Enterprise AI
Expand AI solutions throughout your company to increase output and maintain a competitive edge. Check out list of resources at the conclusion of the piece.
The majority of developers got their start in academia, dabbling, or early-stage business. The enormity and difficulty of implementing a solution at an enterprise size quickly confront you whether you take on a professional role or bring a product from proof-of-concept to production. AI implementation at the corporate level is similar to switching from a bike to a high-performance sports vehicle in that it’s powerful, thrilling, and delicate to operate. We’ll go into detail in this article on how developers can use AI in huge enterprises in an efficient manner.
Understanding Enterprise AI
What is Enterprise AI?
The broad use of AI in a variety of business contexts to boost productivity, spur expansion, and forge competitive advantages is known as enterprise AI. Enterprise AI necessitates strong infrastructure, strategic alignment, and cross-functional cooperation, in contrast to small-scale or experimental AI programs. There are several important advantages to large-scale AI implementation, including:
- Predictive analytics for better decision-making
- Personalized services for better client experiences
- Cost reduction and operational effectiveness
- Innovation in goods and services
Setting the Foundation
Evaluating Business Opportunities and Needs
Finding business needs and opportunities where AI can have the biggest impact is the first step in applying AI. When implementing solutions in an organization, as opposed to an academic or hobbyist project, it is necessary to establish precise objectives and calculate the return on investment of the suggested solution. Solutions in the enterprise, outside of research labs, must be connected, either directly or indirectly, to generating money for the business. You could perform an analysis that includes the following in order to ascertain this:
- Determining which business processes require change and where AI could provide a special value addition.
- Carrying out a business impact analysis to ascertain the effects of your product on revenue or cost savings.
- In order to ascertain whether the solution will have a timely impact on the market, business leaders will require a clear description of goals from conception to final delivery into production.
- Obtaining funding for your solution from your product, finance, and/or R&D departments.
Creating a Multidisciplinary AI Group and Reaching Consensus
A varied team comprising data scientists, engineers, domain specialists, and business executives is necessary for the successful deployment of AI.
Before starting the engineering process, your corporate AI team should address the following important considerations to ascertain appropriate alignment and viability:
“Is this something we should bring to market?
This important question must be posed to product and business leaders when evaluating a new tool, application, or feature.By identifying true end-user need, it helps prevent resources from being squandered on projects that don’t address market demands. As renowned Lean Startup author Eric Ries once remarked, “What if we found ourselves building something that nobody wanted? What difference did it make, therefore, if they completed it on schedule and within budget?
“How do we build this?”
As an engineer, you can make a contribution by outlining the architecture, the technology stack, and the plan for implementation. It’s also an essential phase in evaluating the project’s viability and coming up with a reasonable budget and schedule that complement the company’s goals.
“Are we considering domain-specific requirements?”
Domain expertise must be consulted to ensure your solution meets the needs of the field, industry, genre, or culture it serves. By guaranteeing that the solution is pertinent and meaningful to end users, domain expertise delivers non-generic value and makes the job truly effective.
Selecting Appropriate AI Technologies
Frameworks and Libraries
Selecting the right AI technology to use is a crucial next step after your team has reached consensus on business goals. The selection of appropriate AI tools and frameworks can significantly impact the outcome of any AI endeavor, as there is a wide range available. Some considerations to help you decide:
Libraries and Frameworks
Pick frameworks that fit your challenge and team’s experience. Businesses use PyTorch and TensorFlow for deep learning. Scikit-learn is popular for conventional machine learning, whereas XGBoost and LightGBM are good for structured data.
Many firms favor cloud-based AI solutions due to their scalability and ease of installation. AWS SageMaker, Google AI Platform, and Azure Machine Learning create, train, and deploy AI models. These platforms offer end-to-end services. On-premises solutions, however, could be required for highly regulated businesses because of data privacy issues.
Model Interpretability Tools
It’s crucial to comprehend how models generate predictions as AI plays a bigger role in decision-making. You may make sure that your AI systems are transparent and understandable to business executives as well as engineers by using tools like Explainable AI (XAI) libraries, SHAP, LIME, and others.
