IBM Data Science in Practice

MARCH 8, 2023

The United States published a Blueprint for the AI Bill of Rights. The growth of the AI and Machine Learning (ML) industry has continued to grow at a rapid rate over recent years. This complexity is compounded even further by the need to manage data across hybrid and multi-cloud environments.

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phData

MAY 28, 2024

Snowflake Cortex is a suite of pre-built machine learning models and AI capabilities designed to accelerate the deployment of AI-driven solutions within the Snowflake ecosystem. Cortex ML functions are aimed at Predictive AI use cases, such as anomaly detection, forecasting , customer segmentation , and predictive analytics.

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Towards AI

OCTOBER 4, 2023

Mike Shakhomirov Originally published on Towards AI. Advanced-Data Engineering and ML Ops with Infrastructure as Code This member-only story is on us. This article is for data and ML Ops engineers who would want to deploy and update ML pipelines using CloudFormation templates.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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The MLOps Blog

MARCH 20, 2023

Long-term ML project involves developing and sustaining applications or systems that leverage machine learning models, algorithms, and techniques. An example of a long-term ML project will be a bank fraud detection system powered by ML models and algorithms for pattern recognition. 2 Ensuring and maintaining high-quality data.

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The MLOps Blog

AUGUST 11, 2023

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. Getting a workflow ready which takes your data from its raw form to predictions while maintaining responsiveness and flexibility is the real deal.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Natural language processing (NLP) has been growing in awareness over the last few years, and with the popularity of ChatGPT and GPT-3 in 2022, NLP is now on the top of peoples’ minds when it comes to AI. Companies are finding NLP to be one of the best applications of AI regardless of industry.

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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IBM Journey to AI blog

OCTOBER 16, 2023

Artificial intelligence (AI) adoption is still in its early stages. As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. ” Are foundation models trustworthy?

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DataRobot

SEPTEMBER 14, 2021

As we see from countless examples, the demand for AI is at a fever pitch across every industry. Becoming AI-driven is no longer really optional. As AI continues to advance at such an aggressive pace, solutions built on machine learning are quickly becoming the new norm. So let’s dive in!

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AWS Machine Learning Blog

APRIL 19, 2023

Business requirements We are the US squad of the Sportradar AI department. Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. These models are then pushed to an Amazon Simple Storage Service (Amazon S3) bucket using DVC, a version control tool for ML models.

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Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

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Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

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IBM Journey to AI blog

JULY 27, 2023

According to IBM’s Institute of Business Value (IBV) , AI can contain contact center cases, enhancing customer experience by 70%. Additionally, AI can increase productivity in HR by 40% and in application modernization by 30%. Overall placing emphasis on establishing a trusted and integrated data platform for AI.

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AWS Machine Learning Blog

APRIL 5, 2024

SageMaker geospatial capabilities make it straightforward for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. Janosch Woschitz is a Senior Solutions Architect at AWS, specializing in AI/ML.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. Their task is to construct and oversee efficient data pipelines.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. They’re looking for people who know all related skills, and have studied computer science and software engineering.

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Iguazio

FEBRUARY 8, 2024

Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. LLM examples include GPT, BERT, and similar advanced AI systems. They are characterized by their enormous size, complexity, and the vast amount of data they process.

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Pickl AI

MAY 15, 2024

As a Data Analyst, you’ve honed your skills in data wrangling, analysis, and communication. But the allure of tackling large-scale projects, building robust models for complex problems, and orchestrating data pipelines might be pushing you to transition into Data Science architecture.

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DagsHub

SEPTEMBER 10, 2023

Join us for a month-long celebration of open-source contributions to Machine Learning and AI projects. Gain hands-on experience building datasets, models, pipelines, and more! Intermediate Data Pipeline : Build data pipelines using DVC for automation and versioning of Open Source Machine Learning projects.

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AWS Machine Learning Blog

DECEMBER 15, 2023

We are excited to announce the launch of Amazon DocumentDB (with MongoDB compatibility) integration with Amazon SageMaker Canvas , allowing Amazon DocumentDB customers to build and use generative AI and machine learning (ML) solutions without writing code. Analyze data using generative AI. Choose Add step.

