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All about Machine Learning

Hacker Earth Developers Blog

In our third episode of Breaking 404 , we caught up with Srivatsan Ramanujam, Director of Software Engineering: Machine Learning, Salesforce to discuss everything about Machine Learning and the best practices for ML engineers to excel in their careers. At the time I was a software engineer.

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Machine Learning In Internet Of Things (IoT) – The next big IT revolution in the making

Openxcell

From human genome mapping to Big Data Analytics, Artificial Intelligence (AI),Machine Learning, Blockchain, Mobile digital Platforms (Digital Streets, towns and villages),Social Networks and Business, Virtual reality and so much more. What is Machine Learning? Machine Learning delivers on this need.

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Socket lands $4.6M to audit and catch malicious open source code

TechCrunch

Securing the software supply chain is admittedly somewhat of a dry topic, but knowing which components and code go into your everyday devices and appliances is a critical part of the software development process that billions of people rely on every day. Tainted software updates have led to the mass compromise of U.S.

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MLOps: why and how to build end-to-end product teams

Xebia

Machine Learning Operations (MLOps) climbed in popularity over the past few years with the promise to apply DevOps to Machine Learning. It strives to streamline the arduous process of creating robust, reliable and scalable machine learning systems that are ready to face end-users. Let’s dive in.

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Lessons learned turning machine learning models into real products and services

O'Reilly Media - Data

Why model development does not equal software development. Today, just 15% of enterprises are using machine learning, but double that number already have it on their roadmaps for the upcoming year. So what should an organization keep in mind before implementing a machine learning solution?

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Data engineers vs. data scientists

O'Reilly Media - Data

The two positions are not interchangeable—and misperceptions of their roles can hurt teams and compromise productivity. It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data.

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Harmonic helps investors query the startup searches of their wildest dreams

TechCrunch

Harmonic’s aggregation differentiation, per Ruderman, is the intelligence it uses to help recognize which public data is more accurate for certain fields, and then merges those sources to develop the “most accurate, fresh representation at any point in time.”. Floodgate, another customer, was Harmonic’s first investor.