IBM Journey to AI blog

SEPTEMBER 19, 2023

DevOps , SRE , Platform, I TOps, and developer teams are all under pressure to keep applications performant while operating faster and smarter than ever. Once the telemetry data is collected, Instana uses advanced analytics and machine learning algorithms to automatically detect and diagnose issues.

Automation 219

Automation DevOps Artificial Intelligence Artificial Intelligence 219

Becoming Human

MAY 11, 2023

Machine learning operations, or MLOps, are the set of practices and tools that aim to streamline and automate the machine learning lifecycle. It is a discipline that seeks to automate the various stages of the machine learning lifecycle, from data acquisition and cleaning to model training, deployment, and monitoring.

Machine Learning 78

Machine Learning DevOps Software Development Automation 78

Becoming Human

APRIL 19, 2024

Automated Testing: By automating the creation of test cases, generative AI can expedite the software development process’ testing phase. This automated testing method improves software products’ overall reliability and quality. Higher quality of software and more customer happiness are the results of AI incorporation.

Software Development 103

Software Development Generative AI DevOps Automation 103

IBM Journey to AI blog

OCTOBER 25, 2023

But simultaneously, generative AI has the power to transform the process of application modernization through code reverse engineering, code generation, code conversion from one language to another, defining modernization workflow and other automated processes. Much more can be said about IT operations as a foundation of modernization.

Generative AI 224

Generative AI Automation AI AI 224

Heartbeat

FEBRUARY 1, 2023

MLOps is a highly collaborative effort that aims to manipulate, automate, and generate knowledge through machine learning. They may also be involved in the deployment process’s automation. It can assist you in simplifying and automating the creation and operation of machine-learning models.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

IBM Journey to AI blog

AUGUST 16, 2023

Proper financial management requires FinOps—a combination of financial personnel and DevOps. Intelligent tools based on machine learning algorithms and other predictive technologies can assist in this regard. Embracing cloud optimization tools and automation can keep costs down, especially for periods of low resource usage.

Automation 172

Automation DevOps Algorithm Machine Learning 172

Mlearning.ai

JULY 31, 2023

Extension Of Devops MLOps is an extension of DevOps. DevOps aims to streamline the development and operation of software applications, while MLOps focuses on the machine learning lifecycle. MLOps extends DevOps by including data science practices, like model training and data preprocessing.

DevOps 52

DevOps Machine Learning Data Scientist Automation 52

AI News

AUGUST 29, 2023

The operationalisation of data projects has been a key factor in helping organisations turn a data deluge into a workable digital transformation strategy, and DataOps carries on from where DevOps started. And everybody agrees that in production, this should be automated.”

Data Ingestion 161

Data Ingestion Explainability Big Data ETL 161

Pickl AI

JANUARY 30, 2023

Data Automation: Automate data processing pipelines and workflows using Python scripting and libraries such as PyAutoGUI and Task Scheduler. Scripting: Use Python as a scripting language to automate and simplify tasks and processes. Python helps in this process. to build and implement Machine Learning models.

Python 97

Python DevOps Machine Learning Automation 97

Viso.ai

DECEMBER 20, 2023

Modern vision systems use algorithms based on machine learning, deep learning especially, that need to be trained on images annotated by humans (supervised learning). Therefore, the CVAT tool was designed to accelerate the process of annotating videos and images for use in training computer vision algorithms.

Computer Vision 98

Computer Vision Deep Learning Neural Network Automation 98

AWS Machine Learning Blog

MAY 16, 2024

Therefore, we decided to introduce a deep learning-based recommendation algorithm that can identify not only linear relationships in the data, but also more complex relationships. Recommendation model using NCF NCF is an algorithm based on a paper presented at the International World Wide Web Conference in 2017.

Deep Learning 86

Deep Learning Auto-complete Algorithm ML 86

DataRobot Blog

SEPTEMBER 1, 2022

You need full visibility and automation to rapidly correct your business course and to reflect on daily changes. Imagine yourself as a pilot operating aircraft through a thunderstorm; you have all the dashboards and automated systems that inform you about any risks. Autoscaling Deployments with MLOps. See DataRobot MLOps in Action.

