ODSC - Open Data Science

APRIL 28, 2023

Whether it’s an insurance company leveraging location for better underwriting or risk assessment, a financial services organization enriching transactions for validation and accurate merchant assignment, or a telecommunications company optimizing 5G rollouts and creating new services, there’s one essential commonality: location data.

ML 98

ML ML Data Silos Data Quality 98

Hacker News

SEPTEMBER 26, 2024

Writing this code is much harder, because you're not just telling a computer what to do, you're also grappling with another user's mental model of your code. Now it's equal part computer science and psychology of reasoning, or something. It's common to see documentation for dev tools structured like a computer program.

Python 180

Python Computer Science Computer Science Data Science 180

Dataconomy

MAY 2, 2023

What do machine learning engineers do: They implement and train machine learning models Data modeling One of the primary tasks in machine learning is to analyze unstructured data models, which requires a solid foundation in data modeling. How to become a machine learning engineer without a degree?

ML 110

ML ML Machine Learning Machine Learning 110

DrivenData Labs

MARCH 26, 2024

Ruan graduated from the Federal Institute of Rio Grande do Norte, and is studying hard to become a data scientist or engineer at a big tech company. This year he plans to apply to universities in the USA to study computer science or data science. Wesley is passionate about defending environmental causes.

Power BI 264

Power BI Data Scientist Python Machine Learning 264

Dataconomy

OCTOBER 17, 2023

In this blog post, we’ll examine what is data warehouse architecture and what exactly constitutes good data warehouse architecture as well as how you can implement one successfully without needing some kind of computer science degree!

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Data Warehouse Big Data Big Data ETL 103

Pickl AI

JULY 25, 2023

Skills and Tools of Data Engineers Data Engineering requires a unique set of skills, including: Database Management: SQL, NoSQL, NewSQL, etc. Data Warehousing: Amazon Redshift, Google BigQuery, etc. Data Modeling: Entity-Relationship (ER) diagrams, data normalization, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. js and Tableau Data science, data analytics and IBM Practicing data science isn’t without its challenges.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

AWS Machine Learning Blog

SEPTEMBER 5, 2023

Yingwei received his PhD in computer science from Texas A&M University. With over 8 years of experience building AI and machine learning models for industrial applications, he specializes in generative AI, computer vision, and time series modeling. candidate in computer science at UNC-Charlotte.

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AWS ML ML Machine Learning 108

Flipboard

NOVEMBER 21, 2024

When building generative AI applications using Amazon Bedrock, it’s often necessary to enhance the capabilities of the models by giving them access to external data or functionality. These tools act as bridges, allowing models to retrieve application-specific, real-time or dynamic information […]

AI 171

AI AI Data Models Data Modeling 171

Tableau

DECEMBER 1, 2021

Chris had earned an undergraduate computer science degree from Simon Fraser University and had worked as a database-oriented software engineer. April 2018), which focused on users who do understand joins and curating federated data sources. Relationships in Tableau 2020.2 (May Beginning in Tableau 2020.2,

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Tableau ML ML Database 145

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

ODSC - Open Data Science

FEBRUARY 14, 2023

The database would need to be highly available and resilient, with features like automatic failover and data replication to ensure that the system remains up and running even in the face of hardware or software failures. This could be achieved through the use of a NoSQL data model, such as document or key-value stores.

Database 124

Database AI AI Natural Language Processing 124

AWS Machine Learning Blog

NOVEMBER 22, 2023

With these focus areas, you can conduct an architecture review from different aspects to enhance the effectivity, observability, and scalability of the three components of an AI/ML project, data, model, or business goal. She has extensive experience in machine learning with a PhD degree in computer science.

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AWS ML ML Machine Learning 105

Pickl AI

MARCH 3, 2025

Ensuring data accuracy and consistency through cleansing and validation processes. Data Analysis and Modelling Applying statistical techniques and analytical tools to identify trends, patterns, and anomalies. Developing data models to support analysis and reporting.

Business Intelligence 52

Business Intelligence Business Intelligence Data Analyst Data Visualization 52

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. Open-source projects, academic institutions, startups and legacy tech companies all contributed to the development of foundation models.

AI 88

AI AI Data Warehouse ML 88

DrivenData Labs

MARCH 30, 2023

Steven Wu is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, with his primary appointment in the Software and Societal Systems Department, and affiliated appointments with the Machine Learning Department and the Human-Computer Interaction Institute.

Machine Learning 162

Machine Learning Machine Learning Data Science Algorithm 162

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.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

O'Reilly Media

OCTOBER 19, 2021

We need robust versioning for data, models, code, and preferably even the internal state of applications—think Git on steroids to answer inevitable questions: What changed? They are often built by data scientists who are not software engineers or computer science majors by training. Why did something break?

ML 145

ML ML Data Scientist AWS 145

AWS Machine Learning Blog

AUGUST 30, 2024

ChromaDB offers several notable features: Efficient vector storage – ChromaDB uses advanced indexing techniques to efficiently store and retrieve high-dimensional vector data, enabling fast similarity searches and nearest neighbor queries. He holds a Bachelor’s in Computer Science with a minor in Economics from Tufts University.

