Towards AI

AUGUST 25, 2023

Machine learning (ML) is a subset of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed. In ML, there are a variety of algorithms that can help solve problems. Any competent software engineer can implement any algorithm.

Machine Learning 98

Machine Learning Software Engineer Neural Network Data Science 98

Unite.AI

FEBRUARY 28, 2024

They utilize a transformer-based neural network architecture that excels at processing sequences like text and speech. Foster closer collaboration between security teams and ML engineers to instill security best practices. Develop clear policies governing appropriate use cases and disclosing limitations to users.

Large Language Models 157

Large Language Models Machine Learning LLM Neural Network 157

Marktechpost

OCTOBER 11, 2023

The team started with a collection of 15 ML engineering projects spanning various fields, with experiments that are quick and cheap to run. One challenge, for instance, is to boost the performance of a Convolution Neural Networks (CNN) model by more than 10% on the cifar10 dataset.

Machine Learning 103

Machine Learning AI Research AI Researcher Convolutional Neural Networks 103

ODSC - Open Data Science

JUNE 6, 2023

Using Embeddings to Detect Anomalies Figure 1: Using a trained deep neural network, it is possible to convert unstructured data to numeric representations, i.e., embeddings Embeddings are numerical representations generated from unstructured data like images, text, and audio, and greatly influence machine learning approaches for handling such data.

Machine Learning 90

Machine Learning ML Engineer Neural Network Data Science 90

Towards AI

FEBRUARY 20, 2025

But who exactly is an LLM developer, and how are they different from software developers and ML engineers? It begins by explaining vector transformations, a core idea in neural networks, and contrasts traditional methods like SVMs with learned feature mappings in Transformers.

Neural Network 79

Neural Network Explainability LLM Software Development 79

Pickl AI

NOVEMBER 28, 2024

Fundamental Programming Skills Strong programming skills are essential for success in ML. This section will highlight the critical programming languages and concepts ML engineers should master, including Python, R , and C++, and an understanding of data structures and algorithms. million by 2030, with a remarkable CAGR of 44.8%

Machine Learning 52

Machine Learning Neural Network ML Engineer Algorithm 52

Unite.AI

SEPTEMBER 3, 2024

Whether you're a seasoned ML engineer looking to optimize your workflows or a newcomer eager to harness the power of GPU computing, this guide will equip you with the knowledge to take your machine learning endeavors to the next level. Convolution Operations Convolutional Neural Networks (CNNs) rely heavily on convolution operations.

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Machine Learning Convolutional Neural Networks Neural Network Algorithm 130

ODSC - Open Data Science

MARCH 27, 2023

ML Governance: A Lean Approach Ryan Dawson | Principal Data Engineer | Thoughtworks Meissane Chami | Senior ML Engineer | Thoughtworks During this session, you’ll discuss the day-to-day realities of ML Governance. Some of the questions you’ll explore include How much documentation is appropriate?

Machine Learning 98

Machine Learning Data Science Deep Learning Data Ingestion 98

AWS Machine Learning Blog

MARCH 9, 2023

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. This has led to the emergence of a data-centric approach to ML and various techniques to improve model performance by focusing on data requirements.

ML Engineer 87

ML Engineer Data Scientist Convolutional Neural Networks ML 87

Pickl AI

JANUARY 23, 2023

but performs very well with neural networks. Keras supports a high-level neural network API written in Python. Deep Python integration makes it possible to easily create neural network layers in Python using well-known modules and packages. This framework can perform classification, regression, etc.,

Machine Learning 52

Machine Learning ML Neural Network Auto-classification 52

Viso.ai

FEBRUARY 27, 2024

Since its inception in 2015, the YOLO (You Only Look Once) object-detection algorithm has been closely followed by tech enthusiasts, data scientists, ML engineers, and more, gaining a massive following due to its open-source nature and community contributions. Creating the GELAN, a practical and effective neural network.

