Heartbeat

SEPTEMBER 18, 2023

Unleashing Innovation and Success: Comet — The Trusted ML Platform for Enterprise Environments Machine learning (ML) is a rapidly developing field, and businesses are increasingly depending on ML platforms to fuel innovation, improve efficiency, and mine data for insights.

ML 52

ML Data Scientist ML Engineer Machine Learning 52

Marktechpost

JANUARY 31, 2024

Data scientists and ML engineers often need help to build full-stack applications. These professionals typically have a firm grasp of data and AI algorithms. Still, they may need more skills or time to learn new languages or frameworks to create user-friendly web applications.

Data Scientist 128

Data Scientist Machine Learning Python ML Engineer 128

IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

Data Science 144

Data Science DevOps Machine Learning Data Scientist 144

Towards AI

JANUARY 30, 2024

ML Engineers(LLM), Tech Enthusiasts, VCs, etc. Anybody previously acquainted with ML terms should be able to follow along. The algorithm works by optimizing the reward policy and then fine-tuning the unsupervised LLM according to reward maximization of such human-reinforced preference feedback. How advanced is this post?

Explainability 97

Explainability LLM ML Engineer ML 97

Pickl AI

APRIL 19, 2023

Machine Learning is the part of Artificial Intelligence and computer science that emphasizes on the use of data and algorithms, imitating the way humans learn and improving accuracy. Different industries from education, healthcare to marketing, retail and ecommerce require Machine Learning Engineers. Consequently.

Machine Learning 52

Machine Learning ML Engineer Data Scientist ML 52

ODSC - Open Data Science

SEPTEMBER 21, 2023

Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. Some typical examples are given in the following table, along with some discussion as to whether or not ML would be an appropriate tool for solving the problem: Figure 1.1:

Machine Learning 98

Machine Learning ML Engineer ML Data Science 98

Towards AI

FEBRUARY 15, 2024

AI Engineers: Your Definitive Career Roadmap Become a professional certified AI engineer by enrolling in the best AI ML Engineer certifications that help you earn skills to get the highest-paying job. Skills Needed as an AI Engineer As an AI engineer, you require a blend of soft and hard skills.

AI Engineer 94

AI Engineer Software Engineer Artificial Intelligence Artificial Intelligence 94

AWS Machine Learning Blog

NOVEMBER 21, 2023

By taking care of the undifferentiated heavy lifting, SageMaker allows you to focus on working on your machine learning (ML) models, and not worry about things such as infrastructure. Prior to working at Amazon Music, Siddharth was working at companies like Meta, Walmart Labs, Rakuten on E-Commerce centric ML Problems.

ML 82

ML Deep Learning Machine Learning DevOps 82

Towards AI

AUGUST 24, 2023

Common mistakes and misconceptions about learning AI/ML Markus Spiske on Unsplash A common misconception of beginners is that they can learn AI/ML from a few tutorials that implement the latest algorithms, so I thought I would share some notes and advice on learning AI. Trying to code ML algorithms from scratch.

Software Engineer 92

Software Engineer Neural Network Algorithm AI Engineer 92

Towards AI

AUGUST 25, 2023

As a reminder, I highly recommend that you refer to more than one resource (other than documentation) when learning ML, preferably a textbook geared toward your learning level (beginner/intermediate / advanced). In a nutshell, AI Engineering is the application of software engineering best practices to the field of AI.

Software Engineer 96

Software Engineer Machine Learning Neural Network Artificial Intelligence 96

Pickl AI

APRIL 5, 2023

ML forms the underlying platform for several new developments. Hence, it has also triggered the demand for ML experts. However, if you are new to the tech domain and want to learn Machine Learning for free, then in this blog, we will take you through the 3 best options to start your ML learning journey. Lakhs to ₹ 28.4

Machine Learning 52

Machine Learning ML Engineer ML Data Scientist 52

Marktechpost

JANUARY 10, 2024

Rather than relying on groundbreaking algorithmic changes, the emphasis is on continual learning and iterative improvements. It also automates feature engineering, a task traditionally handled solely by ML engineers, saving time and reducing the risk of errors. Check out the Reference Article.

Machine Learning 80

Machine Learning Continuous Learning Automation ML Engineer 80

Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks.

Machine Learning 94

Machine Learning BERT Deep Learning ML Engineer 94

Unite.AI

NOVEMBER 10, 2023

It is key to remember that AI is an algorithm, which analyzes and adjusts to the data we provide. Leverage the solutions that already exist Many companies aim to, right away, design their own machine learning algorithms. The basic scenario for data collection is as follows: Understand what data we might need to implement AI.

AI 182

AI AI ML Chatbots 182

ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. They’re looking for people who know all related skills, and have studied computer science and software engineering.

Data Science 98

Data Science Data Scientist Big Data Machine Learning 98

AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

Data Scientist 89

Data Scientist ML Data Science Automation 89

Heartbeat

OCTOBER 23, 2023

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms.

Data Scientist 52

Data Scientist Machine Learning Big Data Algorithm 52

AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

Data Science 110

Data Science Data Drift DevOps Auto-complete 110

ODSC - Open Data Science

MARCH 27, 2023

Learn about the flow, difficulties, and tools for performing ML clustering at scale Ori Nakar | Principal Engineer, Threat Research | Imperva Given that there are billions of daily botnet attacks from millions of different IPs, the most difficult challenge of botnet detection is choosing the most relevant data.

