Scalegrid
JANUARY 5, 2024
Enhanced data security, better data integrity, and efficient access to information. Despite initial investment costs, DBMS presents long-term savings and improved efficiency through automated processes, efficient query optimizations, and scalability, contributing to enhanced decision-making and end-user productivity.
Dynatrace
MAY 17, 2023
GPT (generative pre-trained transformer) technology and the LLM-based AI systems that drive it have huge implications and potential advantages for many tasks, from improving customer service to increasing employee productivity.
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DZone
AUGUST 9, 2023
Machine learning (ML) has seen explosive growth in recent years, leading to increased demand for robust, scalable, and efficient deployment methods. Traditional approaches often need help operationalizing ML models due to factors like discrepancies between training and serving environments or the difficulties in scaling up.
Dynatrace
SEPTEMBER 3, 2020
Such a solution needs to collect a substantial amount of data at first for then having a dataset (training data) that an algorithm can use to learn from. Inevitably, this leads to one very important question addressing the efficiency of ML: can such an AI ever keep up with frequent changes and deployments? Traditional AIOps is Slow.
Dynatrace
JANUARY 4, 2024
It can be difficult to understand the basis of AI systems’ decisions, particularly when they are trained on large and complex data sets. AI systems, and their data, can be biased, either intentionally or unintentionally, reflecting the biases of their creators or the data on which they are trained. AI system bias. Data in context.
Percona
NOVEMBER 9, 2023
If you have any experience working with database software, you have undoubtedly heard the term Kubernetes a lot. Cost and resource efficiency One of Kubernetes’ main advantages is its efficient use of resources. You may already be using it daily and find it makes running applications in the cloud much more manageable.
The Netflix TechBlog
MARCH 7, 2024
In addition to Spark, we want to support last-mile data processing in Python, addressing use cases such as feature transformations, batch inference, and training. There are several ways to provide explainability to models but one way is to train an explainer model based on each trained model.
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