TechCrunch

NOVEMBER 1, 2021

OctoML , a Seattle-based startup that helps enterprises optimize and deploy their machine learning models, today announced that it has raised an $85 million Series C round led by Tiger Global Management. ” OctoML raises $28M Series B for its machine learning acceleration platform.

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DevOps.com

APRIL 21, 2023

With the rapid advancement of artificial intelligence (AI) and machine learning (ML), companies need to understand and evaluate their readiness to adopt these technologies and drive material business outcomes.

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CIO

NOVEMBER 7, 2023

These global events have also underscored the crucial significance of safeguarding our critical infrastructure against domestic or foreign attacks. It’s easy to envision the outcomes that could arise from a major assault on our vital infrastructure.

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CIO

JULY 10, 2023

For a decade, Edmunds, an online resource for automotive inventory and information, has been struggling to consolidate its data infrastructure. Now, with the infrastructure side of its data house in order, the California-based company is envisioning a bold new future with AI and machine learning (ML) at its core.

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CIO

AUGUST 2, 2022

Fueled by enterprise demand for data analytics , machine learning , data center consolidation and cloud-native app developmen t, spending on cloud infrastructure services jumped 33% year on year to $62.3 Cloud providers build out infrastructure. billion in the second quarter, according to Canalys. billion out of $62.3

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TechCrunch

OCTOBER 11, 2022

Foxglove , a startup building an infrastructure stack for robotics, today announced that it raised $15 million in a Series A funding round led by Eclipse with participation from Amplify Partners and angel investors including Cruise CEO Kyle Vogt. The proceeds bring Foxglove’s total raised to $18.7

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Netflix Tech

NOVEMBER 11, 2022

By Vi Iyengar , Keila Fong , Hossein Taghavi , Andy Yao , Kelli Griggs , Boris Chen , Cristina Segalin , Apurva Kansara , Grace Tang , Billur Engin , Amir Ziai , James Ray , Jonathan Solorzano-Hamilton Welcome to the first post in our multi-part series on how Netflix is developing and using machine learning (ML) to help creators make better media?—?from

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TechCrunch

DECEMBER 6, 2022

Unfortunately for execs, at the same time recruiting is posing a major challenge, IT infrastructure is becoming more costly to maintain. “We’re differentiated from others in that we automate and manage the full stack [of infrastructure], including switches, servers, storage and networking as well as cloud enablement.”

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O'Reilly Media - Data

APRIL 3, 2018

The growing role of data and machine learning cuts across domains and industries. Companies continue to use data to improve decision-making (business intelligence and analytics) and for automation (machine learning and AI). Data Science and Machine Learning sessions will cover tools, techniques, and case studies.

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TechCrunch

AUGUST 17, 2021

Less than a year after its $3 million seed round, San Francisco- and Africa-based fintech Pngme has snapped up another $15 million for its financial data infrastructure play. The company is also describing itself as a machine learning-as-a-service platform.

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Exadel

APRIL 13, 2021

Python is irreplaceable for Machine Learning, but running Python in production can be a problem if other parts of the system are written using C#. ML.NET is a Machine Learning library for C# that helps deliver Machine Learning features in a.NET environment more quickly. That is where ML.NET can help.

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Hacker Earth Developers Blog

MAY 22, 2020

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. Again, focus on Data Science and Machine Learning.

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O'Reilly Media - Data

JULY 17, 2018

Although machine learning (ML) can produce fantastic results, using it in practice is complex. However, even these internal platforms are limited: typical ML platforms only support a small set of algorithms or libraries with limited customization (whatever the engineering team builds), and are tied to each company’s infrastructure.

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TechCrunch

DECEMBER 12, 2022

Nillion is a deep technology infrastructure project,” Andrew Yeoh, the company’s founding chief marketing officer, told TechCrunch. The startup aims to provide a new internet infrastructure for securing storage and data computation. We’re building infrastructure that is inevitable.

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Openxcell

MARCH 21, 2023

MACHINE LEARNING- the most hyped technology these days due to its ability to automate tasks, detect patterns and learn from the data. In this blog, you will find out the importance of Machine Learning and how it is changing the environment around us. What is Machine Learning?

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DevOps.com

JULY 14, 2021

Successful infrastructure monitoring enables IT teams to ensure constant uptime and performance of their company’s systems. Infrastructure changes are occurring faster than ever […]. The post Using ML/AI to Support Infrastructure Monitoring appeared first on DevOps.com.

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Altexsoft

JULY 23, 2020

When speaking of machine learning, we typically discuss data preparation or model building. In other words, they dedicate a quarter of their efforts to infrastructure — instead of doing what they can do best. I/CD ) practices for deploying and updating machine learning pipelines. Better user experience.

