Is Machine Learning Really AI?

CTOvision

There’s so much being said about machine learning (ML), but perhaps the train has left the station with whether many ML projects are truly AI: One of the downsides to […]. Artificial Intelligence Featured News AI artificial intelligence machine learning ML

Is Machine Learning Really AI? (Pt. II)

CTOvision

Continuing a previous article, Ronald Schmelzer explores in a Forbes article whether or not many machine learning projects are truly AI: If AI is to be a useful term to […].

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Continuous Delivery for Machine Learning

Martin Fowler

Machine Learning

DevOps.com

The post Machine Learning appeared first on DevOps.com. Blogs ROELBOB

Machine Learning for Builders: Tools, Trends, and Truths

Speaker: Rob De Feo, Startup Advocate at Amazon Web Services

Machine learning techniques are being applied to every industry, leveraging an increasing amount of data and ever faster compute. But that doesn’t mean machine learning techniques are a perfect fit for every situation (yet). So how can a startup harness machine learning for its own set of unique problems and solutions, and does it require a warehouse filled with PhDs to pull it off?

There’s No Such Thing As The Machine Learning Platform

CTOvision

Vendors are racing to create the dominant machine learning platform. But the concept of the machine learning platform doesn’t really exist. Artificial Intelligence Big Data and Analytics CTO Featured News AI artificial intelligence machine learning ML platform vendor

Federated Learning, Machine Learning, Decentralized Data

Cloudera

Two years ago we wrote a research report about Federated Learning. You can read it online here: Federated Learning. Federated Learning is a paradigm in which machine learning models are trained on decentralized data. Federated Learning is no panacea.

The future of software testing: Machine learning to the rescue

TechBeacon Testing

App Dev & Testing, Testing, Test Automation, Predictive Analytics, Quality Assurance (QA), Shift-left, Artificial Intelligence (AI), Machine LearningThe last decade has seen a relentless push to deliver software faster.

Review: DataRobot aces automated machine learning

CTOvision

Read Martin Heller take a look at how DataRobot’s end-to-end AutoML suite fares amongst automated machine learning platforms on Information Weekly: Data science is nothing if not tedious, in ordinary […].

Cylance brings AI and machine learning to anti-virus protection

CTOvision

Read Boing Boing’s review of Cylance’s new anti-virus protection powered by artificial intelligence and machine learning: Malware is everywhere. 350,000 new pieces of malware are discovered every day, which breaks […]. News Cylance

How Banks Are Winning with AI and Automated Machine Learning

Banks have always relied on predictions to make their decisions. Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. But times are changing. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

Apache TVM: Portable Machine Learning Across Backends

The New Stack

The Apache Software Foundation ’s newest top-level project, TVM, aims to bridge the gap between the creation of machine learning models and launching them into production. In this case, we [use] machine learning-guided search to search possible candidates to find a good solution.

20 Machine Learning/Artificial Intelligence Influencers To Follow In 2020

Hacker Earth Developers Blog

Machine Learning (ML) is emerging as one of the hottest fields today. The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% Consequently, there has been a significant increase in the number of Machine Learning enthusiasts across the globe. A Deep Learning leader, Adam Coates is currently the Director of Apple’s Special Projects Group. Andrew NG is one of the most sought-after leaders in the Machine Learning arena.

20 Machine Learning/Artificial Intelligence Influencers To Follow In 2020

Hacker Earth Developers Blog

Machine Learning (ML) is emerging as one of the hottest fields today. The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% Consequently, there has been a significant increase in the number of Machine Learning enthusiasts across the globe. A Deep Learning leader, Adam Coates is currently the Director of Apple’s Special Projects Group. Andrew NG is one of the most sought-after leaders in the Machine Learning arena.

The Role of Machine Learning in DevOps

Kovair - DevOps

Artificial intelligence (AI) and machine learning (ML) are advanced technologies in the IT world. DevOps Technologies DevOps Consultants DevOps Implementation Machine Learning Why DevOps gained popularityIt is a perfect combination and helps in both the private.

How Banks Are Winning with AI and Automated Machine Learning

Banks have always relied on predictions to make their decisions. Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. But times are changing. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

Five Challenges of Machine Learning DevOps

DevOps.com

As organizations add machine learning (ML) to their workflows, it’s tempting to try to squeeze model creation and deployment into the existing software development lifecycle (SDLC). However, ML is fundamentally different than traditional applications, and it’s important to account for that in a new, unique process called the machine learning development lifecycle. The post Five Challenges of Machine Learning DevOps appeared first on DevOps.com.

