AWS Machine Learning Blog

JULY 8, 2024

TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. Amazon CloudWatch – Collects and visualizes real-time logs that provide the basis for automation. Used to deploy training and inference code.

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AWS Machine Learning Blog

NOVEMBER 12, 2024

Led by agricultural technology innovators, generative AI is the latest AI technology that helps agronomists and researchers have open-ended human-like interactions with computing applications to assist with a variety of tasks and automate historically manual processes. AWS Lambda is then used to further enrich the data.

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Generative AI Prompt Engineer Prompt Engineering AI 112

Unite.AI

JUNE 24, 2024

It’s been fascinating to see the shifting role of the data scientist and the software engineer in these last twenty years since machine learning became widespread. Having worn both hats, I am very aware of the importance of the software development lifecycle (especially automation and testing) as applied to machine learning projects.

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Data Scientist Large Language Models Machine Learning Software Engineer 130

Mlearning.ai

FEBRUARY 15, 2024

That’s the reason why Robotic Process Automation (RPA) is gaining traction across industries, including the financial and banking sectors. Currently available technologies empower the automation of multiple jobs in different spheres. What is Robotic Process Automation in Banking? What is Robotic Process Automation in Finance?

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Robotics Automation Software Engineer Software Development 52

AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

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AWS Machine Learning Blog

FEBRUARY 21, 2023

Amazon SageMaker for MLOps provides purpose-built tools to automate and standardize steps across the ML lifecycle, including capabilities to deploy and manage new models using advanced deployment patterns. SageMaker deployment guardrails Guardrails are an essential part of software development.

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DataRobot Blog

JULY 21, 2022

Data aggregation such as from hourly to daily or from daily to weekly time steps may also be required. Perform data quality checks and develop procedures for handling issues. Typical data quality checks and corrections include: Missing data or incomplete records Inconsistent data formatting (e.g.,

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AWS Machine Learning Blog

NOVEMBER 15, 2023

As a first step, they wanted to transcribe voice calls and analyze those interactions to determine primary call drivers, including issues, topics, sentiment, average handle time (AHT) breakdowns, and develop additional natural language processing (NLP)-based analytics. He has 20 years of enterprise software development experience.

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Data Ingestion Metadata NLP Data Scientist 126

AWS Machine Learning Blog

OCTOBER 15, 2024

Next, the SageMaker Ground Truth Plus team sets up data labeling workflows, which changes the batch status to In progress. Annotators label the data, and you complete your data quality check by accepting or rejecting the labeled data. Rejected objects go back to annotators to re-label.

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Unite.AI

AUGUST 15, 2024

This is the common belief that if you just build cool software, people will line up to buy it. This never works, and the solution is a robust marketing process connected with your software development process. What role do large language models (LLMs) play in Tamr’s data quality and enrichment processes?

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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. It was evident that a fundamental shift towards a unified and standardized environment was imperative to truly unleash the potential of data-driven endeavors at Philips.

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Data Scientist ML Data Science Automation 114

Topbots

SEPTEMBER 11, 2023

While each of them offers exciting perspectives for research, a real-life product needs to combine the data, the model, and the human-machine interaction into a coherent system. AI development is a highly collaborative enterprise. So, you can add more examples to an existing dataset using data augmentation.

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UX Design AI AI Automation 59

O'Reilly Media

NOVEMBER 28, 2023

Automating the process of building complex prompts has become common, with patterns like retrieval-augmented generation (RAG) and tools like LangChain. Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3% Developers are learning how to find quality data and build models that work.

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Generative AI AI AI Data Analysis 110

Bugra Akyildiz

MAY 25, 2024

Interactive Query Refinement: ChatGPT could engage in an interactive dialog to refine and clarify the user's data analysis requirements, suggesting additional filters, aggregations, or visualizations based on the initial query. gradients of energies to compute forces).

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ChatGPT Machine Learning OpenAI Data Analysis 59

AWS Machine Learning Blog

MARCH 9, 2023

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. This has led to the emergence of a data-centric approach to ML and various techniques to improve model performance by focusing on data requirements.