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IBM Journey to AI blog

AUGUST 11, 2023

Both computer scientists and business leaders have taken note of the potential of the data. Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. MLOps is the next evolution of data analysis and deep learning. What is MLOps?

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IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

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Mlearning.ai

JUNE 16, 2023

Increase your productivity in software development with Generative AI As I mentioned in Generative AI use case article, we are seeing AI-assisted developers. Overall Generative AI in SDLC Here is how Generative AI can help in SDLC stages (we may see more use cases as Generative AI matures).

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phData

FEBRUARY 7, 2024

On the client side, Snowpark consists of libraries, including the DataFrame API and native Snowpark machine learning (ML) APIs for model development (public preview) and deployment (private preview). Machine Learning Training machine learning (ML) models can sometimes be resource-intensive.

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AWS Machine Learning Blog

MARCH 15, 2024

Medical data restrictions You can use machine learning (ML) to assist doctors and researchers in diagnosis tasks, thereby speeding up the process. However, the datasets needed to build the ML models and give reliable results are sitting in silos across different healthcare systems and organizations.

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DagsHub

MAY 7, 2024

Machine learning, particularly its subsets, deep learning, and generative ML, is currently in the spotlight. This surge in interest from both businesses and the general public has been fueled by the excitement around latest AI technologies, such as ChatGPT. This can only be done by rigorous and continuous testing of your ML models.

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DagsHub

MAY 7, 2024

Machine learning, particularly its subsets, deep learning, and generative ML, is currently in the spotlight. This surge in interest from both businesses and the general public has been fueled by the excitement around latest AI technologies, such as ChatGPT. This can only be done by rigorous and continuous testing of your ML models.

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O'Reilly Media

SEPTEMBER 15, 2021

In June 2021, we asked the recipients of our Data & AI Newsletter to respond to a survey about compensation. The average salary for data and AI professionals who responded to the survey was $146,000. The results are biased by the survey’s recipients (subscribers to O’Reilly’s Data & AI Newsletter ).

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phData

JANUARY 31, 2024

In this blog, we’ll show you how to build a robust energy price forecasting solution within the Snowflake Data Cloud ecosystem. We’ll cover how to get the data via the Snowflake Marketplace, how to apply machine learning with Snowpark , and then bring it all together to create an automated ML model to forecast energy prices.

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AWS Machine Learning Blog

OCTOBER 18, 2023

Purina used artificial intelligence (AI) and machine learning (ML) to automate animal breed detection at scale. The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring.

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phData

JUNE 7, 2023

In Short It is clear that AI is being used to power up businesses now more than ever, and companies that don’t use AI risk being outcompeted by those who do. The best AI-powered products are fueled by a diverse collection of high-quality data. But how do you take the first steps toward implementing AI?

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Alation

JUNE 21, 2022

Over time, we called the “thing” a data catalog , blending the Google-style, AI/ML-based relevancy with more Yahoo-style manual curation and wikis. Thus was born the data catalog. In our early days, “people” largely meant data analysts and business analysts. ML and DataOps teams). data pipelines) to support.

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Alation

APRIL 4, 2023

However, the race to the cloud has also created challenges for data users everywhere, including: Cloud migration is expensive, migrating sensitive data is risky, and navigating between on-prem sources is often confusing for users. To build effective data pipelines, they need context (or metadata) on every source.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

Organizations can search for PII using methods such as keyword searches, pattern matching, data loss prevention tools, machine learning (ML), metadata analysis, data classification software, optical character recognition (OCR), document fingerprinting, and encryption.

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Iguazio

NOVEMBER 16, 2023

An MLOps platform enables streamlining and automating the entire ML lifecycle, from model development and training to deployment and monitoring. In this blog post, we explore what is required from you for each option and provide tools that will help you make the right choice for your organization to scale your ML and AI activities.

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