Achieving Next-Level AI/ML Success in 2024 – with Databricks

Introduction

Deep learning technologies like AI and machine learning are transforming business applications to become more intelligent in 2024 and beyond. This development has wide-reaching implications from healthcare to the education industry.

Customers are already integrating AI/ML into their software applications. However, they continue to face challenges like:
● Choosing the best AI-powered data model for various use cases.
● Lack of internal knowledge and domain expertise in foundational models.
● Operationalizing AI models for quality and debugging purposes.

With Databricks, organizations can overcome these challenges readily. They can exploit the full potential of the data at hand and also extend their AI initiatives into the future by building Generative AI applications for a variety of use cases. Using its Foundational Model APIs, Databricks provides immediate access to Large Language Models (LLMs) like Llama 2 and MPT. Similarly, with External Models, organizations can add endpoints for accessing AI models, and external (or outside) Databricks. For instance, Azure OpenAI GPT, Anthropic Claude, and AWS Bedrock.

Possible use cases of Databricks in the AI and machine learning domain

In this blog, let’s discuss 6 possible use cases of Databricks in the AI and machine learning domain:

1. Predictive Analytics
Using AI-powered predictive analytics, organizations can analyze data patterns for business risks and opportunities. Predictive analytics is not possible without Big Data. For instance, in the engineering domain, predictive analytics works on data retrieved from machine sensors, instruments, and connected systems.

With Databricks, organizations now have a scalable collaborative platform for data analysts to build and deploy AI-powered predictive models. For example, retailers can leverage Databricks to analyze customer data and predict purchasing behavior and market trends.

Additionally, with machine learning algorithms, retailers can optimize their inventories and personalize their marketing campaigns for a particular region or demographic.

2. Energy production and distribution
The global energy & utility industry is faced with multiple challenges like low energy production, rising costs, and inefficient distribution. As the industry gradually shifts to renewable energy, companies need to improve the efficiency of their energy production and distribution.

With the Databricks platform, energy companies can leverage real-time data from various sources including weather forecasts and IoT-connected sensors. Through real-time data analytics, Databricks can improve their decision-making process, thus delivering a more reliable and sustainable energy system.

Similarly, machine learning models in the energy sector can deliver predictive maintenance based on the data collected from connected machines. This helps in reducing operational failure and machine downtime.

Here’s a customer success story of how Shell leveraged Databricks to modernize its global operations.

3. Personalized healthcare
Healthcare companies can now deliver patient-centric care by combining the power of data with AI technology. By unifying data analytics and machine learning, healthcare organizations can improve patient engagement and precision care.

With Databricks, healthcare providers can easily analyze large volumes of biomedical and genomics data. Healthcare researchers can identify genetic variations or monitor the progress of any disease. This helps them develop personalized care for individual patients. Similarly, Databricks can innovate precision medicine, resulting in accurate diagnoses and improved patient outcomes.

AI-powered precision prevention is leveraging population data to identify the patients who are at the highest risk of any disease or infection. Here’s how Databricks enabled Walgreens to personalize their patient experience.

4. Supply chain management
In a complex global market, supply chain management is critical for many organizations. Some of the key elements of supply chain management include supply chain planning, sourcing, forecasting, production, and inventory management.

With the Databricks platform, supply chain companies can build a resilient and predictive supply chain. Along with supply chain planning, Databricks can scale and fine-tune supply chain forecasts to predict market demand. Real-time analytics can help companies monitor and respond quickly to supply chain disruptions or global events.

Similarly, Databricks’ machine learning capabilities enable organizations to build efficient predictive models that can optimize the entire supply chain. Apart from improving demand forecasting, organizations can leverage machine learning to reduce their inventory costs and optimize their logistics operations.

Here’s a customer success story of how Databricks transformed the digital ecosystem of an Australian-based rail transportation company.

5. Smart buildings & utilities
Real-time data is the driving force for smart buildings and public utilities. The key to building smart buildings and utilities is to harness the generated data to make informed decisions about energy efficiency and floor layouts. While smart buildings collect a lot of data, they are difficult to understand, particularly if they are integrated with other technology systems.

The AI-powered Databricks platform can assess real-time data from existing facilities and identify potential bottlenecks or issues. For instance, power consumption in an underutilized conference room or an unoptimized utility grid performance.

Here’s a customer success story of how Databricks enabled data-driven insights for a power generation company.

6. Customer churn
Companies with the highest customer retention grow their revenues 250% faster than their competitors. Hence, more companies are focusing on retaining customers for long-term growth. Customer churn can occur at any stage of the customer lifecycle. Most organizations are unable to accurately predict when individual customers are likely to churn or leave.

According to Microsoft, Databricks can predict customer churn with 90% accuracy. Additionally, Databricks offers a feasible recommendation strategy to prevent customer churn. Besides, Databricks provides modern techniques like neural networks and gradient trees to detect customer churn. Through proper configuration and evaluation, these techniques can detect subtle shifts in customer data patterns, thus providing more accurate models.

Conclusion

With the mainstreaming of AI applications, organizations are adopting a data-driven approach to their decision-making process. With its unified analytics, Databricks is the best platform to deliver efficient analytics. With this cloud-based platform, more companies can develop and deploy AI-powered applications across use cases.

As a Consulting and SI partner for Databricks, Pratiti Technologies is empowering clients across industry domains with our advanced Data and AI capabilities. With this robust data platform, our customers are efficiently managing their data processes with real-time insights.

Do you want to know more about our offerings? Contact us today!

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