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Cloudera

APRIL 9, 2021

Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. Predictive Analytics – AI & machine learning. So let’s introduce Cloudera Machine Learning (CML) and discuss how it addresses the aforementioned silo issues.

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O'Reilly Media - Ideas

JUNE 8, 2021

That means that COBOL code is infrastructure we rely on, like roads and bridges. While Fortran was used to create infrastructure, software written in Fortran isn’t itself infrastructure. For the past 15 years or so, we’ve been using the slogan “infrastructure as code.”

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Altexsoft

JUNE 29, 2021

In a world fueled by disruptive technologies, no wonder businesses heavily rely on machine learning. Google, in turn, uses the Google Neural Machine Translation (GNMT) system, powered by ML, reducing error rates by up to 60 percent. The role of a machine learning engineer in the data science team.

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Confluent

FEBRUARY 6, 2019

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Therefore, the majority of machine learning/deep learning frameworks focus on Python APIs.

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DevOps.com

NOVEMBER 7, 2019

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). The post Five Challenges of Machine Learning DevOps appeared first on DevOps.com. We have identified […].

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CIO

NOVEMBER 7, 2022

CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects. Are they ready to transform business processes with machine learning capabilities, or will they slow down investments at the first speed bump?

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O'Reilly Media - Data

JUNE 5, 2018

Today, just 15% of enterprises are using machine learning, but double that number already have it on their roadmaps for the upcoming year. However, in talking with CEOs looking to implement machine learning in their organizations, there seems to be a common problem in moving machine learning from science to production.

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d2iq

FEBRUARY 19, 2021

Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail , and for the few models that are ever deployed, it takes many months to do so. As little as 5% of the code of production machine learning systems is the model itself. Adapted from Sculley et al.

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Cloudera

NOVEMBER 19, 2021

We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. Continuous Operations for Production Machine Learning (COPML) helps companies think about the entire life cycle of an ML model.

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O'Reilly Media - Data

JULY 23, 2018

Interest in machine learning (ML) has been growing steadily , and many companies and organizations are aware of the potential impact these tools and technologies can have on their underlying operations and processes. Machine Learning in the enterprise". Scalable Machine Learning for Data Cleaning.

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CIO

MAY 10, 2024

Sastry Durvasula, chief information and client services officer at TIAA, says the multilayered platform’s extensive use of machine learning as part of its customer service line partnership with Google AI makes JSOC a formidable tool for financial and retirement planning and guiding customers through complex financial journeys.

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CIO

MARCH 6, 2024

This connectivity maximizes efficiency, keeps critical infrastructure running, and gives the business new information and insights. Security is paramount for the core infrastructure that supports manufacturing and industrial operations. The third benefit is the reduction of downtime.

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CIO

JUNE 10, 2024

Moreover, many need deeper AI-related skills, too, such as for building machine learning models to serve niche business requirements. It’s less about the machine learning skill set and more about how you adapt all your roles to take advantage of AI.” Here’s how IT leaders are coping.

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Cloudera

OCTOBER 25, 2021

As the Cloudera Data Platform (CDP) gains popularity and more and more customers make it a critical piece of their infrastructure, we set out to create the best data platform in the enterprise. Get started with Cloudera Machine Learning in CDP Now, you can start here. And in the end, we can make better decisions. .

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Tenable

FEBRUARY 8, 2021

Faced with an expanding attack surface and limited resources, security teams can apply machine learning to prioritize business risks and help predict what attackers will do next. Machine learning helps security teams work smarter. Containers, multi-cloud and connected devices are common in many IT infrastructures. .

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TechCrunch

MARCH 21, 2022

We’ve seen companies across the e-commerce infrastructure and enablement ecosystem pick up larger and larger rounds, and CommerceIQ is the latest to secure late-stage financing. The fire is indeed burning across the e-commerce infrastructure ecosystem, with companies announcing new funding practically daily.

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CIO

MAY 8, 2024

Of late, innovative data integration tools are revolutionising how organisations approach data management, unlocking new opportunities for growth, efficiency, and strategic decision-making by leveraging technical advancements in Artificial Intelligence, Machine Learning, and Natural Language Processing. billion by 2025.

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Dzone - DevOps

DECEMBER 6, 2021

Today, artificial intelligence-powered machine learning (ML) and data analytics solutions are high in demand by companies in almost every area, whether it’s the financial sector, power industry, retail, healthcare, technology, or telecommunications. to leverage ML technologies and solutions in the business infrastructure.

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CIO

JANUARY 24, 2024

Classical machine learning: Patterns, predictions, and decisions Classical machine learning is the proven backbone of pattern recognition, business intelligence, and rules-based decision-making; it produces explainable results. What’s the right infrastructure for AI? Learn more. [1]

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