IBM Journey to AI blog

SEPTEMBER 19, 2023

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. Having the right data strategy and data architecture is especially important for an organization that plans to use automation and AI for its data analytics.

Data Science 247

Data Science Data Scientist Machine Learning Data Mining 247

Unite.AI

JUNE 24, 2024

Deep learning is great for some applications — large language models are brilliant for summarizing documents, for example — but sometimes a simple regression model is more appropriate and easier to explain. Explainable AI is a hugely important and fascinating area of development.

Large Language Models 130

Large Language Models Data Scientist Machine Learning Software Engineer 130

Flipboard

SEPTEMBER 25, 2023

Talc Photo) Co-founders: Matt Lee and Max Kerr Explain what your startup does in two sentences: We provide end-to-end evaluation of large language model apps. We borrow proven techniques from the latest in NLP (natural language processing) academia to build evaluation tooling that any software engineer can use. Talc AI Talc.ai

Large Language Models 169

Large Language Models Explainability Natural Language Processing Software Engineer 169

AWS Machine Learning Blog

NOVEMBER 13, 2024

Unified model governance architecture ML governance enforces the ethical, legal, and efficient use of ML systems by addressing concerns like bias, transparency, explainability, and accountability. Evaluation details like model quality, bias pre-training, bias post-training, and explainability can be reviewed on the Evaluate tab.

Metadata 105

Metadata ML Software Engineer Machine Learning 105

Flipboard

JUNE 13, 2023

Prompt: “A robot helping a software engineer develop code.” ” Generative AI is already changing the way software engineers do their jobs. We have begun scaling out its use across a broader range of engineers and expect greater value as we use it more.” Made with Microsoft Bing Image Creator.

Generative AI 181

Generative AI Auto-complete Software Engineer AI 181

AWS Machine Learning Blog

DECEMBER 19, 2023

Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations. With the rapid growth in ML systems and in the context of ML engineering, MLOps provides capabilities that are needed to handle the unique complexities of the practical application of ML systems.

Machine Learning 129

Machine Learning ML Engineer DevOps ML 129

IBM Journey to AI blog

AUGUST 11, 2023

MLOps, which stands for machine learning operations, uses automation, continuous integration and continuous delivery/deployment (CI/CD) , and machine learning models to streamline the deployment, monitoring and maintenance of the overall machine learning system. How to use ML to automate the refining process into a cyclical ML process.

Data Science 188

Data Science DevOps Machine Learning Data Scientist 188

Unite.AI

SEPTEMBER 3, 2024

Can you explain the motivation behind creating the “Conversation Practice” feature? In fact, according to Gartner, 55% of software engineering leaders say building leadership skills in senior developers is a top challenge. Why do you believe it's crucial for today's workforce?

Prompt Engineer 147

Prompt Engineer Prompt Engineering Software Engineer AI Modeling 147

Unite.AI

APRIL 22, 2024

His interests fall in multiple areas, such as Artificial Intelligence, Software Engineering, and High Performance Computing. This allows the engineering teams to focus on the business logic, while our AI-Service handles the complexities of dealing with different AI providers. He has a PhD.

Software Engineer 147

Software Engineer AI Modeling Generative AI Large Language Models 147

Flipboard

AUGUST 1, 2023

I used to be a software engineer, so I’m a little rusty on coding. ” Boaz Ashkenazy, founder and CEO of Simply Augmented , a Seattle startup that helps brands leverage generative AI and software automation. And it’s a little bit easier to explain.” “I just founded this company.

Generative AI 123

Generative AI Software Engineer AI AI 123

Marktechpost

JULY 23, 2023

Most data scientists believe that deploying models is a software engineering mission and should be handled by software engineers, as all the skills required are more closely aligned with their day-to-day work. Using tools like Dataflow makes it possible to work closely with engineering teams.

Machine Learning 52

Machine Learning Software Engineer Data Scientist ML 52

Unite.AI

OCTOBER 17, 2024

A published scholar in the fields of artificial life, agent-oriented software engineering and distributed artificial intelligence, Babak has 31 granted or pending patents to his name. Trust, explainability and multi-objective decisions are among the important areas we’re pursuing that are vital for Fortune 500 enterprises.

