Unite.AI

FEBRUARY 12, 2025

The software leverages machine learning algorithms to analyze historical sales, seasonality, and other variables, producing more accurate forecasts than manual spreadsheet methods. The AI/ML engine built into MachineMetrics analyzes this machine data to detect anomalies and patterns that might indicate emerging problems.

AI Tools 256

AI Tools Machine Learning Auto-complete AI 256

IBM Journey to AI blog

AUGUST 12, 2024

Whereas AIOps is a comprehensive discipline that includes a variety of analytics and AI initiatives that are aimed at optimizing IT operations, MLOps is specifically concerned with the operational aspects of ML models, promoting efficient deployment, monitoring and maintenance.

Big Data 266

Big Data DevOps Automation Machine Learning 266

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. These solutions can be time-consuming and may not be feasible for data professionals who wish to focus primarily on their areas of expertise.

Data Scientist 133

Data Scientist Machine Learning Python ML Engineer 133

ODSC - Open Data Science

FEBRUARY 24, 2023

Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst.

Data Analysis 126

Data Analysis Data Science Business Intelligence Python 126

Pickl AI

JANUARY 29, 2024

Summary: In the tech landscape of 2024, the distinctions between Data Science and Machine Learning are pivotal. Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. The collective strength of both forms the groundwork for AI and Data Science, propelling innovation.

Data Science 52

Data Science ML Machine Learning Artificial Intelligence 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. After all, this is what machine learning really is; a series of algorithms rooted in mathematics that can iterate some internal parameters based on data.

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. AI engineers usually work in an office environment as part of a team.

AI Engineer 111

AI Engineer Software Engineer Artificial Intelligence Artificial Intelligence 111

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. 12, 2014. [3]

Machine Learning 98

Machine Learning Software Engineer Neural Network Data Science 98

Unite.AI

JUNE 19, 2024

In world of Artificial Intelligence (AI) and Machine Learning (ML), a new professionals has emerged, bridging the gap between cutting-edge algorithms and real-world deployment. As businesses across industries increasingly embrace AI and ML to gain a competitive edge, the demand for MLOps Engineers has skyrocketed.

Software Engineer 176

Software Engineer DevOps Machine Learning ML 176

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.

Neural Network 98

Neural Network Software Engineer Algorithm Machine Learning 98

Towards AI

NOVEMBER 8, 2024

Data preparation isn’t just a part of the ML engineering process — it’s the heart of it. Photo by Myriam Jessier on Unsplash To set the stage, let’s examine the nuances between research-phase data and production-phase data. Data is a key differentiator in ML projects (more on this in my blog post below).

Data Ingestion 111

Data Ingestion Metadata ML Engineer ML 111

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 105

Data Scientist ML Data Science Machine Learning 105

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

Pickl AI

JANUARY 30, 2023

Since the rise of Data Science, it has found several applications across different industrial domains. However, the programming languages that work at the core of Data Science play a significant role in it. Hence for an individual who wants to excel as a data scientist, learning Python is a must.

Python 97

Python DevOps Machine Learning Data Science 97

AWS Machine Learning Blog

FEBRUARY 21, 2025

There are various techniques of preference alignment, including proximal policy optimization (PPO), direct preference optimization (DPO), odds ratio policy optimization (ORPO), group relative policy optimization (GRPO), and other algorithms, that can be used in this process. The following diagram compares predictive AI to generative AI.

LLM 77

LLM AI AI Data Scientist 77

Towards AI

JANUARY 6, 2025

However, you are expected to possess intermediate coding experience and a background as an AI ML engineer; to begin with the course. Build expertise in computer vision, clustering algorithms, deep learning essentials, multi-agent reinforcement, DQN, and more.

Artificial Intelligence 105

Artificial Intelligence Artificial Intelligence Computer Vision Deep Learning 105

ODSC - Open Data Science

APRIL 1, 2025

Adaptive RAG Systems with Knowledge Graphs: Building Smarter LLM Pipelines David vonThenen, Senior AI/ML Engineer at DigitalOcean Unlock the full potential of Retrieval-Augmented Generation by embedding adaptive reasoning with knowledge graphs. Youll explore the latest algorithms, training setups, and real-world applications.

Neural Network 52

Neural Network LLM Software Engineer AI 52

The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

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

Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. million by 2030, with a remarkable CAGR of 44.8%

Machine Learning 52

Machine Learning Neural Network ML Engineer Algorithm 52

AWS Machine Learning Blog

APRIL 26, 2023

The first is by using low-code or no-code ML services such as Amazon SageMaker Canvas , Amazon SageMaker Data Wrangler , Amazon SageMaker Autopilot , and Amazon SageMaker JumpStart to help data analysts prepare data, build models, and generate predictions. Conduct exploratory analysis and data preparation.

