IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

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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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Great Learning

NOVEMBER 7, 2023

While the terms Data Science, Artificial Intelligence (AI), and Machine learning fall in the same domain and are connected, they have specific applications and meanings. There may be overlaps in these domains now and then, but each of these three terms has unique uses.

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Towards AI

APRIL 10, 2024

How much machine learning really is in ML Engineering? There are so many different data- and machine-learning-related jobs. But what actually are the differences between a Data Engineer, Data Scientist, ML Engineer, Research Engineer, Research Scientist, or an Applied Scientist?! It’s so confusing!

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AWS Machine Learning Blog

NOVEMBER 19, 2024

This year, generative AI and machine learning (ML) will again be in focus, with exciting keynote announcements and a variety of sessions showcasing insights from AWS experts, customer stories, and hands-on experiences with AWS services. Visit the session catalog to learn about all our generative AI and ML sessions.

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Marktechpost

APRIL 29, 2024

Acquiring skills in data science enables professionals to unlock new opportunities for innovation and gain a competitive edge in today’s digital age. This article lists the top data science courses one should take to master the necessary skills and meet the growing demand for data expertise in various industries.

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ODSC - Open Data Science

MARCH 12, 2025

The field of data science has evolved dramatically over the past several years, driven by technological breakthroughs, industry demands, and shifting priorities within the community. 20212024: Interest declined as deep learning and pre-trained models took over, automating many tasks previously handled by classical ML techniques.

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Analytics Vidhya

JUNE 1, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction In this article, I’m gonna explain about DBSCAN algorithm. The post Understand The DBSCAN Clustering Algorithm! appeared first on Analytics Vidhya.

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AI News

DECEMBER 15, 2023

Key hurdles with edge AI adoption Grande highlighted three primary pain points companies face when attempting to productise edge machine learning models, including difficulties determining optimal data collection strategies, scarce AI expertise, and cross-disciplinary communication barriers between hardware, firmware, and data science teams.

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ODSC - Open Data Science

MARCH 12, 2025

As a machine learning (ML) practitioner, youve probably encountered the inevitable request: Can we do something with AI? Stephanie Kirmer, Senior Machine Learning Engineer at DataGrail, addresses this challenge in her talk, Just Do Something with AI: Bridging the Business Communication Gap for ML Practitioners. The key takeaway?

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DataRobot Blog

DECEMBER 6, 2022

Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. These data science teams are seeing tremendous results—millions of dollars saved, new customers acquired, and new innovations that create a competitive advantage.

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Becoming Human

JANUARY 23, 2023

Data Science You heard this term most of the time all over the internet, as well this is the most concerning topic for newbies who want to enter the world of data but don’t know the actual meaning of it. I’m not saying those are incorrect or wrong even though every article has its mindset behind the term ‘ Data Science ’.

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AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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Flipboard

OCTOBER 25, 2023

After decades of digitizing everything in your enterprise, you may have an enormous amount of data, but with dormant value. However, with the help of AI and machine learning (ML), new software tools are now available to unearth the value of unstructured data. The solution integrates data in three tiers.

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Towards AI

JULY 22, 2024

I have been studying machine learning for the past 6 years, in which I worked as an ML student researcher for over 2 years, and have even written my first 3 papers. My journey started studying computer engineering, not knowing what ML was or even that it existed, to where I am now, soon joining my favorite AI startup as a research scientist!

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Towards AI

JANUARY 22, 2025

In this regard, I believe the future of data science belongs to those: who can connect the dots and deliver results across the entire data lifecycle. You have to understand data, how to extract value from them and how to monitor model performances. These two languages cover most data science workflows.

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Towards AI

OCTOBER 19, 2024

Yet, for all their sophistication, they often can’t explain their choices — this lack of transparency isn’t just frustrating — it’s increasingly problematic as AI becomes more integrated into critical areas of our lives. Enter Explainable AI (XAI), a field dedicated to making AI’s decision-making process more transparent and understandable.

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Artificial Corner

JANUARY 23, 2025

This is what I did when I started learning Python for data science. I checked the curriculum of paid data science courses and then searched all the stuff related to Python. I selected the best 4 free courses I took to learn Python for data science. It makes machine learning model building easy for beginners.

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AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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DataRobot Blog

MARCH 16, 2023

Traditionally, developing appropriate data science code and interpreting the results to solve a use-case is manually done by data scientists. The integration allows you to generate intelligent data science code that reflects your use case. Data scientists still need to review and evaluate these results.

