days of deploying machine learning; 5 beginner-friendly steps to learn ML with Python; and more.

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Google Research AI blog

APRIL 7, 2023

As part of Google's Crisis Response and our efforts to address the climate crisis , we are using machine learning (ML) models for Flood Forecasting to alert people in areas that are impacted before disaster strikes. Frederik Kratzert (Research Scientist at Google) provided an overview of the Caravan project (below).

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AWS Machine Learning Blog

APRIL 7, 2025

Discover Llama 4 models in SageMaker JumpStart SageMaker JumpStart provides FMs through two primary interfaces: SageMaker Studio and the Amazon SageMaker Python SDK. Alternatively, you can use the SageMaker Python SDK to programmatically access and use SageMaker JumpStart models. billion to a projected $574.78

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KDnuggets

OCTOBER 9, 2019

Also: 12 things I wish I'd known before starting as a Data Scientist; 10 Free Top Notch Natural Language Processing Courses; The Last SQL Guide for Data Analysis; The 4 Quadrants of #DataScience Skills and 7 Principles for Creating a Viral DataViz.

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AWS Machine Learning Blog

APRIL 19, 2023

Right now, most deep learning frameworks are built for Python, but this neglects the large number of Java developers and developers who have existing Java code bases they want to integrate the increasingly powerful capabilities of deep learning into. The DJL was created at Amazon and open-sourced in 2019.

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The MLOps Blog

APRIL 5, 2023

In this comprehensive guide, we’ll explore the key concepts, challenges, and best practices for ML model packaging, including the different types of packaging formats, techniques, and frameworks. Best practices for ml model packaging Here is how you can package a model efficiently.

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Flipboard

APRIL 13, 2023

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of scalable compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are transforming their businesses.

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AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. Second, open source Metaflow provides the necessary software infrastructure to build production-grade ML/AI systems in a developer-friendly manner.

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AWS Machine Learning Blog

JULY 29, 2024

Amazon Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time series forecasts. Benefits of SageMaker Canvas Forecast customers have been seeking greater transparency, lower costs, faster training, and enhanced controls for building time series ML models.

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Data Science 101

NOVEMBER 22, 2019

AWS Storage Day On November 20, 2019, Amazon held AWS Storage Day. Much of this is in anticipation of AWS re:Invent, coming in early December 2019. PyTorch on Azure with streamlined ML lifecycle Microsoft Azure supports the latest version of PyTorch. Many announcements came out regarding storage of all types at AWS.

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AWS Machine Learning Blog

APRIL 4, 2023

AWS provides various services catered to time series data that are low code/no code, which both machine learning (ML) and non-ML practitioners can use for building ML solutions. We recommend running this notebook on Amazon SageMaker Studio , a web-based, integrated development environment (IDE) for ML. References Dua, D.

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ODSC - Open Data Science

SEPTEMBER 21, 2023

The following is an extract from Andrew McMahon’s book , Machine Learning Engineering with Python, Second Edition. Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. What does an ML solution look like?

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ODSC - Open Data Science

APRIL 10, 2023

There are countless blockers that may keep you from getting your ML projects off the ground, including: The time required to understand and stitch together a fragmented ecosystem of low-level, ML-specific packages. The engineering effort involved with building and maintaining infrastructure that can execute large-scale ML workloads.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

According to a 2019 survey by Deloitte , only 18% of businesses reported being able to take advantage of unstructured data. Consider the following machine learning (ML) problem: user asks a large language model (LLM) question: “How to filter and search models in Amazon Bedrock?”. Choose Python (PySpark) for this use-case.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

Run the following command to install the AWS SDK for Python (Boto3). Boto3 makes it straightforward to integrate a Python application, library, or script with AWS services. Boto3 makes it straightforward to integrate a Python application, library, or script with AWS services. Tell me again what was the revenue in 2019?

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Flipboard

FEBRUARY 2, 2023

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. We then explain the details of the ML methodology and model training procedures.

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AWS Machine Learning Blog

AUGUST 7, 2023

AWS ProServe solved this use case through a joint effort between the Generative AI Innovation Center (GAIIC) and the ProServe ML Delivery Team (MLDT). However, LLMs are not a new technology in the ML space. The new ML workflow now starts with a pre-trained model dubbed a foundation model.

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ODSC - Open Data Science

FEBRUARY 17, 2023

TensorFlow is desired for its flexibility for ML and neural networks, PyTorch for its ease of use and innate design for NLP, and scikit-learn for classification and clustering. BERT is still very popular over the past few years and even though the last update from Google was in late 2019 it is still widely deployed.

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The MLOps Blog

AUGUST 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Stefan: Yeah.

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Towards AI

APRIL 19, 2024

Intelligent Medical Objects 👉Industry domain: AI, Health Tech, IT, NLP, Software, Analytics, Generative AI 👉Location: 3 offices 👉Year founded: 1994 👉Programming languages deployed: Angular, C#, SQL, Scikit, TensorFlow, Spark, GitHub, R, Python 👉Benefits: Flexible time off, family medical leave, pet insurance, (..)

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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Mlearning.ai

JUNE 14, 2023

Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube BECOME a WRITER at MLearning.ai // Try These FREE ML Tools Today! Happy learning. Originally published at [link].

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phData

JANUARY 31, 2024

We’ll cover how to get the data via the Snowflake Marketplace, how to apply machine learning with Snowpark , and then bring it all together to create an automated ML model to forecast energy prices. Python has long been the favorite programming language of data scientists.

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AWS Machine Learning Blog

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. The quality of our labels will affect the quality of our ML model. This three-step process is generic and can be used for any model architecture and ML framework of your choice.

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AWS Machine Learning Blog

JUNE 13, 2023

The size of the machine learning (ML) models––large language models ( LLMs ) and foundation models ( FMs )–– is growing fast year-over-year , and these models need faster and more powerful accelerators, especially for generative AI. and Hugging Face transformers (v4.7.0), the main libraries used in this experiment, ran on Python 3.8.

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DrivenData Labs

DECEMBER 10, 2023

degree in AI and ML specialization from Gujarat University, earned in 2019. He has diligently refined his abilities in the development, deployment, and scaling of AI and ML models, offering substantial contributions to GenAI projects. His educational background includes a Master's in AI and ML from John Moorse University, UK.

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PyImageSearch

FEBRUARY 3, 2025

Gradio is an open-source Python library that empowers developers to build interactive web interfaces for their machine learning models, APIs, or any Python functions with ease. Gradio is an open-source Python library that simplifies the creation of user-friendly web interfaces for machine learning models, APIs, or any Python function.

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Mlearning.ai

FEBRUARY 28, 2023

In 2018–2019, while new car sales were recorded at 3.6 These are common Python libraries used for data analysis and visualization. Submission Suggestions Linear Regression for tech start-up company Cars4U in Python was originally published in MLearning.ai million units, around 4 million second-hand cars were bought and sold.

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Heartbeat

NOVEMBER 29, 2023

In contrast, PyTorch embraces a more intuitive and Pythonic nature , inviting newcomers to the world of deep learning with open arms. However, it embraces the Python ecosystem’s flexibility, allowing developers to use visualization tools such as Matplotlib. PyTorch lacks a native equivalent to TensorBoard.

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