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ML Engineer Convolutional Neural Networks Data Scientist Machine Learning 90

The MLOps Blog

MARCH 15, 2023

Automation : Automating as many tasks to reduce human error and increase efficiency. Collaboration : Ensuring that all teams involved in the project, including data scientists, engineers, and operations teams, are working together effectively. This includes data quality, privacy, and compliance.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Model governance involves overseeing the development, deployment, and maintenance of ML models to help ensure that they meet business objectives and are accurate, fair, and compliant with regulations. It also helps achieve data, project, and team isolation while supporting software development lifecycle best practices.

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AWS Machine Learning Blog

NOVEMBER 7, 2024

It also lets you automate your evaluation process in your pre-production environments. You can set up a test application and a red team with your own employees or automate it against a known set of vulnerabilities. He has over 15 years of combined work experience in machine learning, software development and data science on the cloud.

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Marktechpost

JULY 17, 2023

Consider retail automation in the shape of an Amazon Go location, where a computer vision system monitors shoppers to ensure no one leaves with any five-finger discounts. YData By enhancing the caliber of training datasets, YData offers a data-centric platform that speeds up the creation and raises the return on investment of AI solutions.

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AWS Machine Learning Blog

JULY 31, 2023

It uses Auto-ML, which automates various tasks of ML, including model selection, trying various algorithms relevant to your ML use case, hyperparameter tuning, and creating model explainability reports. Quick build – Builds a simple model in a fraction of the time compared to a standard build, but accuracy is exchanged for speed.

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Snorkel AI

FEBRUARY 27, 2023

This is a platform that supports this new data-centric development loop. This is then used to train models, and those models then power feedback and analyses that guide how to improve the quality of your data and therefore of your models.

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Machine Learning AI AI Large Language Models 52

Snorkel AI

FEBRUARY 27, 2023

This is a platform that supports this new data-centric development loop. This is then used to train models, and those models then power feedback and analyses that guide how to improve the quality of your data and therefore of your models.

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Machine Learning AI AI Large Language Models 52

Dlabs.ai

MARCH 30, 2022

Several breakthroughs enabled us to fix data quality issues within the dataset. Our close collaboration with the client in Poland ended up with us delivering dedicated workshops for the client’s data science team. However, we felt we could still improve it. team loves to do. can help.

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The MLOps Blog

APRIL 17, 2023

Then you must specify, analyze, test, and manage them throughout the software development lifecycle. Creating user stories , analyzing them, and validating requirements are all parts of requirements development that deserve their own article. Using the right tools and automation makes it user-friendly and efficient.

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Metadata Data Scientist Explainability ML 52

The MLOps Blog

JULY 18, 2023

It’s an automated chief of staff that automates conversational tasks. We are aiming to automate that functionality so that every worker in an organization can have access to that help, just like a CEO or someone else in the company would. How do you ensure data quality when building NLP products?

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AWS Machine Learning Blog

JANUARY 26, 2024

Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications. You can also use Amazon SageMaker Model Monitor to evaluate the quality of SageMaker ML models in production, and notify you when there is drift in data quality, model quality, and feature attribution.

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Unite.AI

AUGUST 8, 2024

While single models are suitable in some scenarios, acting as co-pilots, agentic architectures open the door for LLMs to become active components of business process automation. One noteworthy application of LLM-MA systems is call/service center automation. What is an LLM-MA System?

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Unite.AI

MAY 21, 2024

He joined the company as a software developer in 2004 after studying computer science with a heavy focus on databases, distributed systems, software development processes, and genetic algorithms. By 2005, he was responsible for the Database Optimizer team and in 2007 he became Head of Research & Development.

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AWS Machine Learning Blog

SEPTEMBER 9, 2024

Generative artificial intelligence (AI) has revolutionized this by allowing users to interact with data through natural language queries, providing instant insights and visualizations without needing technical expertise. This can democratize data access and speed up analysis. powered by Amazon Bedrock Domo.AI

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Software Architect Generative AI AI AI 119

The MLOps Blog

MARCH 21, 2023

Version control for code is common in software development, and the problem is mostly solved. However, machine learning needs more because so many things can change, from the data to the code to the model parameters and other metadata. Automation is a good MLOps practice for speeding up all parts of that lifecycle.

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Machine Learning Data Scientist ML Metadata 69

Unite.AI

MARCH 13, 2024

While these efforts have made significant strides, the development and deployment of healthcare LLMs face several challenges. Ensuring data quality, addressing potential biases, and maintaining strict privacy and security standards for sensitive medical data are the major concerns.

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