Data Analytics is the keystone of transformative technologies like Artificial Intelligence (AI) and Machine Learning (ML). In the realm of AI and ML applications, data-driven insights empower businesses and researchers to make informed decisions, unravel patterns, and predict future trends. For complete dissertation by statistics solution, visit - https://shorturl.at/oMSXY Check our site to know more about real-time data analytics examples - https://shorturl.at/oszJ6 For #Enquiry: Email: info@phdassistance.com India: +91 91769 66446 UK: +44 7537144372

1. Exploring Real-
Time Data
Analytics for AI &
ML Applications
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Phdassistance
Group www.phdassistance.com
Email: info@phdassistance.com

2. Introduction
• Data Analytics is the keystone of transformative technologies like Artificial Intelligence (AI) and Machine
Learning (ML).
• In the realm of AI and ML applications, data-driven insights empower businesses and researchers to make
informed decisions, unravel patterns, and predict future trends.
• This interdisciplinary field marries statistical expertise with advanced computational techniques to extract
meaningful information from vast and complex datasets. By harnessing the power of data, organizations can
optimize operations, enhance customer experiences, and drive innovation.
• This blog provides the dynamic landscape of Data Analytics for AI & ML, where we explore the synergy between
data, algorithms, and groundbreaking applications.
contd...

3. • Real-time data analytics plays a critical role in AI (Artificial Intelligence) and ML
(Machine Learning) applications, enabling organizations to make timely, data-
driven decisions and achieve better performance. Let's explore the key aspects of
real-time data analytics in the context of AI and ML applications:
Key aspects of real-time data analytics
Real-time Data Sources:
• Streaming Data: This includes big data analytics in healthcare from sources like IoT
devices, social media feeds, clickstreams, sensor data, etc. It's essential to handle
and analyze data as it arrives to derive immediate insights.
contd...

4. contd...
Data Processing:
• In-Memory Computing: Real-time analytics often requires processing
big data analytics services in memory rather than on disk to achieve
low-latency processing.
• Distributed Computing: Scalable frameworks such as Apache Spark
and Apache Flink are commonly used for processing large-scale real-
time data.

5. contd...
Real-Time Feature Engineering:
• Feature Extraction: Real-time analytics can involve extracting features from incoming data
streams to be used as inputs for AI/ML models.
• Feature Transformation: Converting raw data research designs into suitable formats for ML
models, such as one-hot encoding or normalization.
Real-Time ML Model Deployment:
• Model Serving: AI/ML models need to be deployed in a way that they can make predictions on
incoming real-time data streams without significant delay.
• Model Scaling: Ensuring that the deployed models can handle the high throughput demands
of real-time data.

7. contd...
Anomaly Detection:
• Real-Time Monitoring: Detecting anomalies or unusual patterns in
real-time data streams is crucial for early intervention and
maintaining system integrity.
Decision-Making:
• Automated Decisions: Real-time analytics can be used to make
automated decisions based on incoming data, reducing the need
for manual intervention.
Real-Time Dashboards and Visualization:
• Real-Time Insights: Displaying real-time analytics results through
interactive dashboards and visualizations to aid in quick decision-
making.

8. Challenges:
• Latency: Achieving low-latency processing is challenging, especially when dealing with massive data
streams.
• Scalability: Ensuring that the infrastructure can handle the increasing volume of data.
• Model Drift: Continuously monitoring and updating ML models to handle evolving data patterns.
• Data Quality: Maintaining data quality in real-time environments can be complex.

9. • Real-time data analytics is a fundamental component of AI and ML applications, enabling
organizations to harness the power of data for immediate insights and decision-making.
• It involves handling PhD in data analytics as it arrives, processing it efficiently, deploying ML models in
real-time, and addressing latency, scalability, and data quality challenges.
Check out our sample PhD Data Analytics examples to see how PhD Data Analytics is developed.

11. • The integration of AI and machine learning in big data analytics has revolutionized various industries.
These technologies empower organizations to extract valuable insights from massive datasets, uncover
hidden patterns, and enhance decision-making.
• Applications range from predictive analysis and customer behaviour modelling to personalized
recommendations and fraud detection. However, this synergy brings forth challenges like data privacy,
algorithm bias, and the need for skilled professionals.
• Despite these hurdles, the prospects are promising. As the latest artificial intelligence applications
continue to advance, it has the potential to transform how we process, interpret, and leverage big data
analytics in healthcare, driving innovation, efficiency, and competitiveness in the digital age.
A brief overview of AI and machine learning in big data analytics: applications,
problems, and future prospects
• Check out our study guide to learn more about How can AI and ML improve your data analytics
workflow?

12. • At PhD Assistance, we evaluate data important to the thesis and ensure that you fully
comprehend the outcome.
• Aside from that, Ph. D professionals will discuss your findings for and against the literature
review. Our expert and experienced team members are highly knowledgeable in many
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