AWS Machine Learning Blog

DECEMBER 18, 2023

MongoDB’s robust time series data management allows for the storage and retrieval of large volumes of time-series data in real-time, while advanced machine learning algorithms and predictive capabilities provide accurate and dynamic forecasting models with SageMaker Canvas. Setup the Database access and Network access.

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Clustering AWS Database ML 121

Pickl AI

NOVEMBER 4, 2024

Mathematics is critical in Data Analysis and algorithm development, allowing you to derive meaningful insights from data. Linear algebra is vital for understanding Machine Learning algorithms and data manipulation. Scikit-learn covers various classification , regression , clustering , and dimensionality reduction algorithms.

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Data Science Python Machine Learning Machine Learning 52

IBM Journey to AI blog

MARCH 21, 2024

GPUs (graphics processing units) and TPUs (tensor processing units) are specifically designed to handle complex mathematical computations central to AI algorithms, offering significant speedups compared with traditional CPUs. Additionally, using in-memory databases and caching mechanisms minimizes latency and improves data access speeds.

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Apache Hadoop AI AI Natural Language Processing 79

Smart Data Collective

SEPTEMBER 13, 2020

These Hadoop based tools archive links and keep track of them. They use a sophisticated data-driven algorithm to assess the quality of these sites based on the volume and quantity of inbound links. This algorithm is known as Google PageRank. It’s a bad idea to link from the same domain, or the same cluster of domains repeatedly.

Analytics 120

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Dataconomy

JULY 24, 2023

Concepts such as linear algebra, calculus, probability, and statistical theory are the backbone of many data science algorithms and techniques. Coding skills are essential for tasks such as data cleaning, analysis, visualization, and implementing machine learning algorithms. Specializing can make you stand out from other candidates.

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AWS Machine Learning Blog

MAY 16, 2024

Therefore, we decided to introduce a deep learning-based recommendation algorithm that can identify not only linear relationships in the data, but also more complex relationships. Recommendation model using NCF NCF is an algorithm based on a paper presented at the International World Wide Web Conference in 2017.

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Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

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Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing.

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Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

DECEMBER 9, 2024

Familiarise yourself with essential tools like Hadoop and Spark. What are the Main Components of Hadoop? Hadoop consists of the Hadoop Distributed File System (HDFS) for storage and MapReduce for processing data across distributed systems. What is the Role of a NameNode in Hadoop ? What is a DataNode in Hadoop?

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IBM Journey to AI blog

SEPTEMBER 25, 2023

With a few taps on a mobile device, riders request a ride; then, Uber’s algorithms work to match them with the nearest available driver and calculate the optimal price. Automation enabled Uber to grow to their current state with more than 256 petabytes of data, 3,000 nodes and 12 clusters. But the simplicity ends there.

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Data Lakes Analytics Analytics Clustering 102

Pickl AI

DECEMBER 2, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. For example, financial institutions utilise high-frequency trading algorithms that analyse market data in milliseconds to make investment decisions. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

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Pickl AI

NOVEMBER 25, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. For example, financial institutions utilise high-frequency trading algorithms that analyse market data in milliseconds to make investment decisions. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

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Big Data Big Data Data Lakes Apache Hadoop 52

AWS Machine Learning Blog

FEBRUARY 13, 2024

The BigBasket team was running open source, in-house ML algorithms for computer vision object recognition to power AI-enabled checkout at their Fresho (physical) stores. Model training was accelerated by 50% through the use of the SMDDP library, which includes optimized communication algorithms designed specifically for AWS infrastructure.

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Smart Data Collective

NOVEMBER 8, 2020

This will enable you to leverage the right algorithms to create good, well structured, and performing software. Spark outperforms old parallel systems such as Hadoop, as it is written using Scala and helps interface with other programming languages and other tools such as Dask. Data processing is often done in batches.

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Data Engineering Data Engineering Data Engineer Data Engineering 104

Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. Below, we explore some of the most widely used algorithms in ML.

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Pickl AI

JULY 25, 2023

With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently. These models may include regression, classification, clustering, and more.

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Pickl AI

MAY 29, 2024

These skills encompass proficiency in programming languages, data manipulation, and applying Machine Learning Algorithms , all essential for extracting meaningful insights and making data-driven decisions. Machine Learning Algorithms Understanding and implementing Machine Learning Algorithms is a core requirement.

