Data Engineering Machine Learning Training Video 
CIO
SEPTEMBER 29, 2022
And we recognized as a company that we needed to start thinking about how we leverage advancements in technology and tremendous amounts of data across our ecosystem, and tie it with machine learning technology and other things advancing the field of analytics. Watch the full video below for more insights.
CIO
MAY 30, 2024
The course covers principles of generative AI, data acquisition and preprocessing, neural network architectures, natural language processing, image and video generation, audio synthesis, and creative AI applications. Upon completing the learning modules, you will need to pass a chartered exam to earn the CGAI designation.
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CIO
SEPTEMBER 29, 2023
Most relevant roles for making use of NLP include data scientist , machine learning engineer, software engineer, data analyst , and software developer. PyTorch is known in the deep learning and AI community as being a flexible, fast, and easy-to-use framework for building deep neural networks.
Cloudera
APRIL 9, 2021
The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Data Collection – streaming data.
Altexsoft
JULY 23, 2020
When speaking of machine learning, we typically discuss data preparation or model building. Living in the shadow, this stage, according to the recent study , eats up 25 percent of data scientists time. MLOps lies at the confluence of ML, data engineering, and DevOps. More time for development of new models.
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.
O'Reilly Media - Data
JULY 18, 2018
It’s no secret that companies place a lot of value on data and the data pipelines that produce key features. In the early phases of adopting machine learning (ML), companies focus on making sure they have sufficient amount of labeled (training) data for the applications they want to tackle.
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