Remove 2020 Remove Algorithm Remove Data Pipeline
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The 2021 Executive Guide To Data Science and AI

Applied Data Science

Automation Automating data pipelines and models ➡️ 6. The Data Engineer Not everyone working on a data science project is a data scientist. Data engineers are the glue that binds the products of data scientists into a coherent and robust data pipeline.

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AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

Wearable devices (such as fitness trackers, smart watches and smart rings) alone generated roughly 28 petabytes (28 billion megabytes) of data daily in 2020. And in 2024, global daily data generation surpassed 402 million terabytes (or 402 quintillion bytes). Massive, in fact.

Big Data 106
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Pioneering computer vision: Aleksandr Timashov, ML developer

Dataconomy

I led several projects that dramatically advanced the company’s technological capabilities: Real-time Video Analytics for Security: We developed an advanced system integrating deep learning algorithms with existing CCTV infrastructure. A key challenge was mapping drone inspection detections to real-world maps.

ML 91
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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN. This is accomplished by breaking the problem into independent parts so that each processing element can complete its part of the workload algorithm simultaneously.

AWS 113
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How to become an AI Architect?

Pickl AI

They possess a deep understanding of AI technologies, algorithms, and frameworks and have the ability to translate business requirements into robust AI systems. AI Engineers focus primarily on implementing and deploying AI models and algorithms, working closely with data scientists and machine learning experts.

AI 52
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When his hobbies went on hiatus, this Kaggler made fighting COVID-19 with data his mission | A…

Kaggle

David: My technical background is in ETL, data extraction, data engineering and data analytics. I spent over a decade of my career developing large-scale data pipelines to transform both structured and unstructured data into formats that can be utilized in downstream systems.

ETL 71
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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

When answering a new question in real time, the input question is converted to an embedding, which is used to search for and extract the most similar chunks of documents using a similarity metric, such as cosine similarity, and an approximate nearest neighbors algorithm. The search precision can also be improved with metadata filtering.

SQL 133