Data Drift 145

Data Drift AI AI Data Science 145

Marktechpost

JULY 14, 2023

Generative AI is a common term for any type of process that uses an algorithm to generate, manipulate, and synthesize data. It blends the features of both DevOps and ML to help organizations design robust ML pipelines with minimal resources and maximum efficiency. What is Generative AI?

Generative AI 52

Generative AI AI Developer AI Development DevOps 52

ODSC - Open Data Science

JULY 24, 2023

This firm is a leader in AI and NLP-powered no-code solutions that help build AI co-workers that help “automate complex people- and process-centric processes across functions.” This push for what they call “AI co-workers” allow companies to automate complex business processes that would normally keep their human employees focused.

Data Science 52

Data Science Automation Chatbots AI 52

IBM Journey to AI blog

AUGUST 28, 2023

If you’re ready to expand—or even start—your automation and AIOps strategy, you’ve come to the right place. First, let’s start with a basic premise—as IT systems become more complex and intertwined, automation is the most essential tool you have at your disposal. Read the Enterprise Guide.

Automation 156

Automation DevOps Algorithm Artificial Intelligence 156

IBM Journey to AI blog

OCTOBER 13, 2023

Automatic and continuous discovery of application components One of Instana’s key advantages is its fully automated and continuous discovery of application components. By leveraging machine learning algorithms, Instana can identify patterns and trends in application behavior, anticipating issues before they manifest as problems.

DevOps 251

DevOps Automation Machine Learning Algorithm 251

Unite.AI

DECEMBER 19, 2023

During the training phase, we add “noise,” which changes the raw data from the files we feed into our algorithm, in order to automatically generate slight “mutations,” which are fed in each training cycle during our training phase. From there, we validate and classify data ourselves with algorithms we developed internally.

Neural Network 147

Neural Network Deep Learning ML Metadata 147

Marktechpost

JULY 4, 2023

Regex generation Regular expression generation is time-consuming for developers; however, Autoregex.xyz leverages GPT-3 to automate the process. Automated code reviews & code quality improvement Codiga provides automatic code reviews, and Mutable AI has industrialized Jupyter notebooks.

Large Language Models 100

Large Language Models BERT Natural Language Processing Categorization 100

Heartbeat

FEBRUARY 29, 2024

Furthermore, the software development process has evolved to embrace Agile methodologies, DevOps practices, and continuous integration/continuous delivery (CI/CD) pipelines. These tools have evolved to support the demands of modern software engineering, offering features like real-time collaboration, code analysis, and automated testing.

Software Engineer 52

Software Engineer Software Development LLM Large Language Models 52

Snorkel AI

JUNE 22, 2023

They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. Transparency is a critical ingredient in improving the quality and changing data through automated approaches. But the risks are real, and governance is critical to this process. So, how do you tackle this?

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. Transparency is a critical ingredient in improving the quality and changing data through automated approaches. But the risks are real, and governance is critical to this process. So, how do you tackle this?

Data Quality 52

Data Quality Automation Data Scientist ML 52

AWS Machine Learning Blog

NOVEMBER 9, 2023

We build a model to predict the severity (benign or malignant) of a mammographic mass lesion trained with the XGBoost algorithm using the publicly available UCI Mammography Mass dataset and deploy it using the MLOps framework. This will enable us to test the pattern to trigger automated retraining of the model. csv dataset.

Data Drift 99

Data Drift Auto-complete ML Automation 99

Unite.AI

OCTOBER 4, 2023

Hallucinations stem from the way statistics are used in the implementation of the algorithms. The second is optimizing the operations, particularly through the use of automation and FinOps. What are some of the challenges behind Generative AI hallucinations and how is Amdocs addressing this to reduce or mitigate these?

Generative AI 278

Generative AI DevOps Machine Learning Automation 278

Heartbeat

JULY 31, 2023

Comet provides a built-in hyperparameter optimization feature that automates the process of hyperparameter tuning. It uses state-of-the-art algorithms such as Bayesian optimization and grid search to automatically search through the hyperparameter space and find the best ones for the given machine learning problem.

LLM 52

LLM Machine Learning Deep Learning Data Scientist 52

ODSC - Open Data Science

JANUARY 31, 2023

Getting insight into your system allows you to detect and resolve issues quickly — and it’s an essential part of DevOps best practices. This is especially important for companies moving toward DevOps practices, where production environments have become more automated and continuous delivery pipelines are more complex.

