Smart Data Collective

OCTOBER 20, 2021

As per the TDWI survey, more than a third (nearly 37%) of people has shown dissatisfaction with their ability to access and integrate complex data streams. Why is Data Integration a Challenge for Enterprises? How Can AI Transform Data Integration? This speeds up data transformation and decision-making.

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Smart Data Collective

DECEMBER 19, 2021

The rise of machine learning and the use of Artificial Intelligence gradually increases the requirement of data processing. That’s because the machine learning projects go through and process a lot of data, and that data should come in the specified format to make it easier for the AI to catch and process.

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DataRobot

NOVEMBER 12, 2021

Only one in ten companies reports significant financial benefits from implementing AI, according to a recent study by BCG GAMMA, the BCG Henderson Institute, and the MIT Sloan Management Review. Few businesses now benefit from AI because they have not yet fully implemented it throughout their organizations. How can that be?

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Dataversity

JANUARY 13, 2021

In marketing, artificial intelligence (AI) is the process of using data models, mathematics, and algorithms to generate insights that marketers can use. Marketers use insights gained from AI to guide future decisions on event spending, strategy, and content topics. AI also […].

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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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Dataconomy

MAY 2, 2023

They design, develop, and deploy the machine learning algorithms that power everything from self-driving cars to personalized recommendations. In the context of a business, machine learning engineers are responsible for creating bots that are utilized for chat purposes or data collection. They build the future.

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Pickl AI

JUNE 23, 2023

In the world of artificial intelligence (AI), data plays a crucial role. It is the lifeblood that fuels AI algorithms and enables machines to learn and make intelligent decisions. And to effectively harness the power of data, organizations are adopting data-centric architectures in AI.

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Data Science Dojo

JANUARY 10, 2023

Data scientists will be replaced by artificial intelligence As artificial intelligence advances, a common misconception arises that AI will replace all human intelligent labor. Today’s AI systems, even the most advanced ones, require human guidance to work. Data scientists only work on predictive modeling Another myth!

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Pickl AI

AUGUST 1, 2023

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types. What is Machine Learning?

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Cambridge Intelligence

FEBRUARY 20, 2024

Experienced analysts and investigators have an important part to play at every stage, as do advanced technologies like AI and machine learning. But to improve the speed, accuracy and effectiveness of decision making, there’s been a rise in the usage of AI technologies and machine learning.

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IBM Journey to AI blog

APRIL 5, 2024

Predict functionality builds predictive models to predict imminent failures and calculate assets’ remaining useful life. These models often incorporate machine learning and AI algorithms to detect the onset of degradation mechanisms in an early stage.

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Iguazio

MAY 21, 2024

In this blog post, we explain the role of LLM evaluation in AI lifecycles and the different types of LLM evaluation methods. This blog post is based on a webinar with Ehud Barnea, PhD, Head of AI at Tasq. This includes: Cleaning and preparing data during pre-processing, before it is sent to the model.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Pickl AI

APRIL 3, 2023

Accordingly, Machine Learning allows computers to learn and act like humans by providing data. Apparently, ML algorithms ensure to train of the data enabling the new data input to make compelling predictions and deliver accurate results. What is Supervised Learning?

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ODSC - Open Data Science

MARCH 23, 2023

Real-time Analytics & Built-in Machine Learning Models with a Single Database Akmal Chaudhri, Senior Technical Evangelist at SingleStore, explores the importance of delivering real-time experiences in today’s big data industry and how data models and algorithms rely on powerful and versatile data infrastructure.

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DataRobot

JUNE 23, 2021

To help data scientists experiment faster, DataRobot has added Composable ML to automated machine learning. This allows data science teams to incorporate any machine learning algorithm or feature engineering method and seamlessly combine them with hundreds of built-in methods. Leverage Built-in Insights to Debug Models.

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Data Science Dojo

MARCH 7, 2023

These skills include programming languages such as Python and R, statistics and probability, machine learning, data visualization, and data modeling. To perform exploratory data analysis effectively, data scientists must have a strong understanding of math and statistics.

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Pickl AI

JULY 31, 2023

Examples of Eager Learning Algorithms: Logistic Regression : A classic Eager Learning algorithm used for binary classification tasks. Support Vector Machines (SVM) : SVM is a powerful Eager Learning algorithm used for both classification and regression tasks. Eager Learning Algorithms: How does it work?