Neural Network 52

Neural Network Computer Vision Convolutional Neural Networks Python 52

O'Reilly Media

JANUARY 17, 2023

This mindset has followed me into my work in ML/AI. Because if companies use code to automate business rules, they use ML/AI to automate decisions. Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? But first, let’s talk about the typical ML workflow.

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Automation Robotics Data Scientist ML 145

Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Agentic AI DesignPatterns LLMs & RAG forAgents Agent Architectures &Chaining Evaluating AI Agent Performance Building with LangChain and LlamaIndex Real-World Applications of Autonomous Agents Who Should Attend: Data Scientists, Developers, AI Architects, and ML Engineers seeking to build cutting-edge autonomous systems.

Data Scientist 52

Data Scientist Machine Learning Large Language Models ML Engineer 52

AWS Machine Learning Blog

APRIL 12, 2023

The EC2 G5g instances powered by NVIDIA T4G Tensor Core GPUs and featuring AWS Graviton2 processors were a natural fit for some of the custom neural network inference code that had developed for client-side usage. To be able to iterate quickly, we needed a compute environment that was familiar to our data scientists and ML engineers.

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Neural Network Machine Learning ML ML Engineer 73

TheSequence

JANUARY 19, 2024

You probably don’t need ML engineers In the last two years, the technical sophistication needed to build with AI has dropped dramatically. ML engineers used to be crucial to AI projects because you needed to train custom models from scratch. At the same time, the capabilities of AI models have grown.

Prompt Engineer 59

Prompt Engineer Prompt Engineering Generative AI Large Language Models 59

Allen AI

NOVEMBER 8, 2023

Patrick Beukema is the Lead ML Engineer for Skylight Patrick Beukema is the Lead ML Engineer for Skylight. What put you on the path to your current role? After my son was born, I grew increasingly concerned about the state of the planet, both now and when he’s grown up. Real-life vessel monitoring with my son.

Computer Vision 52

Computer Vision ML Engineer ML Neural Network 52

AWS Machine Learning Blog

JUNE 9, 2023

By using Graph Neural Networks (GNNs), GuardDuty is able to enhance its capability to alert customers. However, developing, launching, and operating graph ML solutions takes months and requires graph ML expertise.

ML 98

ML Machine Learning BERT Neural Network 98

ODSC - Open Data Science

FEBRUARY 21, 2025

This hands-on workshop shows you how to combine premium LLMs with open-source tools to streamline labeling, generate embeddings, and train neural network classifiersreducing costs without sacrificing accuracy. Walk away with practical techniques to accelerate text classification and optimize your machine learning pipeline.

Data Scientist 52

Data Scientist Software Engineer LLM Neural Network 52

AWS Machine Learning Blog

SEPTEMBER 20, 2023

This approach is beneficial if you use AWS services for ML for its most comprehensive set of features, yet you need to run your model in another cloud provider in one of the situations we’ve discussed. When model training is complete, we use the Open Neural Network Exchange (ONNX) runtime library to export the PyTorch model as an ONNX model.

ML 117

ML Python Deep Learning Machine Learning 117

Dlabs.ai

OCTOBER 23, 2019

But times are changing — as are the dynamics of ML engineering. And thanks to TensorFlow.js, teams can now create and run ML models in static HTML documents without ever setting up a server or even database — enabling the following services, hosted entirely client-side. …and that includes Javascript.

Machine Learning 52

Machine Learning Convolutional Neural Networks Python ML 52

AWS Machine Learning Blog

OCTOBER 23, 2023

This post shows our joint approach to designing a job recommendation system, including feature engineering, deep learning model architecture design, hyperparameter optimization, and model evaluation that ensures the reliability and effectiveness of our solution for both job seekers and employers. The recommendation system has driven an 8.6%

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NLP Deep Learning Machine Learning Natural Language Processing 106

AWS Machine Learning Blog

SEPTEMBER 1, 2023

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. Security SMEs review the architecture based on business security policies and needs.