AI 130

AI AI Artificial Intelligence Artificial Intelligence 130

Heartbeat

FEBRUARY 1, 2023

MLOps is a highly collaborative effort that aims to manipulate, automate, and generate knowledge through machine learning. The machine learning engineers are in charge of taking the models developed by data scientists and deploying them into production. They may also be involved in the deployment process’s automation.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

ODSC - Open Data Science

JUNE 3, 2023

With an AI coding assistant, syntax becomes easier to remember as these programs can help you correct your mistakes, but most importantly, explain why a certain script that is coming up wrong is wrong. Software Engineers This wouldn’t be a proper blog if we didn’t touch the elephant in the room -software engineers.

Data Scientist 52

Data Scientist Software Engineer Data Science AI 52

ODSC - Open Data Science

SEPTEMBER 19, 2023

Prompt engineers take on this challenge by optimizing LLMs to process and generate content at scale. This task involves a combination of software engineering expertise and computational efficiency. Engineers delve into the architecture of LLMs, identifying potential bottlenecks and areas for improvement.

Prompt Engineer 98

Prompt Engineer Prompt Engineering Large Language Models Software Engineer 98

Unite.AI

FEBRUARY 21, 2024

Could you share your personal story of how you became involved with software engineering, and worked your way up to being CTO of Astronomer? For me, engineering has always been a great creative outlet: I can come up with an idea and use whatever technology’s necessary to build towards a vision.

LLM 147

LLM Data Platform Software Engineer Chatbots 147

ODSC - Open Data Science

JULY 7, 2023

Most of the discussions have focused on the implications for writers, designers, software engineers, researchers, lawyers, and administrative workers. This involves building relationships, explaining concepts, and clear communication with others — particularly non-data specialists. But the use of AI could go further.

Explainability 59

Explainability AI AI Data Science 59

The MLOps Blog

JANUARY 23, 2023

Not a fork: – The MLOps team should consist of a DevOps engineer, a backend software engineer, a data scientist, + regular software folks. I don’t see what special role ML and MLOps engineers would play here. – We need both automated continuous monitoring AND periodic manual inspection. Ok, let me explain.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

O'Reilly Media

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. Software Architecture. The new category is often called MLOps.

DevOps 140

DevOps Software Engineer Data Scientist ML 140

AWS Machine Learning Blog

AUGUST 20, 2024

Amazon SageMaker Ground Truth significantly reduces the cost and time required for labeling data by integrating human annotators with machine learning to automate the labeling process. Michael Borde is a lead software engineer at Amazon AI, where he has been for seven years.

Software Engineer 113

Software Engineer ML Machine Learning Metadata 113

Robot Writers AI

DECEMBER 17, 2023

“For instance, the video’s YouTube description explains that ‘for the purposes of this demo, latency has been reduced and Gemini outputs have been shortened for brevity.’ Observes Blake: “Google modified interactions with Gemini in numerous ways in order to create the demonstration.

ChatGPT 100

ChatGPT AI AI Robotics 100

IBM Journey to AI blog

JULY 6, 2023

A major online media company uses data science to develop personalized content, enhance marketing through targeted ads and continuously update music streams, among other automation decisions. The company made its data open-source, and trains and empowers employees to take advantage of data-driven insights.

Data Science 200

Data Science Machine Learning Big Data Data Mining 200

ODSC - Open Data Science

DECEMBER 19, 2023

They need to be able to explain complex technical concepts to non-technical stakeholders and to identify and solve problems that arise during the development and implementation of AI models. Machine Learning Engineer Machine learning engineers are responsible for developing and deploying machine learning models.

AI Product Manager 96

AI Product Manager AI Product Management Prompt Engineer Prompt Engineering 96

DataRobot Blog

MAY 31, 2022

For example, a job description for a software engineer in the private sector was only one paragraph long, while the government job description was seven pages long. DataRobot automatically provides explainable insights such as prediction explanations to help leaders understand “why” a specific prediction is made.

AI 59

AI AI Software Engineer Explainability 59

Pickl AI

SEPTEMBER 18, 2024

Their work environments are typically collaborative, involving teamwork with Data Scientists, software engineers, and product managers. Team Collaboration Machine Learning projects often involve cross-functional teams, including Data Scientists, software engineers, and business analysts.

