The MLOps Blog

JUNE 27, 2023

Data storage and versioning You need data storage and versioning tools to maintain data integrity, enable collaboration, facilitate the reproducibility of experiments and analyses, and ensure accurate ML model development and deployment. Easy collaboration, annotator management, and QA workflows.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

MAY 1, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Chatbots 129

Chatbots Automation Machine Learning DevOps 129

AWS Machine Learning Blog

APRIL 29, 2024

Close collaboration with AWS Trainium has also played a major role in making the Arcee platform extremely performant, not only accelerating model training but also reducing overall costs and enforcing compliance and data integrity in the secure AWS environment. Malikeh Ehghaghi is an Applied NLP Research Engineer at Arcee.

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AWS Machine Learning Blog

AUGUST 4, 2023

With this capability, businesses can access their Salesforce data securely with a zero-copy approach using SageMaker and use SageMaker tools to build, train, and deploy AI models. The inference endpoints are connected with Data Cloud to drive predictions in real time.

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John Snow Labs

SEPTEMBER 26, 2024

Data integration has been an enormous challenge in healthcare for decades.

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AWS Machine Learning Blog

AUGUST 1, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API.

Generative AI 125

Generative AI Machine Learning Artificial Intelligence Artificial Intelligence 125

Heartbeat

OCTOBER 12, 2023

Challenges in Multi-Modal Learning Multi-modal learning, the convergence of multiple data modalities (e.g., Heterogeneous Data Integration : Combining data from different modalities that differ in format, scale, and dimensionality requires careful integration.

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Unite.AI

JUNE 17, 2024

This involves defining clear policies and procedures for how data is collected, stored, accessed, and used within the organization. It should include guidelines for data quality, data integration, and data security, as well as defining roles and responsibilities for data management.

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Automation Artificial Intelligence Artificial Intelligence Generative AI 147

ODSC - Open Data Science

JANUARY 30, 2024

Not only does it involve the process of collecting, storing, and processing data so that it can be used for analysis and decision-making, but these professionals are responsible for building and maintaining the infrastructure that makes this possible; and so much more.

Data Science 52

Data Science Data Scientist Data Mining Machine Learning 52

AWS Machine Learning Blog

SEPTEMBER 30, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Generative AI 90

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Pickl AI

JUNE 23, 2023

By leveraging comprehensive and accurate data, it provides more informed and evidence-based decisions, leading to improved outcomes across various domains. Ethical and Responsible AI Data-centric AI architecture promotes ethical practices and responsible AI development.

Continuous Learning 52

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ODSC - Open Data Science

FEBRUARY 21, 2024

EVENT — ODSC East 2024 In-Person and Virtual Conference April 23rd to 25th, 2024 Join us for a deep dive into the latest data science and AI trends, tools, and techniques, from LLMs to data analytics and from machine learning to responsible AI.

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DevOps Data Science Metadata Machine Learning 52

Pickl AI

NOVEMBER 23, 2023

We will navigate the landscape of AI’s ascendancy and underscore the pivotal role of integrating AI with robust data protection measures. Understanding AI privacy issues In delving into the intricate realm of AI privacy issues, real-world examples vividly illuminate the challenges.

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Pickl AI

SEPTEMBER 26, 2024

AI can forecast customer needs and market trends, helping businesses anticipate changes and adapt their strategies accordingly. Enhancing Agility and Responsiveness AI strategies facilitate real-time monitoring of business operations, allowing companies to quickly respond to changes in the market or operational inefficiencies.

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Artificial Intelligence Artificial Intelligence AI Strategy Machine Learning 52

Pickl AI

DECEMBER 6, 2024

Its decentralised nature ensures data integrity while reducing fraud risks across various sectors. AI Governance Platforms As Artificial Intelligence becomes more prevalent, the need for governance frameworks to ensure ethical use grows.

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Automation Robotics Algorithm Generative AI 52

Defined.ai blog

SEPTEMBER 7, 2023

We see it as a valuable resource for those at the forefront of AI and healthcare integration, paving the way for more precise and responsible AI outcomes. As we explore the immense sea of information, certain best practices ensure that our AI endeavors are efficient and ethically sound.

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Artificial Intelligence Artificial Intelligence AI AI 52

Pickl AI

NOVEMBER 26, 2024

This distinction shifts some responsibility to the end user, necessitating robust practices like multi-factor authentication and encryption. Role of Network Security in Enabling Safe Cloud Operations Effective network security ensures seamless cloud operations by protecting data integrity and maintaining user trust.

Machine Learning 52

Machine Learning Artificial Intelligence Artificial Intelligence Automation 52

Unite.AI

FEBRUARY 26, 2025

Some of these threats include model theft and inversion attacks, in which attackers extract sensitive information or reverse-engineer AI models, potentially exposing proprietary data or intellectual property. To proactively address these risks, organizations need to embed security at every stage of the AI lifecycle.

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Automation AI Modeling Machine Learning Data Integration 130

Flipboard

DECEMBER 3, 2024

The success of this solution highlights AI’s potential to transform traditional agricultural practices, opening doors for further innovations across the sector. As Cropwise AI continues to evolve, efforts will focus on expanding capabilities, enhancing data integration, and maintaining compliance with shifting regulatory standards.

Generative AI 150

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Unite.AI

JULY 5, 2024

Healthcare players must proactively align with evolving ethical standards to ensure Gen AI applications are fair, responsible, and patient-focused. A human-in-the-loop approach, combined with responsible AI practices, can help achieve equitable healthcare outcomes while maximizing Gen AI's potential.

Data Integration 147

Data Integration Data Quality Algorithm AI Modeling 147

IBM Journey to AI blog

JUNE 20, 2024

AI governance establishes the frameworks, rules and standards that direct AI research, development and application to ensure safety, fairness and respect for human rights. Learn how IBM Consulting can help weave responsible AI governance into the fabric of your business. The result is that AI systems often lack oversight.

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Unite.AI

MAY 30, 2024

Users heavily rely on platform providers for security, leaving data vulnerable to risks. Regular audits, encryption, and secure access controls are essential for mitigating these risks and maintaining data integrity.

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AI Developer AI Development AI Tools AI 221

Pickl AI

DECEMBER 10, 2024

As the global AI market, valued at $196.63 from 2024 to 2030, implementing trustworthy AI is imperative. This blog explores how AI TRiSM ensures responsible AI adoption. Key Takeaways AI TRiSM embeds fairness, transparency, and accountability in AI systems, ensuring ethical decision-making.

Explainability 52

Explainability AI AI Artificial Intelligence 52

Unite.AI

NOVEMBER 21, 2024

This shift is also leading to new types of work in IT services, such as developing custom models, data engineering for AI needs and implementing responsible AI. The evolution of AI is promising but also brings many corporate challenges, especially around ethical considerations in how we implement it.

DevOps 147

DevOps Automation Responsible AI Software Development 147

AWS Machine Learning Blog

NOVEMBER 12, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Generative AI 113

Generative AI Prompt Engineering Prompt Engineer AI 113

AWS Machine Learning Blog

OCTOBER 21, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

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