Generative AI 120

Generative AI Prompt Engineering Prompt Engineer ML 120

The MLOps Blog

JANUARY 26, 2024

We’ll see how this architecture applies to different classes of ML systems, discuss MLOps and testing aspects, and look at some example implementations. Understanding machine learning pipelines Machine learning (ML) pipelines are a key component of ML systems. But what is an ML pipeline?

Machine Learning 72

Machine Learning Metadata ML Python 72

Pickl AI

JANUARY 29, 2024

ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition. AI Engineer, Machine Learning Engineer, and Robotics Engineer are prominent roles in AI. ML Engineer, Data Scientist, and Research Scientist are typical roles in Machine Learning.

Data Science 52

Data Science ML Machine Learning Artificial Intelligence 52

The MLOps Blog

JUNE 27, 2023

Collaborative workflows : Dataset storage and versioning tools should support collaborative workflows, allowing multiple users to access and contribute to datasets simultaneously, ensuring efficient collaboration among ML engineers, data scientists, and other stakeholders.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Viso.ai

FEBRUARY 12, 2024

The complexity of machine learning models has exponentially increased from linear regression to multi-layered neural networks, CNNs , transformers , etc. While neural networks have revolutionized the prediction power, they are also black-box models. Why do we need Explainable AI (XAI)?

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Explainable AI Explainability Deep Learning Neural Network 52

AWS Machine Learning Blog

JUNE 8, 2023

LLMs are based on the Transformer architecture , a deep learning neural network introduced in June 2017 that can be trained on a massive corpus of unlabeled text. Ryan Gomes is a Data & ML Engineer with the AWS Professional Services Intelligence Practice. He leads the NYC machine learning and AI meetup.

Generative AI 83

Generative AI LLM Machine Learning Large Language Models 83

Mlearning.ai

FEBRUARY 9, 2023

During my MS, I got the opportunity to work on many types of data and ML projects, including web scraping to collect data, parsing big data, building unsupervised ML models, building supervised ML models, creating deep neural networks, working with text data using Natural Language Processing, and with speech data using audio processing techniques.

Data Scientist 52

Data Scientist Data Science ML Machine Learning 52

Mlearning.ai

APRIL 17, 2023

image by author Introduction Error analysis is a vital process in diagnosing errors made by an ML model during its training and testing steps. It enables data scientists or ML engineers to evaluate their models’ performance and identify areas for improvement.

Machine Learning 52

Machine Learning Categorization Data Analysis Neural Network 52

The MLOps Blog

MAY 10, 2023

Next, you will see how you can save an ML model in a database. A neural network design with numerous layers and a set of labeled data are used to train deep learning models. These models have two major components, Weights and Network architecture, that you need to save to restore them for future use.

Python 105

Python Metadata ML Machine Learning 105

Dlabs.ai

OCTOBER 21, 2024

A perfect blend of technical details and industry news Breaks down complex AI papers into digestible summaries Covers a wide range of topics, from machine learning algorithms to neural network architectures Provides insights into practical applications of cutting-edge AI research Unique design and tone make it fun to read 2.

Deep Learning 52

Deep Learning AI AI Machine Learning 52

Pickl AI

MAY 30, 2023

Moreover, you can easily opt for 6 month certification program that pays well in the field that will allow you to gain perfection in ML. Learn the techniques in Machine Learning Use different tools for applications of ML and NLP Salary of the ML Engineer in India ranges between 3 Lakhs to 20.8 Lakhs annually.

Data Mining 52

Data Mining Data Science Machine Learning Deep Learning 52

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. Since Data-IQ can be used with any ML model (including neural networks, gradient boosting etc.),

Data Scientist 64

Data Scientist ML Computer Vision AI 64

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. Since Data-IQ can be used with any ML model (including neural networks, gradient boosting etc.),

Data Scientist 59

Data Scientist ML Computer Vision AI 59

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. Since Data-IQ can be used with any ML model (including neural networks, gradient boosting etc.),

Data Scientist 52

Data Scientist NLP ML Computer Vision 52

Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” So does that mean feature selection is no longer necessary? If not, when should we consider using feature selection?”

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Large Language Models Metadata Machine Learning AI 52