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Your 2023 Data strategy in four resolutions

Capgemini

Following a volatile year in the market, you can get ahead of your 2023 plans and see where your organization can improve processes, bring on new tools, and set goals that make sense for your team. Here are four resolutions to make your data strategy pay off this year. Reassess your data architecture.

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AI Adoption in the Enterprise 2021

O'Reilly Media - Ideas

During the first weeks of February, we asked recipients of our Data & AI Newsletter to participate in a survey on AI adoption in the enterprise. We were also interested in the practice of AI: how developers work, what techniques and tools they use, what their concerns are, and what development practices are in place.

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MLOps: Methods and Tools of DevOps for Machine Learning

Altexsoft

Living in the shadow, this stage, according to the recent study , eats up 25 percent of data scientists time. The same survey shows that putting a model from a research environment to production — where it eventually starts adding business value — takes between 8 to 90 days on average. More time for development of new models.

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The Good and the Bad of Apache Airflow Pipeline Orchestration

Altexsoft

You can hardly compare data engineering toil with something as easy as breathing or as fast as the wind. The platform went live in 2015 at Airbnb, the biggest home-sharing and vacation rental site, as an orchestrator for increasingly complex data pipelines. How data engineering works. What is Apache Airflow?

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Technology Trends for 2023

O'Reilly Media - Ideas

Now developers are using AI to write software. Content about software development was the most widely used (31% of all usage in 2022), which includes software architecture and programming languages. Practices like the use of code repositories and continuous testing are still spreading to both new developers and older IT departments.

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Technology Trends for 2022

O'Reilly Media - Ideas

AI is making that transition now; we can see it in our data. What developments represent new ways of thinking, and what do those ways of thinking mean? What are the bigger changes shaping the future of software development and software architecture? What does that mean, and how is it affecting software developers?

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