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
JANUARY 17, 2024
As organizations turn to artificial intelligence for operational efficiency and product innovation in multicloud environments, they have to balance the benefits with skyrocketing costs associated with AI. The good news is AI-augmented applications can make organizations massively more productive and efficient.
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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
JANUARY 31, 2024
Augmenting LLM input in this way reduces apparent knowledge gaps in the training data and limits AI hallucinations. The LLM then synthesizes the retrieved data with the augmented prompt and its internal training data to create a response that can be sent back to the user. million AI server units annually by 2027, consuming 75.4+
The Netflix TechBlog
MARCH 7, 2024
Since its inception , Metaflow has been designed to provide a human-friendly API for building data and ML (and today AI) applications and deploying them in our production infrastructure frictionlessly. There are several ways to provide explainability to models but one way is to train an explainer model based on each trained model.
The Netflix TechBlog
NOVEMBER 17, 2022
While conventional video codecs remain prevalent, NN-based video encoding tools are flourishing and closing the performance gap in terms of compression efficiency. We employed an adaptive network design that is applicable to the wide variety of resolutions we use for encoding. How do we apply neural networks at scale efficiently?
The Netflix TechBlog
MARCH 4, 2024
We have deployed Auto Remediation in production for handling memory configuration errors and unclassified errors of Spark jobs and observed its efficiency and effectiveness (e.g., For efficient error handling, Netflix developed an error classification service, called Pensive, which leverages a rule-based classifier for error classification.
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