IBM Journey to AI blog

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning.

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

Marktechpost

JUNE 25, 2024

Google Gemini is a generative AI-powered collaborator from Google Cloud designed to enhance various tasks such as code explanation, infrastructure management, data analysis, and application development. It includes videos and hands-on labs to improve data analysis and machine learning workflows.

DevOps 107

DevOps Data Analysis Large Language Models Generative AI 107

AWS Machine Learning Blog

DECEMBER 3, 2024

Complexity in data interpretation – Team members may struggle to interpret monitoring and observability data due to complex applications with numerous services and cloud infrastructure entities, and unclear symptom-problem relationships.

DevOps 97

DevOps AI AI ML 97

Pickl AI

JANUARY 30, 2023

Scientific Computing: Use Python for scientific computing tasks, such as data analysis and visualization, Machine Learning, and numerical simulations. An Artificial Intelligence/Machine Learning (AI/ML) Engineer uses Python For: Data Pre-processing : Before coding and creating an algorithm, it is important to clean and filter the data.

Python 97

Python DevOps Machine Learning Data Science 97

IBM Journey to AI blog

FEBRUARY 14, 2024

Financial services AI-powered FinOps (Finance + DevOps) helps financial institutions operationalize data-driven cloud spend decisions to safely balance cost and performance in order to minimize alert fatigue and wasted budget. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions.

Computer Vision 247

Computer Vision NLP Robotics Automation 247

Marktechpost

JULY 5, 2024

Programming for Data Science with Python This course series teaches essential programming skills for data analysis, including SQL fundamentals for querying databases and Unix shell basics. Students also learn Python programming, from fundamentals to data manipulation with NumPy and Pandas, along with version control using Git.

Data Science 60

Data Science Machine Learning Data Analysis Software Engineer 60

Pickl AI

JUNE 29, 2023

Data Analysis is significant as it helps accurately assess data that drive data-driven decisions. Different tools are available in the market that help in the process of analysis. It is a powerful and widely-used platform that revolutionises how organisations analyse and derive insights from their data.

Big Data 52

Big Data DevOps Data Analysis Machine Learning 52

Marktechpost

JULY 17, 2023

It is a clear leader in all types of analytics tools and methodologies, including predictive analytics, and has continued to invent new tools used by statisticians and data scientists. government launched the first version of the company’s tools to better data analysis for healthcare in 1966.

Machine Learning 98

Machine Learning Data Mining Data Scientist Data Science 98

AWS Machine Learning Blog

OCTOBER 21, 2024

He has rich experience in cloud computing, data analysis, and machine learning and is currently dedicated to research and practice in the fields of data science, machine learning, and serverless. Tian Shi is Senior Solution Architect at Amazon Web Services.

Machine Learning 78

Machine Learning AI AI LLM 78

AWS Machine Learning Blog

MAY 16, 2024

MLOps component 4: CI/CD structure A CI/CD structure is a fundamental part of DevOps, and is also an important part of organizing an MLOps environment. We load tested it with Locust using five g4dn.2xlarge 2xlarge instances and found that it could be reliably served in an environment with 1,000 TPS.

Deep Learning 111

Deep Learning Auto-complete Algorithm ML 111

ODSC - Open Data Science

JULY 19, 2023

Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow. A typical workflow is illustrated here from data ingestion, EDA (Exploratory Data Analysis), experimentation, model development and evaluation, to the registration of a candidate model for production.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

AWS Machine Learning Blog

MARCH 21, 2023

Therefore, organizations have adopted technology best practices, including microservice architecture, MLOps, DevOps, and more, to improve delivery time, reduce defects, and increase employee productivity. This post introduces a best practice for managing custom code within your Amazon SageMaker Data Wrangler workflow.

Categorization 69

Categorization Python Automation Machine Learning 69

Marktechpost

JULY 17, 2023

The advantages of using synthetic data include easing restrictions when using private or controlled data, adjusting the data requirements to specific circumstances that cannot be met with accurate data, and producing datasets for DevOps teams to use for software testing and quality assurance.

Machine Learning 52

Machine Learning Data Scientist Computer Vision Deep Learning 52

AWS Machine Learning Blog

FEBRUARY 7, 2025

In this post, we assign the functions in terms of the ML lifecycle to each role as follows: Lead data scientist Provision accounts for ML development teams, govern access to the accounts and resources, and promote standardized model development and approval process to eliminate repeated engineering effort.