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ODSC - Open Data Science

MAY 10, 2023

Unfortunately, even the data science industry — which should recognize tabular data’s true value — often underestimates its relevance in AI. Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication.

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Smart Data Collective

JUNE 4, 2021

Some of the applications of data science are driverless cars, gaming AI, movie recommendations, and shopping recommendations. Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating data models.

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AssemblyAI

JUNE 27, 2023

Over the past decade, we've witnessed significant advancements in AI-powered audio generation techniques, including music and speech synthesis. Listen to the following short audio clip: Guitar solo 0:00 / 0:09 1× This was generated in a handful of seconds by Google’s audio-generative model MusicLM.

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IBM Journey to AI blog

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

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IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. This led to the theory and development of AI.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service. Based on this tenet, we can classify generative AI users who need LLM evaluation capabilities into 3 groups as shown in the following figure: model providers, fine-tuners, and consumers.

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Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

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Pickl AI

AUGUST 23, 2023

Predictive Analytics One of the most remarkable aspects of Data Science in stock market analysis is its predictive capabilities. Through sophisticated algorithms and Machine Learning models , data scientists can predict stock price movements with a degree of accuracy that was previously unthinkable.

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ODSC - Open Data Science

FEBRUARY 14, 2023

Trained with 570 GB of data from books and all the written text on the internet, ChatGPT is an impressive example of the training that goes into the creation of conversational AI. ChatGPT is a next-generation language model (referred to as GPT-3.5) But, like other large language models, it can be amusingly wrong.

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How to Learn Machine Learning

JANUARY 21, 2023

Need inspiration building AI systems? As Artificial Intelligence (AI) continues to become more and more prevalent in our daily lives, it’s no surprise that more and more people are eager to learn how to work with the technology. Creating a virtual personal assistant starts with understanding the basics of AI and NLP.

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Pickl AI

JULY 3, 2023

It is the process of converting raw data into relevant and practical knowledge to help evaluate the performance of businesses, discover trends, and make well-informed choices. Data gathering, data integration, data modelling, analysis of information, and data visualization are all part of intelligence for businesses.

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DataRobot

FEBRUARY 22, 2021

We started the 10x Academy in 2020 to address employers’ need for talent with strong applied automated AI skills and workers’ desire to upskill. The founding of the 10X Academy is part of DataRobot’s commitment to developing automation that improves the productivity of data scientists while democratizing access to AI for non-data scientists.

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Snorkel AI

MARCH 16, 2023

Dillon Laird is an Engineering Manager at Landing AI where he leads the machine learning team. He presented the session “LandingLens: A Journey in Democratizing AI” at Snorkel AI’s 2022 Future of Data-Centric AI (FDCAI) Conference. The ML life cycle is broken down into three stages: data, model, and deploy.

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Snorkel AI

MARCH 16, 2023

Dillon Laird is an Engineering Manager at Landing AI where he leads the machine learning team. He presented the session “LandingLens: A Journey in Democratizing AI” at Snorkel AI’s 2022 Future of Data-Centric AI (FDCAI) Conference. The ML life cycle is broken down into three stages: data, model, and deploy.

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IBM Journey to AI blog

APRIL 10, 2023

The network state consumers are various analytical modules that may vary from a simple threshold-based algorithms that analyze the current network state to very complex AI/ML-based algorithms that predict the future network state. Analytics Layer: analyzing the data for the various Use Cases to provide actionable insights.

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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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Iguazio

FEBRUARY 8, 2024

Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. LLMOps (Large Language Model Operations), is a specialized domain within the broader field of machine learning operations (MLOps). LLMOps is MLOps for LLMs. How Does LLMOps Work?

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DataRobot Blog

OCTOBER 11, 2022

Want to learn how AI/ML can be so effective in this space? So how can AI/ML help McLaren Formula 1 Team, one of the sports oldest and most successful teams, in this space? Learn more about how McLaren is using data and AI to gain a competitive advantage here.). The How – Data, Modeling, and Predictions!

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ODSC - Open Data Science

APRIL 26, 2023

Though just about every industry imaginable utilizes the skills of a data-focused professional, each has its own challenges, needs, and desired outcomes. This is why you’ll often find that there are jobs in AI specific to an industry, or desired outcome when it comes to data. Well then, you’re in luck. Check it out here !

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