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IBM Journey to AI blog

MAY 9, 2023

Consistent principles guiding the design, development, deployment and monitoring of models are critical in driving responsible, transparent and explainable AI. Building responsible AI requires upfront planning, and automated tools and processes designed to drive fair, accurate, transparent and explainable results.

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Towards AI

MAY 6, 2024

A typical SHAP Plot — Image by Author In Part 1 of Data Science Case Study — Credit Default Prediction, we have talked about feature engineering, model training, model evaluation and classification threshold selection. Model Explainability Our model can make predictions by feeding into it the features. Let’s jump in!

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Marktechpost

APRIL 7, 2024

With the rapid advancements in machine learning (ML), there has been an increase in the demand for MLOps specialists as well. The book teaches how to build robust training loops and how to deploy scalable ML systems. The book also teaches how to design MLOps life cycle to ensure that the models are unbiased, fair, and explainable.

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IBM Journey to AI blog

MAY 18, 2023

” “IBM has strengths in decision intelligence technologies, authoring tools, explainability, and ModelOps.” Intelligent , helping businesses to take predictions and scores from external data science services into account and apply them to make decisions.

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Unite.AI

APRIL 9, 2024

These techniques include Machine Learning (ML), deep learning , Natural Language Processing (NLP) , Computer Vision (CV) , descriptive statistics, and knowledge graphs. Key benefits include: reducing the necessity of large data science teams. Explainability is essential for accountability, fairness, and user confidence.

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Towards AI

FEBRUARY 6, 2024

What you need to expect when entering the field of ML research. So, with this post, I definitely don’t want to talk down the ML researcher career, but I want to shed some light on what the harsh reality of being an ML researcher can look like and whether it is something for you.

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ODSC - Open Data Science

MARCH 10, 2025

AI and data science are advancing at a lightning-fast pace with new skills and applications popping up left and right. Explainable AI for Decision-Making Applications Patrick Hall, Assistant Professor at GWSB and Principal Scientist at HallResearch.ai

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IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. An AI governance framework ensures the ethical, responsible and transparent use of AI and machine learning (ML). The development and use of these models explain the enormous amount of recent AI breakthroughs.

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AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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Heartbeat

FEBRUARY 13, 2023

Before you go… If you liked this article and want to stay tuned with more exciting articles on Python & Data Science  — do consider becoming a medium member by clicking here [link]. In this way, the portion of the membership fee goes to me, which motivates me to write more exciting stuff on Python and Data Science.

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ODSC - Open Data Science

MARCH 20, 2025

Explore the must-attend sessions and cutting-edge tracks designed to equip AI practitioners, data scientists, and engineers with the latest advancements in AI and machine learning. The ODSC East 2025 Schedule: 150+ AI & Data Science Sessions, Keynotes, &More ODSC East 2025 is THE AI & data science event of the year!

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Marktechpost

JUNE 10, 2024

Turn ML Models into Online Apps This course demonstrates how to transform machine learning (ML) models into online apps using the GitLab DevSecOps Platform and Vertex AI. It allows learners to gain practical insights through a detailed demo to integrate ML models into web applications seamlessly.

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Pickl AI

JANUARY 29, 2024

Summary: In the tech landscape of 2024, the distinctions between Data Science and Machine Learning are pivotal. Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. The collective strength of both forms the groundwork for AI and Data Science, propelling innovation.

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Flipboard

MARCH 7, 2023

This post presents a solution that uses a workflow and AWS AI and machine learning (ML) services to provide actionable insights based on those transcripts. We use multiple AWS AI/ML services, such as Contact Lens for Amazon Connect and Amazon SageMaker , and utilize a combined architecture. Validation set 11 1500 0.82

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AWS Machine Learning Blog

JANUARY 23, 2025

In this post, we explain how to automate this process. The solution described in this post is geared towards machine learning (ML) engineers and platform teams who are often responsible for managing and standardizing custom environments at scale across an organization. Ajay Raghunathan is a Machine Learning Engineer at AWS.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.

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Towards AI

DECEMBER 15, 2024

When it comes to machine learning regression models, interviewers typically focus on five key performance metrics, which are the ones mostly used by Data Scientists in real time. In this article, I have explained each of these key metrics in a short and concise way, using real-life examples to make them easy to understand.

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