Towards AI

AUGUST 23, 2024

Last Updated on September 2, 2024 by Editorial Team Author(s): Ori Abramovsky Originally published on Towards AI. Like regular ML, LLM hyperparameters (e.g., Photo by Daniel K Cheung on Unsplash Large Language Models (LLMs) are the latest buzz, often seen as both exciting and intimidating.

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Data Science Dojo

AUGUST 24, 2023

ML models have grown significantly in recent years, and businesses increasingly rely on them to automate and optimize their operations. However, managing ML models can be challenging, especially as models become more complex and require more resources to train and deploy. What is MLOps?

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AWS Machine Learning Blog

JANUARY 26, 2023

Amazon SageMaker is a fully managed machine learning (ML) service providing various tools to build, train, optimize, and deploy ML models. ML insights facilitate decision-making. To assess the risk of credit applications, ML uses various data sources, thereby predicting the risk that a customer will be delinquent.

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Towards AI

AUGUST 16, 2023

Last Updated on August 17, 2023 by Editorial Team Author(s): Jeff Holmes MS MSCS Originally published on Towards AI. Jason Leung on Unsplash AI is still considered a relatively new field, so there are really no guides or standards such as SWEBOK. 85% or more of AI projects fail [1][2]. 85% or more of AI projects fail [1][2].

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Pickl AI

FEBRUARY 17, 2025

This scenario highlights a common reality in the Machine Learning landscape: despite the hype surrounding ML capabilities, many projects fail to deliver expected results due to various challenges. Statistics reveal that 81% of companies struggle with AI-related issues ranging from technical obstacles to economic concerns.

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Flipboard

MAY 1, 2025

In this post, we introduce agentic automatic mortgage approval, a next-generation sample solution that uses autonomous AI agents powered by Amazon Bedrock Agents and Amazon Bedrock Data Automation. Powered by Amazon Bedrock Data Automation, it adapts to changing document formats and data sources, further reducing manual work.

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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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DrivenData Labs

JANUARY 22, 2025

Final Stage Overall Prizes where models were rigorously evaluated with cross-validation and model reports were judged by a panel of experts. The cross-validations for all winners were reproduced by the DrivenData team. Lower is better. Unsurprisingly, the 0.10 quantile was easier to predict than the 0.90

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AWS Machine Learning Blog

MAY 24, 2023

In this three-part series, we present a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. In the following sections, we discuss the stages of the process in detail.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

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Towards AI

AUGUST 23, 2024

Last Updated on September 2, 2024 by Editorial Team Author(s): Ori Abramovsky Originally published on Towards AI. Simplifying LLM Development: Treat It Like Regular ML Photo by Daniel K Cheung on Unsplash Large Language Models (LLMs) are the latest buzz, often seen as both exciting and intimidating.

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DrivenData Labs

MAY 22, 2024

Also, I have 10 years of experience with C++ cross-platform development, especially in the medical imaging domain, and for embedded solutions. Vitaly Bondar: ML Team lead in theMind (formerly Neuromation) company with 6 years of experience in ML/AI and almost 20 years of experience in the industry.

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AWS Machine Learning Blog

DECEMBER 13, 2024

Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. Ensemble models are becoming popular within the ML communities. Pipelines can quickly be used to create and end-to-end ML pipeline for ensemble models.

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AWS Machine Learning Blog

FEBRUARY 25, 2025

AI now plays a pivotal role in the development and evolution of the automotive sector, in which Applus+ IDIADA operates. In this post, we showcase the research process undertaken to develop a classifier for human interactions in this AI-based environment using Amazon Bedrock. values.tolist()) y_train = df_train['agent'].values.tolist()

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AWS Machine Learning Blog

MAY 31, 2024

Here, we use AWS HealthOmics storage as a convenient and cost-effective omic data store and Amazon Sagemaker as a fully managed machine learning (ML) service to train and deploy the model. With SageMaker Training, a managed batch ML compute service, users can efficiently train models without having to manage the underlying infrastructure.

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AWS Machine Learning Blog

OCTOBER 3, 2024

For information about how you can manage and process your own unstructured data, see Unstructured data management and governance using AWS AI/ML and analytics services. He is also a member of the AWS Canada Generative AI vTeam and has helped a growing number of Canadian companies successful launch advanced Generative AI use-cases.

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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. ML-based predictive models nowadays may consider time-dependent components — seasonality, trends, cycles, irregular components, etc. — to

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Ocean Protocol

FEBRUARY 2, 2023

Indeed, the most robust predictive trading algorithms use machine learning (ML) techniques. On the optimistic side, algorithmically trading assets with predictive ML models can yield enormous gains à la Renaissance Technologies… Yet algorithmic trading gone awry can yield enormous losses as in the latest FTX scandal.

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Pickl AI

NOVEMBER 28, 2024

Introduction Machine Learning ( ML ) is revolutionising industries, from healthcare and finance to retail and manufacturing. As businesses increasingly rely on ML to gain insights and improve decision-making, the demand for skilled professionals surges. This growth signifies Python’s increasing role in ML and related fields.