ML 105

ML Machine Learning Data Science Data Drift 105

Pickl AI

APRIL 5, 2023

This makes it easier for you to understand the algorithm and the different techniques used in Machine Learning. Moreover, you will also learn the use of clustering and dimensionality reduction algorithms. This course is useful for Data Scientists who are keen to expand their expertise in ML.

Machine Learning 52

Machine Learning ML Engineer ML Data Scientist 52

Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

Data Scientist 52

Data Scientist Machine Learning Big Data Algorithm 52

AWS Machine Learning Blog

NOVEMBER 14, 2024

Runs are executions of some piece of data science code and record metadata and generated artifacts. Model training You can continue experimenting with different feature engineering techniques in your JupyterLab environment and track your experiments in MLflow. An experiment collects multiple runs with the same objective.

ML 87

ML Machine Learning Auto-complete Auto-classification 87

Towards AI

FEBRUARY 11, 2025

🚀 MLflow Experiment Tracking: The Ultimate Beginners Guide to Streamlining ML Workflows Photo by Alvaro Reyes on Unsplash Introduction Have you ever felt that you were losing command over your machine-learning projects? The algorithm in question? Sound familiar? If you facing this problem, then you are not alone.

ML 67

ML Machine Learning Data Scientist ML Engineer 67

ODSC - Open Data Science

FEBRUARY 22, 2024

By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows. Using Graphs for Large Feature Engineering Pipelines Wes Madrigal | ML Engineer | Mad Consulting Feature engineering from raw entity-level data is complex, but there are ways to reduce that complexity.

Machine Learning 52

Machine Learning Data Science Python ML 52

AWS Machine Learning Blog

JANUARY 13, 2023

Machine learning (ML) presents an opportunity to address some of these concerns and is being adopted to advance data analytics and derive meaningful insights from diverse HCLS data for use cases like care delivery, clinical decision support, precision medicine, triage and diagnosis, and chronic care management.

Machine Learning 84

Machine Learning ML Algorithm Data Scientist 84

Heartbeat

FEBRUARY 1, 2023

Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. In this article, we’ll learn everything there is to know about these operations and how ML engineers go about performing them. Testing : This relates to data testing, model development, and facilities.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

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

The MLOps Blog

SEPTEMBER 7, 2023

This situation is not different in the ML world. Data Scientists and ML Engineers typically write lots and lots of code. is an experiment tracker for ML teams that struggle with debugging and reproducing experiments, sharing results, and messy model handover. Aside neptune.ai

Software Engineer 52

Software Engineer Machine Learning ETL Data Scientist 52

Mlearning.ai

MAY 23, 2023

Product First versus Model First mindset is a important concept as you mature in data science. 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. Complex Relationships and Patterns in Data.

Machine Learning 52

Machine Learning Data Scientist ML Data Science 52

The MLOps Blog

NOVEMBER 14, 2023

Visualizing deep learning models can help us with several different objectives: Interpretability and explainability: The performance of deep learning models is, at times, staggering, even for seasoned data scientists and ML engineers. Data scientists and ML engineers: Creating and training deep learning models is no easy feat.

Deep Learning 111

Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111

AWS Machine Learning Blog

FEBRUARY 29, 2024

Configuration files (YAML and JSON) allow ML practitioners to specify undifferentiated code for orchestrating training pipelines using declarative syntax. The following are the key benefits of this solution: Automation – The entire ML workflow, from data preprocessing to model registry, is orchestrated with no manual intervention.

Automation 109

Automation Python Machine Learning ML 109

AWS Machine Learning Blog

JUNE 9, 2023

Introducing GraphStorm Graph algorithms and graph ML are emerging as state-of-the-art solutions for many important business problems like predicting transaction risks, anticipating customer preferences, detecting intrusions, optimizing supply chains, social network analysis, and traffic prediction.

ML 98

ML Machine Learning BERT Neural Network 98

Dlabs.ai

OCTOBER 21, 2024

Their newsletter covers the latest papers, technological breakthroughs, and insights into machine learning algorithms with humor and adorable cat-themed design. The Rundown Frequency: Daily Best for: AI enthusiasts and students Content: Broad perspective on AI, ML, and data science The Rundown is daily, concise, and reader-friendly.

Deep Learning 52

Deep Learning AI AI Machine Learning 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. RL algorithms, such as Deep Q-Networks (DQNs) and AlphaGo, demonstrated significant accomplishments in game playing and control tasks.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

Snorkel AI

OCTOBER 12, 2023

Chief Data Scientist In this fireside chat as Snorkel AI CEO and co-founder Alex Ratner and DJ Patil, the Former U.S. Chief Data Scientist dive into data science’s history, impact, and challenges in the United States government.

Data Scientist 64

Data Scientist ML Computer Vision AI 64