ML 62

ML Data Scientist ML Engineer Data Science 62

Heartbeat

FEBRUARY 29, 2024

This linguistic diversity has enabled programmers to tackle various tasks, from web development and data analysis to system-level programming. Furthermore, the software development process has evolved to embrace Agile methodologies, DevOps practices, and continuous integration/continuous delivery (CI/CD) pipelines.

Software Engineer 52

Software Engineer Software Development Large Language Models LLM 52

The MLOps Blog

MAY 9, 2023

Model deployment You can deploy the packaged and registered model to a staging environment (as traditional software with DevOps) or the production environment. ReSpo.Vision uses ML in sports data analysis to extract 3D data from single-view camera sports broadcast videos. They run a lot of kedro pipelines in the process.

ML 98

ML Machine Learning Metadata Data Science 98

IBM Journey to AI blog

JUNE 5, 2024

However, SaaS architectures can easily overwhelm DevOps teams with data aggregation, sorting and analysis tasks. It provides automated, democratized observability with AI, making it accessible to anyone across DevOps , SRE, platform engineering, ITOps and development.

DevOps 236

DevOps Automation ML Machine Learning 236

IBM Journey to AI blog

AUGUST 15, 2023

Recent AI developments are also helping businesses automate and optimize HR recruiting and professional development, DevOps and cloud management, and biotech research and manufacturing. Building an effective hybrid multicloud model is essential for AI to manage the massive amounts of data that must be stored, processed and analyzed.

Artificial Intelligence 234

Artificial Intelligence Artificial Intelligence Natural Language Processing AI 234

Mlearning.ai

JUNE 16, 2023

Game changer ChatGPT in Software Engineering: A Glimpse Into the Future | HackerNoon Generative AI for DevOps: A Practical View - DZone ChatGPT for DevOps: Best Practices, Use Cases, and Warnings. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API.

Software Development 52

Software Development Generative AI ChatGPT Prompt Engineer 52

Pickl AI

MAY 15, 2024

Understanding the Challenges of Scaling Data Science Projects Successfully transitioning from Data Analyst to Data Science architect requires a deep understanding of the complexities that emerge when scaling projects. But as data volume and complexity increase, traditional infrastructure struggles to keep up.

Data Scientist 52

Data Scientist Data Science Machine Learning Data Quality 52

Marktechpost

OCTOBER 27, 2024

It’s widely used in data science, machine learning, artificial intelligence, and web development. Python’s extensive libraries, like NumPy, Pandas, and TensorFlow, make it a powerful tool for data analysis and scientific computing. Strengths: Powerful query language for data retrieval and manipulation.

Data Analysis 133

Data Analysis Machine Learning Big Data Natural Language Processing 133

Unite.AI

NOVEMBER 21, 2024

Archana Joshi brings over 24 years of experience in the IT services industry, with expertise in AI (including generative AI), Agile and DevOps methodologies, and green software initiatives. In healthcare, we’re seeing GenAI make a big impact by automating things like medical diagnostics, data analysis and administrative work.

DevOps 144

DevOps Automation Responsible AI Software Development 144

Flipboard

JANUARY 7, 2025

The main focus of the work so far has been data analysis and AI, and looking at ways to implement these across the business. Elias has experience in migration delivery, DevOps engineering and cloud infrastructure. Magnus Schoeman is a Principal Customer Solutions Manager at AWS.

IDP 138

IDP Machine Learning Generative AI LLM 138

ODSC - Open Data Science

MARCH 27, 2025

While its core solver is commercial, it supports multiple open-source projects, including Python libraries that help data scientists and operations researchers implement optimization solutions. ProspectiveReal-Time Streaming Analytics Prospective is an innovative open-source platform for real-time data analysis and visualization.

Data Scientist 52

Data Scientist DevOps Data Science Machine Learning 52

ODSC - Open Data Science

MARCH 21, 2025

While its core solver is commercial, it supports multiple open-source projects, including Python libraries that help data scientists and operations researchers implement optimization solutions. ProspectiveReal-Time Streaming Analytics Prospective is an innovative open-source platform for real-time data analysis and visualization.

Data Scientist 52

Data Scientist DevOps Data Science Machine Learning 52

Pickl AI

DECEMBER 25, 2024

Learning Curve for Teams and Organisations Adopting cloud-native principles demands new skills in containerisation, orchestration, and DevOps practices. Leverage DevOps Practices Adopt DevOps to streamline the development, deployment, and management of applications. Teams often struggle to adapt quickly.

DevOps 52

DevOps Automation ML Software Development 52