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AI-powered assistants for investment research with multi-modal data: An application of Agents for Amazon Bedrock

AWS Machine Learning Blog

This post is a follow-up to Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets. For unstructured data, the agent uses AWS Lambda functions with AI services such as Amazon Comprehend for natural language processing (NLP). The following diagram illustrates the technical architecture.

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Few-shot prompt engineering and fine-tuning for LLMs in Amazon Bedrock

AWS Machine Learning Blog

AWS, Online Stores, etc.) The transcripts mention continued growth in third-party seller services, advertising, and AWS. Innovation and Partnerships Generative AI initiatives and partnerships (such as Anthropic, Amazon Bedrock, and Amazon CodeWhisperer) are discussed in relation to AWS.

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A Guide to Choose the Best Data Science Bootcamp

Data Science Dojo

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Statistics : Fundamental statistical concepts and methods, including hypothesis testing, probability, and descriptive statistics.

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How to differentiate the thin line separating innovation and risk in experimentation

Aryng

We have seen this as a general trend in start-ups, and we know that it’s an awful feeling! Is a feeling of despair engulfing you due to continuous experiment failures, making you believe that your ideas are inaccurate and wrong?

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Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

In Inferential Statistics, you can learn P-Value , T-Value , Hypothesis Testing , and A/B Testing , which will help you to understand your data in the form of mathematics. It is highly configurable and can integrate with other tools like Git, Docker, and AWS. Things to learn: AWS , GCP , or Microsoft Azure anyone of them.

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Must-Have Skills for a Machine Learning Engineer

Pickl AI

Concepts such as probability distributions, hypothesis testing , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. Scalability Considerations Scalability is a key concern in model deployment.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Statistical Analysis: Hypothesis testing, probability, regression analysis, etc. Cloud Platforms: AWS, Azure, Google Cloud, etc. Skills and Tools of Data Scientists To excel in the field of Data Science, professionals need a diverse skill set, including: Programming Languages: Python, R, SQL, etc.