Unmentioned but yet crucial to take into account in order to enable AI technology is data infrastructure.
AI operationalization
Starting a model’s production is just the first step. To guarantee that models continue to function as intended, operationalizing AI necessitates continual administration, oversight, and iteration. The following are some methods for successfully operationalizing AI:
Monitoring and Alerting
AI models may experience problems during production, much like any other software system. These could include data drift, which is the process of a model’s performance declining as a result of changes in the input data distribution over time. Monitoring technologies like Azure’s Application Insights and Evidently AI can assist in identifying these problems early and notifying developers of them.
Retraining of Models
AI models require retraining over time in order to adapt to evolving data and business environments. By using an MLOps pipeline to automate this process, models may be kept current and correct without needing human interaction.
Explainability and Audits
AI models may be the subject of audits in highly regulated sectors like banking or healthcare. Make that there is a transparent audit record of data usage, training procedures, and decision-making outputs, and that the models are simply comprehensible.
Unmentioned but nonetheless crucial to take into account for operational AI is cooperation with IT and Azure DevOps.
Safety and Adherence
When implementing AI systems at an organizational scale, security and compliance are crucial. AI systems are frequently in charge of sensitive data, which makes them easy targets for hackers. To ensure legal and safe AI initiatives, follow these steps:
Data privacy
Adherence to laws such as the CCPA, GDPR, and HIPAA is essential. Make that your procedures for gathering, storing, and using data are transparent. When needed, put in place systems for data anonymization or pseudonymization.
Model Security
Results from adversarial attacks on AI models may be compromised. You may defend your models against these kinds of attacks by implementing strategies like differential privacy or adversarial training.
Role-Based Access Control (RBAC)
Use role-based access control, or RBAC, to limit who has access to sensitive information and model outputs. This adds another level of protection by guaranteeing that only authorized workers can view or edit AI models or datasets.
Enterprise AI implementation is a challenging but worthwhile endeavor. Through the implementation of appropriate technologies and a strategic strategy, companies can generate substantial benefits and stimulate innovation. Long-term success in AI will depend on keeping up with trends and emphasizing moral and responsible AI practices as the technology develops.
Explore our collection of resources
View our carefully selected content, which covers topics such as retrieval augmented generation (RAG) implementation, chances to construct and exploit microservices, and improving data utilization for enterprise-level application development using RAG methodologies for both aspiring and experienced Enterprise AI. Here, they go over the essential tactics and resources to assist developers in utilizing RAG’s potential to create AI solutions that are both scalable and significant.
What you will discover
- Put retrieval augmented generation (RAG) into practice.
- Determine the possibilities to use and develop microservices
- Acknowledge chances to improve chances to use data to construct enterprise applications using RAG
How to begin
Step 1: View this video to learn how to use LangChain to run a RAG pipeline on Intel.
Guy Tamir, the Technical Evangelist at Intel, takes you through a straightforward explanation and a Jupyter notebook to carry out RAG, or retrieval-augmented generation, on Intel hardware with the help of LangChain and OpenVINO acceleration.
Step 2: Developing a ChatQnA Application Service in
Using LangChain, Redis VectorDB, and Text Generation Inference, this ChatQnA use case runs RAG on an Intel Xeon Scalable CPU or Intel Gaudi 2 AI accelerator. Specifically for LLMs, the Intel Gaudi 2 accelerator facilitates deep learning model training and inference.
Step 3: Attend the OPEA Community Days with professionals and other community members.
The goal of OPEA is to provide enterprise-grade, proven GenAI reference implementations that streamline development and deployment, resulting in a quicker time to market and the realization of commercial value. Come hang out with us at one of our fall virtual events!
Step 4: Study up on Retrieval Augmented Generation (RAG) by reading this technical paper.
Image credit to Intel
Ezequiel Lanza provides a clear road map in this essay for developers looking to scale AI solutions in big businesses. To have a significant business impact, learn how to evaluate business needs, assemble the best teams, choose AI technology, and successfully operationalize models.
