Introduction
There are
6 technology tracks (specialties)
that cover highly demanded jobs.
Those tracks are:
There are
6 technology tracks (specialties)
that cover highly demanded jobs.
Those tracks are:
● Provide trainees
with the necessary
technical
background and experience
needed to excel in specific technologies that match trainee’s capabilities and aspirations. This includes all necessary prerequisites.
● Guide trainees on how to develop a professional and product quality module.
● Explain different aspects of product development life cycle (planning, design, development, integration, testing)
that help trainees succeed in job market .
● Involve trainees in
Hands on experience /
training that
deepen
their understanding of
the
introduced technologies.
An IBM data scientist can leverage a unique set of skills and tools to help businesses derive value from their data. A data scientist is capable of mining the data for drivers of past and future performance, opportunities for new revenue, methods to increase efficiency, ways to improve customer experience and a host of other critical business insights. All this is done mostly using IBM technologies.
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Microsoft Data Engineer learns how to use Microsoft tools for data management and processing. He/she should be experienced in SQL, Microsoft Azure platform, and DevOps roles.
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Microsoft machine learning engineers leverage Microsoft technology to translate the raw data gathered from various data pipelines into data science models that can be applied and scaled as needed. A machine learning engineer connects that structured data to the models defined by the data scientists they work with. In addition, machine learning engineers also develop algorithms and build programs that enable machines, computers, and robots to process incoming data and identify patterns.
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AWS Machine learning engineers use AWS technology to work with algorithms, data, and artificial intelligence. Their tasks involve researching, building, and designing the artificial intelligence responsible for machine learning and maintaining and improving existing artificial intelligence systems. Often, a machine learning engineer will also serve as a critical communicator between other data science team members, working directly with the data scientists who develop the models for building AI systems and the people who construct and run them.
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Learn the skills required to use generative AI in your professional life. Discover the fundamental ideas behind generative AI and artificial intelligence. Gain knowledge about generative AI models. Discover the moral implications of employing generative AI. Examine the effects of tools for generative AI.
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