Machine Learning 52

Machine Learning Natural Language Processing Software Engineer Algorithm 52

AWS Machine Learning Blog

DECEMBER 14, 2023

The flexible and extensible interface of SageMaker Studio allows you to effortlessly configure and arrange ML workflows, and you can use the AI-powered inline coding companion to quickly author, debug, explain, and test code. Majisha Namath Parambath is a Senior Software Engineer at Amazon SageMaker.

Generative AI 125

Generative AI AI Tools ML Auto-complete 125

AWS Machine Learning Blog

SEPTEMBER 14, 2023

The following section explains how the workflow can be used in different industries and verticals. Specific use case: Automate issue resolution for employees based on corporate guidelines. Specific use case: Use customer financial history and previous loan applications to decide and explain loan decision.

Generative AI 122

Generative AI LLM Large Language Models Software Engineer 122

AWS Machine Learning Blog

FEBRUARY 6, 2023

We end by explaining how contact centers can keep AI models up to date using Talkdesk AI Trainer. Solution overview The solution consists of the following key components: Amazon Lex – Amazon Lex combines with Amazon Polly to automate customer service interactions by adding conversational AI capabilities to your contact center.

Conversational AI 79

Conversational AI Chatbots Automation Natural Language Processing 79

Pickl AI

AUGUST 1, 2024

Collaboratio n: Working with data scientists, software engineers, and other stakeholders to integrate Deep Learning solutions into existing systems. Operational Efficiency Deep Learning can optimise healthcare operations by automating administrative tasks, predicting patient flow, and optimising resource allocation.

Deep Learning 52

Deep Learning Neural Network Machine Learning Natural Language Processing 52

AWS Machine Learning Blog

OCTOBER 19, 2023

Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. Amazon SageMaker provides purpose-built tools for ML teams to automate and standardize processes across the ML lifecycle. Neelam Koshiya is an Enterprise Solutions Architect at AWS.

Machine Learning 115

Machine Learning Python ML Automation 115

AWS Machine Learning Blog

NOVEMBER 14, 2024

Transparency and explainability : Making sure that AI systems are transparent, explainable, and accountable. However, explaining why that decision was made requires next-level detailed reports from each affected model component of that AI system. Mitigation strategies : Implementing measures to minimize or eliminate risks.

ML 92

ML Auto-complete Machine Learning Auto-classification 92

deepsense.ai

OCTOBER 14, 2024

Web Agents are no longer just a concept from science fiction—they’re the cutting-edge tools that are automating and streamlining our online interactions at an unprecedented scale. Want to automate a tedious online task? The future of web interaction is here, and it’s more exciting—and challenging—than ever before.

LLM 52

LLM Automation Large Language Models Explainability 52

Chatbots Life

MAY 13, 2023

Ask you interview questions for a specific job I'm interviewing for a software engineer position, can you give me some interview questions Please provide me with some common interview questions for a marketing manager role Give you gift ideas for business partners, customers, or clients I need gift ideas for my clients, can you help?

ChatGPT 95

ChatGPT Chatbots Explainability Software Engineer 95

ODSC - Open Data Science

JANUARY 25, 2023

To make sure that machine learning solutions are fair and the value of their predictions easy to understand and explain, it is essential to get access to tools that developers and data scientists can use to improve accuracy and asses their models’ fairness and mitigate any observed unfairness issues.

Machine Learning 59

Machine Learning Data Scientist Python ML 59

The MLOps Blog

JUNE 27, 2023

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines.

Metadata 134

Metadata Machine Learning Data Scientist Data Quality 134

Towards AI

JANUARY 27, 2023

I started my journey as a software engineer around technologies such as web stack including python, javascript, and java stack. Later we went to automate this to complete Business Intelligence and Reporting tool having aggregated and detailed charts. But I didn’t about data science in a way on how it is known.

Data Analysis 73

Data Analysis Data Science Software Engineer Business Intelligence 73

Artificial Corner

AUGUST 3, 2023

Examples: *Create me a plan for a vacation roadtrip for me and my family* *Explain the relativity theory to me* *Summarize the following text…* *Implement me a snake game in Python* *Tell me what is the sense of life* What is prompt engineering? Previously, we had copy/paste engineers navigating the seas of Google or StackOverflow.

Prompt Engineer 83

Prompt Engineer Prompt Engineering Large Language Models Explainability 83