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Towards AI

JULY 19, 2023

Last Updated on July 19, 2023 by Editorial Team Author(s): Yashashri Shiral Originally published on Towards AI. Sales Prediction| Using Time Series| End-to-End Understanding| Part -2 Sales Forecasting determines how the company invests and grows to create a massive impact on company valuation.

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Ocean Protocol

SEPTEMBER 29, 2023

AI / ML offers tools to give a competitive edge in predictive analytics, business intelligence, and performance metrics. By leveraging cross-validation, we ensured the model’s assessment wasn’t reliant on a singular data split. Ocean tools enable people to privately & securely publish, exchange, and consume data.

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Mlearning.ai

JULY 6, 2023

AI-generated image ( craiyon ) In machine learning (ML), a hyperparameter is a parameter whose value is given by the user and used to control the learning process. This is in contrast to other parameters, whose values are obtained algorithmically via training.

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DataRobot Blog

DECEMBER 20, 2022

The use of artificial intelligence (AI) in the investment sector is proving to be a significant disruptor, catalyzing the connection between the different players and delivering a more vivid picture of the future risk and opportunities across all different market segments. Understand & Explain Models with DataRobot Trusted AI.

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Pickl AI

MAY 17, 2023

Training data plays an important role in deciding the effectiveness of an ML model. However, an overfitting ML model can work on data but produces less accurate output because the model has memorized the existing data points and fails to predict unseen data. K-fold Cross Validation ML experts use cross-validation to resolve the issue.

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Mlearning.ai

SEPTEMBER 24, 2023

Figure 1: Brute Force Search It is a cross-validation technique. Figure 2: K-fold Cross Validation On the one hand, it is quite simple. Running a cross-validation model of k = 10 requires you to run 10 separate models. The result is the optimal combination of values from this set. Johnston, B. and Mathur, I.

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Pickl AI

JULY 26, 2023

To mitigate variance in machine learning, techniques like regularization, cross-validation, early stopping, and using more diverse and balanced datasets can be employed. Cross-Validation Cross-validation is a widely-used technique to assess a model’s performance and find the optimal balance between bias and variance.

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Pickl AI

JULY 12, 2024

Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence. Python’s simplicity, versatility, and extensive library support make it the go-to language for AI development. Python is renowned for its simplicity and versatility, making it an ideal choice for AI applications.

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phData

MAY 24, 2024

That’s why Snowflake Data Cloud created Cortex, an AI service built directly in Snowflake that’s easy to use and understand. Cortex offers pre-built ML functions for tasks like forecasting and anomaly detection and access to industry-leading large language models (LLMs) for working with unstructured text data.

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Ocean Protocol

FEBRUARY 1, 2024

Challenge Overview Objective : Building upon the insights gained from Exploratory Data Analysis (EDA), participants in this data science competition will venture into hands-on, real-world artificial intelligence (AI) & machine learning (ML). About Ocean Protocol Ocean was founded to level the playing field for AI and data.

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DrivenData Labs

APRIL 10, 2024

We take a gap year to participate in AI competitions and projects, and organize and attend events. We look for AI competitions that contribute to the UN SDGs, and have a timeframe of 2~3 months. Combining deep and practical understanding of technology, computer vision and AI with experience in big data architectures.

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Pickl AI

JANUARY 3, 2024

The growing application of Machine Learning also draws interest towards its subsets that add power to ML models. Key takeaways Feature engineering transforms raw data for ML, enhancing model performance and significance. EDA, imputation, encoding, scaling, extraction, outlier handling, and cross-validation ensure robust models.

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DagsHub

APRIL 21, 2024

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD. What is MLOps?

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Ocean Protocol

AUGUST 8, 2023

This deployed hyperparameters tuning and cross-validation to ensure an effective and generalizable model. Describe the ML model you chose and explain why it suited this task. Explain how the ML model contributed to your analysis and supported your findings in the report.

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Pickl AI

SEPTEMBER 17, 2024

Understanding these questions will equip aspiring AI professionals with the knowledge needed to excel in interviews and navigate the evolving AI landscape. As the technology continues to evolve, it is crucial for aspiring AI practitioners to stay up-to-date with the latest trends, concepts, and best practices.

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Pickl AI

MARCH 26, 2024

Experimentation and cross-validation help determine the dataset’s optimal ‘K’ value. Cross-Validation: Employ techniques like k-fold cross-validation to evaluate model performance and prevent overfitting. Unlock Your Data Science Career with Pickl.AI

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Heartbeat

OCTOBER 23, 2023

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms.

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Mlearning.ai

FEBRUARY 10, 2024

2874 == Results for DecisionTreeClassifier: Best Parameters: {'DecisionTreeClassifier__max_depth': 3} Best Cross-Validation Score: 0.8374095963137334 Test Accuracy: 0.8347251217814892 Classification Report: precision recall f1-score support 0 0.84 Medium’s Boost / New Multimodal Models / BEST AI Assistants Mlearning.ai

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