SQL 71

SQL AWS Database AI 71

AWS Machine Learning Blog

JUNE 5, 2023

Hyperparameter overview When training any machine learning (ML) model, you are generally dealing with three types of data: input data (also called the training data), model parameters, and hyperparameters. You use the input data to train your model, which in effect learns your model parameters.

Machine Learning 82

Machine Learning Machine Learning ML ML 82

Tableau

DECEMBER 1, 2021

Chris had earned an undergraduate computer science degree from Simon Fraser University and had worked as a database-oriented software engineer. April 2018), which focused on users who do understand joins and curating federated data sources. Relationships in Tableau 2020.2 (May Beginning in Tableau 2020.2,

Tableau 98

Tableau ML ML Database 98

AWS Machine Learning Blog

SEPTEMBER 12, 2024

Thomson Reuters knew they would need to run a series of experiments—training LLMs from 7B to more than 30B parameters, starting with an FM and continuous pre-training (using various techniques) with a mix of Thomson Reuters and general data. Ankit Anand is a Senior Foundation Models Go-To-Market (GTM) Specialist at AWS.

Clustering 85

Clustering AWS ML ML 85

AWS Machine Learning Blog

MAY 30, 2023

This kind of SFT trains the model to recognize patterns of behavior demonstrated by the humans in the demonstration training data. Model producers need to do this type of fine-tuning to show that their models are capable of performing such tasks for downstream adopters. He is an ACM Fellow and IEEE Fellow.

Machine Learning 71

Machine Learning Machine Learning AI AI 71

AWS Machine Learning Blog

SEPTEMBER 26, 2024

When extended to videos, this iterative process must be applied to multiple frames, multiplying the computational load. Increased parameter count – To handle the additional complexity of video data, models often require more parameters, leading to larger memory footprints and increased computational demands.

Clustering 107

Clustering Algorithm ML ML 107

ODSC - Open Data Science

APRIL 26, 2023

Because of this, they will be required to work closely with business stakeholders, data teams, and even other tech-focused members of an organization to sure that the needs of the organization are met and comply with overall business objectives.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

Snorkel AI

MARCH 1, 2023

Unsupervised learning requires no labels and identifies underlying patterns in the data. Model architectures that qualify as “supervised learning”—from traditional regression models to random forests to most neural networks—require labeled data for training. What are some examples of Foundation Models?

Natural Language Processing 83

Natural Language Processing Supervised Learning Machine Learning Machine Learning 83

Pickl AI

MAY 30, 2023

As Data Scientists, you will find immense job opportunities with advanced skills in SQL in the market. You will learn data modelling to indexes and SQL query functions. It also includes learning on security and privacy mechanisms, the advanced SQL certificate will allow you to enhance your expertise.

Data Science 52

Data Science Data Mining Data Mining Data Mining 52

Pickl AI

DECEMBER 4, 2023

Data Architect Designs and creates data systems and structures for optimal organisation and retrieval of information. 24,30000 Data modelling, system architecture, communication skills Pursue degrees in computer science or related fields, gain experience in database design, and stay updated on industry trends.

Data Analysis 52

Data Analysis Data Analysis Artificial Intelligence Artificial Intelligence 52

PyImageSearch

SEPTEMBER 23, 2024

This means that as you define your endpoints and data models, FastAPI automatically creates detailed, interactive documentation for your API, making it easier for developers and clients to understand and use your service. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

Deep Learning 60

Deep Learning Deep Learning Python Natural Language Processing 60

The MLOps Blog

JANUARY 23, 2023

Especially as those baseline models get so good. Ok, so we’ll have more need for ML engineers than data scientists, right? Let’s talk about computer science education. I believe that packaging/building/deploying the vanilla, run-of-the-mill ML model will become common knowledge for backend devs. Not so fast.

ML 59

ML ML Data Scientist Machine Learning 59

The MLOps Blog

OCTOBER 3, 2023

Mikiko Bazeley: Most people are really surprised to hear that my background in college was not computer science. I actually did not pick up Python until about a year before I made the transition to a data scientist role. How did you manage to jump from a more analytical, scientific type of role to a more engineering one?

ML 52

ML ML Data Scientist Machine Learning 52

Flipboard

JULY 31, 2023

Nowadays, with the advent of deep learning and convolutional neural networks, this process can be automated, allowing the model to learn the most relevant features directly from the data. Model Training: With the labeled data and identified features, the next step is to train a machine learning model.

Machine Learning 123

Machine Learning Machine Learning Deep Learning Deep Learning 123

AWS Machine Learning Blog

NOVEMBER 22, 2024

Although QLoRA reduces computational requirements and memory footprint, FSDP, a data/model parallelism technique, will help shard the model across all eight GPUs (one ml.p4d.24xlarge 24xlarge ), enabling training the model even more efficiently.

Clustering 72

Clustering AWS ML ML 72