Dataconomy

MARCH 27, 2023

Robotic process automation vs machine learning is a common debate in the world of automation and artificial intelligence. However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. What is machine learning (ML)?

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Heartbeat

JUNE 5, 2023

In recent years, machine learning has exploded in popularity because of its wide range of potential uses in fields including healthcare, finance, eCommerce, and the arts. However, managing machine learning projects can be challenging, especially as the size and complexity of the data and models increase.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

In these scenarios, as you start to embrace generative AI, large language models (LLMs) and machine learning (ML) technologies as a core part of your business, you may be looking for options to take advantage of AWS AI and ML capabilities outside of AWS in a multicloud environment.

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phData

AUGUST 24, 2023

However, these models are evolving, with machine learning now playing an essential role in refining and improving the accuracy and efficiency of credit scoring and decisioning. How Does Machine Learning Impact These Models? Below are just some of the advantages provided by incorporating machine learning in credit models.

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Pickl AI

JULY 28, 2023

Image Recognition using Machine Learning and MATLAB: With the advent of Machine Learning , it is now possible for computers to recognize and decipher objects, patterns, and other properties in digital photographs. In the early days of computer vision, handcrafted features like edges, corners, and textures were used.

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Heartbeat

FEBRUARY 1, 2023

Machine learning has become an essential part of our lives because we interact with various applications of ML models, whether consciously or unconsciously. Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. What is MLOps?

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AWS Machine Learning Blog

MARCH 8, 2023

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. We add this data to Snowflake as a new table.

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Becoming Human

MAY 12, 2023

In what ways do we understand image annotations, the underlying technology behind AI and machine learning (ML), and its importance in developing accurate and adequate AI training data for machine learning models? Overall, it shows the more data you have, the better your AI and machine learning models are.

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Data Science Dojo

AUGUST 28, 2023

Similar to traditional Machine Learning Ops (MLOps), LLMOps necessitates a collaborative effort involving data scientists, DevOps engineers, and IT professionals. The scope of LLMOps within machine learning projects can vary widely, tailored to the specific needs of each project.

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Towards AI

AUGUST 24, 2023

Common mistakes and misconceptions about learning AI/ML Markus Spiske on Unsplash A common misconception of beginners is that they can learn AI/ML from a few tutorials that implement the latest algorithms, so I thought I would share some notes and advice on learning AI. Trying to code ML algorithms from scratch.

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The MLOps Blog

NOVEMBER 30, 2022

These days enterprises are sitting on a pool of data and increasingly employing machine learning and deep learning algorithms to forecast sales, predict customer churn and fraud detection, etc., ML model versioning: where are we at? The short answer is we are in the middle of a data revolution.

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IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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Heartbeat

JANUARY 12, 2024

Let's learn more about 'few-shot learning' and 'contrastive learning.' It leverages sentence transformers to embed the text data and fine-tunes the head layer to perform the classification task. Here, the objective is to extract the food-related data from the Alpaca dataset.

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The MLOps Blog

DECEMBER 19, 2022

Classification is one of the most widely applied areas in Machine Learning. As Data Scientists, we all have worked on an ML classification model. A Multiclass Classification is a class of problems where a given data point is classified into one of the classes from a given list. Let’s take a look at some of them.

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AWS Machine Learning Blog

JANUARY 6, 2023

The rules in this engine were predefined and written in SQL, which aside from posing a challenge to manage, also struggled to cope with the proliferation of data from TR’s various integrated data source. TR customer data is changing at a faster rate than the business rules can evolve to reflect changing customer needs.

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AWS Machine Learning Blog

JUNE 22, 2023

Utilizing data streamed through LnW Connect, L&W aims to create better gaming experience for their end-users as well as bring more value to their casino customers. Predictive maintenance is a common ML use case for businesses with physical equipment or machinery assets.

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Heartbeat

JANUARY 9, 2024

This is brought on by various developments, such as the availability of data, the creation of more potent computer resources, and the development of machine learning algorithms. Data privacy and security are equally vital, safeguarding sensitive textual information from potential breaches.

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The MLOps Blog

FEBRUARY 22, 2024

TL;DR: In many machine-learning projects, the model has to frequently be retrained to adapt to changing data or to personalize it. Continual learning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. What is continual learning?