Machine Learning 98

Machine Learning Data Ingestion Explainability ML 98

TheSequence

NOVEMBER 12, 2023

Edge 344: We discuss another of hte great papers of the year in which Google shows that the combination of LLMs and memory is enough to simulate any algorithm. 📌 ML Engineering Event: Join Meta, PepsiCo, RiotGames, Uber & more at apply(ops) apply(ops) is in two days! We also review the Chatbot Arena framework.

OpenAI 64

OpenAI Generative AI ML Prompt Engineer 64

ODSC - Open Data Science

FEBRUARY 22, 2024

Key points of this talk are: In this talk, we will focus on: The dangers of using post-hoc explainability methods as tools for decision making, and how traditional ML isn’t suited in situations where want to perform interventions on the system. technical architecture that simplifies the process of managing real-time ML models and features.

Machine Learning 52

Machine Learning Data Science Python ML 52

Mlearning.ai

MAY 23, 2023

Model mindset prioritizes the ML model that you are building. When you are iterating, its better to adopt a model first mindset to improve a well performing ML model. This isn’t limited to just the business users, but also the individual partners along the way: the data analysts, data scientists, engineers, and ML engineers.

Machine Learning 52

Machine Learning Data Scientist ML Data Science 52

Towards AI

MARCH 28, 2024

As an AI/ML engineer, I was inspired by the striking resemblance between this concept and how ML algorithms tackle zero-shot learning (ZSL) methods. Recently, I read David Epstein’s ‘Range’ and stumbled upon Duncker’s Radiation Problem in Chapter 5, where the author talks about analogical problem-solving.

ML Engineer 73

ML Engineer ChatGPT Machine Learning ML 73

Towards AI

APRIL 27, 2023

Nothing in the world motivates a team of ML engineers and scientists to spend the required amount of time in data annotation and labeling. We asked ourselves, what if we leverage the zero-shot capability of LLMs, or large foundation models and our own proprietary algorithms fine-tuning auto labeling layer?

Automation 87

Automation Auto-complete Computer Vision ML Engineer 87

Viso.ai

JANUARY 30, 2024

It enables enterprises to create and implement computer vision solutions , featuring built-in ML tools for data collection, annotation, and model training. In overfitting , there are too few examples for the ML model to work on. For variance, it often helps to add data points that will make trends clearer to the ML.

Data Drift 52

Data Drift Machine Learning Computer Vision Algorithm 52

Heartbeat

FEBRUARY 1, 2023

Machine learning has become an essential part of our lives because we interact with various applications of ML models, whether consciously or unconsciously. Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. What is MLOps?

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

Heartbeat

OCTOBER 30, 2023

Ideally, the responsibilities of the ML engineering team should be completed once the model is deployed. Model Drift and Data Drift are two of the main reasons why the ML model's performance degrades over time. But, as a data scientist or an ML engineer, you focus on the solutions rather than problems, right?

Machine Learning 52

Machine Learning Data Drift ML Data Scientist 52

Pickl AI

JULY 14, 2023

Evolution of AI The evolution of Artificial Intelligence (AI) spans several decades and has witnessed significant advancements in theory, algorithms, and applications. Deep Learning, a subfield of ML, gained attention with the development of deep neural networks. Artificial Intelligence and the Future of Humans 1.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

Pickl AI

JANUARY 30, 2023

It’s a universal programming language that finds application in different technologies like AI, ML, Big Data and others. 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 Automation 97

Flipboard

MARCH 13, 2023

With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) solutions to surface critical attacks and spotlight emerging threats from noise. Solution overview The following diagram illustrates the ML platform architecture.

Data Scientist 102

Data Scientist Auto-complete ML Machine Learning 102

AWS Machine Learning Blog

FEBRUARY 12, 2024

The Ranking team at Booking.com plays a pivotal role in ensuring that the search and recommendation algorithms are optimized to deliver the best results for their users. Essential ML capabilities such as hyperparameter tuning and model explainability were lacking on premises.

ML 110

ML Explainability Machine Learning Deep Learning 110

AssemblyAI

DECEMBER 15, 2022

To use Photostudio, a user clicks a picture using their phone and then multiple AI Image algorithms are used to generate stunning product images while preserving all details of the original product. Shubham Saboo (Head of DevRel at Tenstorrent Inc.

Software Engineer 130

Software Engineer ML Engineer Data Scientist AI Chatbots 130

AWS Machine Learning Blog

MARCH 22, 2023

Generating this data can take months to gather and require large teams of labelers to prepare it for use in machine learning (ML). Behind the scenes, Rekognition Custom Labels automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics.

Automation 80

Automation Machine Learning ML Computer Vision 80

AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

ML 88

ML Machine Learning Data Science Data Drift 88

AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

Data Drift 99

Data Drift Auto-complete ML Automation 99

The MLOps Blog

MARCH 14, 2023

When working on real-world ML projects , you come face-to-face with a series of obstacles. The ml model reproducibility problem is one of them. This is indeed an erroneous thing to do when working on ML projects at scale. Reproducibility is the key factor when it comes to stabilizing the outcomes of any ML pipeline.

ML 52

ML Machine Learning Metadata ML Engineer 52

AWS Machine Learning Blog

AUGUST 1, 2023

Artificial intelligence (AI) and machine learning (ML) models have shown great promise in addressing these challenges. Amazon SageMaker , a fully managed ML service, provides an ideal platform for hosting and implementing various AI/ML-based summarization models and approaches. No ML engineering experience required.

ML 74

ML Large Language Models NLP Natural Language Processing 74