Prerequisites For Machine Learning

The Crazy Programmer

Machine Learning has rightly become one of the most popular technologies around and according to Artificial Intelligence (AI) researchers, every single thing ranging from our food, to our jobs, to the software we write will be affected by it. And if you are a beginner looking to build a career in this field, it’s necessary that you understand the prerequisites for Machine Learning. Prerequisites For Machine Learning. Machine Learning

AWS Brings Machine Learning to Code Optimization

DevOps.com

Amazon Web Services (AWS) has made generally available a tool dubbed Amazon CodeGuru that employs machine learning algorithms to recommend ways to improve code quality and identify which lines of code are the most expensive to run on its cloud service. The post AWS Brings Machine Learning to Code Optimization appeared first on DevOps.com. Blogs DevOps in the Cloud DevOps Practice AWS code optimization coding debugging machine learning

From Machine Learning to Machine Reasoning

CTOvision

The conversation around Artificial Intelligence usually revolves around technology-focused topics: machine learning, conversational interfaces, autonomous agents, and other aspects of data science, math, and implementation. Artificial Intelligence CTO News Cognilytica common sense deep learning knowledge graph machine learning machine reasoning ontology

Intelligent Process Automation: Boosting Bots with AI and Machine Learning

Across all sectors, companies are learning that they can transform their businesses by embracing Intelligent Process Automation, or IPA. With the pairing of AI and RPA, IPA adds a new layer of intelligent decision-making processes to automated RPA tasks. By automating repetitive work, and adding the ability to automate intelligent decision making, intelligent automation frees up your most valuable resources – your employees – to spend more time on higher value and more strategic work. But in order to reap the rewards of Intelligent Process Automation, organizations must first educate themselves and prepare for the adoption of IPA. In our ebook, Intelligent Process Automation: Boosting Bots with AI and Machine Learning.

Amazon Web Services Brings Machine Learning to DataOps

The New Stack

Well, Amazon Web Services is hoping to bring more machine learning to the worldwide development community. Machine learning is a very iterative process. The company’s Redshift data warehouse has been outfitted with machine learning capabilities.

When machine learning matters

Erik Bernhardsson

I joined Spotify in 2008 to focus on machine learning and music recommendations. In the majority of all products, machine learning will not be a key differentiator in the first five years. Most machine learning is sprinkles on the top. I lead the tech team at a startup and we are nowhere near using any kind of sophisticated machine learning, two years into the process. Rarely is machine learning the fundamental enabler of a product.

Introducing Lightweight, Customizable ML Runtimes in Cloudera Machine Learning

Cloudera

The truth is that one size does not fit all, and managing all aspects of enterprise machine learning requires a lightweight, versatile, and customizable approach to effectively enable data science across your business. New Machine Learning Runtimes For The Win.

Transactional Machine Learning at Scale with MAADS-VIPER and Apache Kafka

Confluent

This blog post shows how transactional machine learning (TML) integrates data streams with automated machine learning (AutoML), using Apache Kafka® as the data backbone, to create a frictionless machine learning […].

Humility in AI: Building Trustworthy and Ethical AI Systems

AI is becoming ubiquitous. More and more critical decisions are automated through machine learning models, determining the future of a business or making life-altering decisions for real people. The number of critical touch points is growing exponentially with the adoption of AI. In this ebook, we explore the concept of humility in AI systems and how it can be applied to existing solutions to ensure their trustworthiness, ethicality, and reliability in a fast-changing world.

Reserved.ai Applies Machine Learning to Rein in Cloud Costs

DevOps.com

The company says its Purchase Planner tool uses machine learning to optimize cloud computing workloads by automatically purchasing cloud reserve instances based on the forecasts it generates. Purchase Planner leverages the machine learning algorithms […].

Article: How to Get Hired as a Machine Learning Engineer

InfoQ Culture Methods

To become a machine learning engineer, you have to interview. interviewing An introduction to Machine Learning Machine Learning Culture & Methods AI, ML & Data Engineering articleYou have to gain relevant skills from books, courses, conferences, and projects.

Operationalizing machine learning

O'Reilly Media - Data

Dinesh Nirmal explains how real-world machine learning reveals assumptions embedded in business processes that cause expensive misunderstandings. Continue reading Operationalizing machine learning

Testing machine learning interpretability techniques

O'Reilly Media - Data

Interpreting machine learning models is a pretty hot topic in data science circles right now. Machine learning models need to be interpretable to enable wider adoption of advanced predictive modeling techniques, to prevent socially discriminatory predictions, to protect against malicious hacking of decisioning systems, and simply because machine learning models affect our work and our lives.

5 Things a Data Scientist Can Do to Stay Current

DataRobot together with Snowflake – a leading cloud data platform provider — is helping data scientists stay current with the latest technology and data science best practices so that they can excel in an increasingly AI-driven workplace. Five Things a Data Scientist Can Do to Stay Current offers data scientists guidance for thriving in AI-driven enterprises.

Foiling fraud with machine learning

CTOvision

Read Caroline Hermon explain how machine learning can be used to foil cyber frauds on Tech Radar: Digital transformation has for years seemed like nothing but a buzzword. More recently, […].

Machine learning for personalization

O'Reilly Media - Ideas

Continue reading Machine learning for personalization Tony Jebara explains how Netflix is personalizing and optimizing the images shown to subscribers.

Microsoft: Machine Learning Models Can Be Easily Reverse Engineered

The New Stack

Aronchick himself heads up the development of KubeFlow , a machine learning operations platform built for Kubernetes. Honeycomb provides Observability for all software engineering teams to learn, debug, and improve production systems to delight end-users and eliminate toil.

Measuring Fairness in Machine Learning Models

Dataiku

The next step in our fairness journey is to dig into how to detect biased machine learning models. In our previous article , we gave an in-depth review on how to explain biases in data. Tech Blog

Data Science Fails: Building AI You Can Trust

The new DataRobot whitepaper, Data Science Fails: Building AI You Can Trust, outlines eight important lessons that organizations must understand to follow best data science practices and ensure that AI is being implemented successfully.