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Mlearning.ai

NOVEMBER 29, 2023

How to Use Machine Learning (ML) for Time Series Forecasting — NIX United The modern market pace calls for a respective competitive edge. Data forecasting has come a long way since formidable data processing-boosting technologies such as machine learning were introduced.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

Sharing in-house resources with other internal teams, the Ranking team machine learning (ML) scientists often encountered long wait times to access resources for model training and experimentation – challenging their ability to rapidly experiment and innovate. If it shows online improvement, it can be deployed to all the users.

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AWS Machine Learning Blog

FEBRUARY 22, 2023

In this post, we discuss how Boomi used the bring-your-own-container (BYOC) approach to develop a new AI/ML enabled solution for their customers to tackle the “blank canvas” problem. Boomi’s ML and data engineering teams needed the solution to be deployed quickly, in a repeatable and consistent way, at scale.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. Third, a number of sessions will be of interest to ML practitioners who build, deploy, and operationalize both traditional and generative AI models.

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IBM Journey to AI blog

MARCH 27, 2024

These innovations have showcased strong performance in comparison to conventional machine learning (ML) models, particularly in scenarios where labelled data is in short supply. This framework comprises four distinct modules: data preparation, domain adaptation, classification and emission computation.

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Heartbeat

JULY 17, 2023

Comet, a cloud-based machine learning platform, offers a powerful solution for tracking, comparing, and benchmarking fine-tuned models, allowing users to easily analyze and visualize their performance. Comet allows ML engineers to track these metrics in real-time and visualize their performance using interactive dashboards.

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Heartbeat

MAY 16, 2023

Photo by ROMAN ODINTSOV: [link] Introduction Did you know that machine learning is one of the most popular approaches for sentiment analysis? Machine learning algorithms can be trained on large datasets of labeled examples to automatically learn patterns and relationships between textual features and sentiment labels.

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DagsHub

APRIL 21, 2024

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. You need to make that model available to the end users, monitor it, and retrain it for better performance if needed.

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Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. Data scientists and ML engineers require capable tooling and sufficient compute for their work.

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AWS Machine Learning Blog

AUGUST 14, 2023

SageMaker JumpStart SageMaker JumpStart serves as a model hub encapsulating a broad array of deep learning models for text, vision, audio, and embedding use cases. Some of the models offer capabilities for you to fine-tune them with your own data. In recent years, ML techniques have become increasingly popular to enhance search.

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ODSC - Open Data Science

JANUARY 29, 2024

They design intricate sequences of prompts, leveraging their knowledge of AI, machine learning, and data science to guide powerful LLMs (Large Language Models) towards complex tasks. Thus while crafting clever prompts for chatbots might be part of the picture, the prompt engineer role is far more intricate.

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The MLOps Blog

APRIL 5, 2023

Have you ever spent weeks or months building a machine learning model, only to later find out that deploying it into a production environment is complicated and time-consuming? Machine learning model packaging is crucial to the machine learning development lifecycle.

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IBM Data Science in Practice

MARCH 21, 2023

Deep learning models built using Maximo Visual Inspection (MVI) are used for a wide range of applications, including image classification and object detection. These models train on large datasets and learn complex patterns that are difficult for humans to recognize. What are the types of image processing ML models?

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Heartbeat

AUGUST 21, 2023

AlexNet significantly improved performance over previous approaches and helped popularize deep learning and CNNs. This helps avoid disappearing gradients in very deep networks, allowing ResNet to attain cutting-edge performance on a wide range of computer vision applications.

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Towards AI

JULY 19, 2023

Please refer to Part 1– to understand what is Sales Prediction/Forecasting, the Basic concepts of Time series modeling, and EDA I’m working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model. Data Preparation — Collect data, Understand features 2.

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Mlearning.ai

JUNE 1, 2023

Using PyTorch Deep Learning Framework and CNN Architecture Photo by Andrew S on Unsplash Motivation Build a proof-of-concept for Audio Classification using a deep-learning neural network with PyTorch framework. During training, images are streamed into the neural network.

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Snorkel AI

AUGUST 22, 2023

We use machine learning algorithms to analyze and understand the descriptive information (e.g. Example above shows results for “modern yellow sofa” We develop machine learning algorithms to extract product tags from images which are available when suppliers upload products to our catalog.

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