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Pickl AI

SEPTEMBER 17, 2024

This section will highlight key tools such as Apache Hadoop, Spark, and various NoSQL databases that facilitate efficient Big Data management. Apache Hadoop Hadoop is an open-source framework that allows for distributed storage and processing of large datasets across clusters of computers using simple programming models.

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Mlearning.ai

MAY 23, 2023

Check out this course to build your skillset in Seaborn —  [link] Big Data Technologies Familiarity with big data technologies like Apache Hadoop, Apache Spark, or distributed computing frameworks is becoming increasingly important as the volume and complexity of data continue to grow.

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Pickl AI

JULY 10, 2023

Hence, you can use R for classification, clustering, statistical tests and linear and non-linear modelling. Packages like caret, random Forest, glmnet, and xgboost offer implementations of various machine learning algorithms, including classification, regression, clustering, and dimensionality reduction.

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Data Science Data Scientist Machine Learning Machine Learning 52

ODSC - Open Data Science

JANUARY 7, 2025

Just as a writer needs to know core skills like sentence structure and grammar, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and soon. Hadoop, though less common in new projects, is still crucial for batch processing and distributed storage in large-scale environments.

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AWS Machine Learning Blog

APRIL 19, 2024

This solution includes the following components: Amazon Titan Text Embeddings is a text embeddings model that converts natural language text, including single words, phrases, or even large documents, into numerical representations that can be used to power use cases such as search, personalization, and clustering based on semantic similarity.

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Pickl AI

MARCH 10, 2023

Furthermore, it ensures that data is consistent while effectively increasing the readability of the data’s algorithm. Using machine learning algorithms, data from these sources can be effectively controlled and further improve the utilisation of the data. This can help companies to access information quickly and faster than usual.

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Pickl AI

JULY 18, 2023

With its powerful ecosystem and libraries like Apache Hadoop and Apache Spark, Java provides the tools necessary for distributed computing and parallel processing. This environment allows users to write, execute, and debug code in a seamless manner, facilitating rapid prototyping and exploration of algorithms. About Pickl.AI

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Data Science SQL Data Scientist Python 52

Pickl AI

MAY 31, 2023

Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines. After that, move towards unsupervised learning methods like clustering and dimensionality reduction. To obtain practical expertise, run the algorithms on datasets.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Pickl AI

JANUARY 28, 2025

The company employs advanced algorithms and BI tools to analyse vast amounts of data generated from user interactions across its platforms. By consolidating data from over 10,000 locations and multiple websites into a single Hadoop cluster, Walmart can analyse customer purchasing trends and optimize inventory management.

Business Intelligence 52

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DagsHub

NOVEMBER 11, 2024

We use data-specific preprocessing and ML algorithms suited to each modality to filter out noise and inconsistencies in unstructured data. Additionally, context-aware algorithms enhance data quality by interpreting information based on its surrounding context, improving relevance for specific tasks. Tools like Unstructured.io

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Pickl AI

JULY 20, 2023

Techniques like regression analysis, time series forecasting, and machine learning algorithms are used to predict customer behavior, sales trends, equipment failure, and more. Use machine learning algorithms to build a fraud detection model and identify potentially fraudulent transactions.

Analytics 40

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Pickl AI

MAY 15, 2023

The type of data processing enables division of data and processing tasks among the multiple machines or clusters. Distributed processing is commonly in use for big data analytics, distributed databases and distributed computing frameworks like Hadoop and Spark. It essentially helps in the generation of a desirable output.

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DagsHub

OCTOBER 23, 2024

Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. Apache Hadoop Apache Hadoop is an open-source framework that supports the distributed processing of large datasets across clusters of computers. Data Processing Tools These tools are essential for handling large volumes of unstructured data.

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Pickl AI

FEBRUARY 23, 2025

These tools leverage advanced algorithms and methodologies to process large datasets, uncovering valuable insights that can drive strategic decision-making. Best Big Data Tools Popular tools such as Apache Hadoop, Apache Spark, Apache Kafka, and Apache Storm enable businesses to store, process, and analyse data efficiently.

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Dataconomy

MARCH 19, 2025

Predictive analytics utilizes statistical algorithms and machine learning to forecast future outcomes based on historical data. Additionally, biases in algorithms can lead to skewed results, highlighting the need for careful data validation. Roles of data professionals Various professionals contribute to the data science ecosystem.

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