DevOps 105

DevOps Automation Machine Learning Data Science 105

Mlearning.ai

DECEMBER 21, 2023

It defines a family of algorithms, encapsulates each one, and makes them interchangeable. Used in sorting algorithms where the sorting strategy can be switched dynamically. This makes sorting algorithms interchangeable and easily extendable. The team sets up an automated pipeline for continuous integration and deployment.

Software Engineer 52

Software Engineer Software Development DevOps Algorithm 52

IBM Journey to AI blog

SEPTEMBER 20, 2023

They use self-supervised learning algorithms to perform a variety of natural language processing (NLP) tasks in ways that are similar to how humans use language (see Figure 1). IBM watsonx.governance is an end-to-end automated AI lifecycle governance toolkit that is built to enable responsible, transparent and explainable AI workflows.

Large Language Models 225

Large Language Models DevOps AI Modeling Automation 225

Marktechpost

MAY 3, 2024

Trained on a large open-source code dataset, it suggests snippets to full functions, automating repetitive tasks and enhancing code quality. The ‘Notion AI is an AI system that may be used to automate a wide variety of writing tasks, from blogs and lists to brainstorming sessions and creative writing.

Artificial Intelligence 111

Artificial Intelligence Artificial Intelligence AI Tools Auto-complete 111

Viso.ai

MARCH 29, 2024

MLflow can be seen as a tool that fits within the MLOps (synonymous with DevOps) framework. Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments. ML Tasks – source Automating these ML lifecycle steps is extremely difficult.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

Explosion

AUGUST 3, 2017

Most of those insights have been used to make spaCy better: AI DevOps was hard, so we made sure models could be installed via pip. — Andrew Ng (@AndrewYNg) July 7, 2017 Unsupervised algorithms return meaning representations, based on the internal structure of the data.

Convolutional Neural Networks 52

Convolutional Neural Networks Python Machine Learning Algorithm 52

AWS Machine Learning Blog

MAY 8, 2024

Concurrently, the ensemble model strategically combines the strengths of various algorithms. By conducting experiments within these automated pipelines, significant cost savings could be achieved. million subscribers, which amounts to 57% of the Sri Lankan mobile market. It also helps maintain an experiment version tracking system.

ML 93

ML AI AI Categorization 93

Marktechpost

DECEMBER 4, 2023

Trained on a large open-source code dataset, it suggests snippets to full functions, automating repetitive tasks and enhancing code quality. The ‘Notion AI is an AI system that may be used to automate a wide variety of writing tasks, from blogs and lists to brainstorming sessions and creative writing.

AI Tools 125

AI Tools Auto-complete Artificial Intelligence Artificial Intelligence 125

Marktechpost

JULY 17, 2023

It relates to employing algorithms to find and examine data patterns to forecast future events. Algorithms and models Predictive analytics uses several methods from fields like machine learning, data mining, statistics, analysis, and modeling. Machine learning and deep learning models are two major categories of predictive algorithms.

Machine Learning 91

Machine Learning Data Mining Data Scientist Artificial Intelligence 91

AWS Machine Learning Blog

AUGUST 15, 2023

Content moderation automation allows Amazon to scale the process while keeping high accuracy. Rekognition Content Moderation automates and streamlines image and video moderation workflows without requiring ML experience. Combined with the DevOps efficiency gains, the Amazon Shopping team achieved significant cost savings.

DevOps 76

DevOps Computer Vision Automation ML 76

Marktechpost

JULY 17, 2023

Synthetic data is generated algorithmically and used to train machine learning models, validate mathematical models, and act as a stand-in for test production or operational data test datasets. Data scientists can now enhance datasets using cutting-edge synthetic data generation and automated data quality profiling.

Machine Learning 52

Machine Learning Data Scientist Computer Vision Deep Learning 52

DataRobot Blog

MARCH 16, 2023

That focus often leads to over-rotatation on building a better algorithm or neural-network or finding more data to improve model performance as opposed to the improvement of business performance. This narrow focus can lead to accurate and true insights that are not really useful, leaving business stakeholders feeling frustrated.

AI 98

AI AI DevOps Neural Network 98

The MLOps Blog

JUNE 27, 2023

For example, if your team works on recommender systems or natural language processing applications, you may want an MLOps tool that has built-in algorithms or templates for these use cases. This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134