In this lesson we will take a look at AI Deployment and Future Trends – Practical Deployment Strategies and Emerging Trends in AI.
At the end of the lesson, you will be able to:
- better understand AI implementation mechanisms in various sectors,
- discuss about practical deployment strategies,
- discuss the advantages and disadvantages of emerging trends in artificial intelligence.
During the lesson you must read the written explanations and follow the given instructions at interactive elements. To achieve the designated learning outcomes the learning process by offering relevant training content, like media, interactive activities etc.
Practical Deployment Strategies
What is AI Deployment?
Deployment is the method by which you integrate a machine learning model into an existing production environment to make practical business decisions based on data. It is one of the last stages in the machine learning life cycle and can be one of the most cumbersome.
What is the Machine Learning Life Cycle?
The machine learning life cycle is the cyclical process that data science projects follow. It defines each step that an organization should follow to take advantage of machine learning and artificial intelligence (AI) to derive practical business value.
https://www.datarobot.com/wiki/machine-learning-life-cycle/
What is a machine learning Model?
A machine learning model is a program that can find patterns or make decisions from a previously unseen dataset. For example, in natural language processing, machine learning models can parse and correctly recognize the intent behind previously unheard sentences or combinations of words. In image recognition, a machine learning model can be taught to recognize objects – such as cars or dogs. A machine learning model can perform such tasks by having it ‘trained’ with a large dataset. During training, the machine learning algorithm is optimized to find certain patterns or outputs from the dataset, depending on the task. The output of this process – often a computer program with specific rules and data structures – is called a machine learning model.
What is Model Deployment?
Deployment is the method by which you integrate a machine learning model into an existing production environment to make practical business decisions based on data. It is one of the last stages in the machine learning life cycle and can be one of the most cumbersome. Often, an organization’s IT systems are incompatible with traditional model-building languages, forcing data scientists and programmers to spend valuable time and brainpower rewriting them.
Deploy an AI model
Artificial intelligence (AI) services and machine learning models, like their predecessor traditional modeling approaches, benefit from continuous monitoring and performance optimization. With AI, you’re delivering a broad range of solutions across industries, from finance to consumer products to
oil and gas and retail. Applications for customer service, virtual agents, and information processing for service delivery all must adapt to the ever-changing trends in data and processing.
For example, a retail store might offer new products periodically, which need to be recognized and reflected in its retail applications. Similarly, when an insurance company’s policies change based on its increased business offerings, its machine learning models must adopt and incorporate those changes into the system.
To incorporate this adaptive approach to assimilate changing data and processes, use an iterative model deployment that can detect the changing patterns in data and processes. Plan to periodically update the machine learning models to deliver up-to-date value and capabilities.
The domain and technology experts are the key stakeholders of this deployment approach. The premise of the continuous model deployment is to continuously monitor and identify changing requirements, both programmatically and through business requests.
Blue-Green Deployment
Blue-Green Deployment involves maintaining two identical production environments: “blue” (currently active) and “green” (new version). The new version is deployed to the “green” environment, and once it is tested and verified, traffic is switched from the “blue” to the “green” environment.
Use Case: Reduces downtime and allows for quick rollback in case of issues, making it suitable for critical systems.
Shadow Deployment
In a shadow deployment, the updated version of a machine learning model runs parallel to the existing one, but the results are not used in the live system. This allows for monitoring and validation without impacting the actual production environment.
Use Case: Useful for testing new versions without affecting live operations, particularly when deploying critical models.
Canary Deployment
Canary deployment releases a new version of a machine learning model to a small subset of users or instances before rolling it out to the entire user base. This approach allows for real-time testing and monitoring of the new features or improvements in a controlled environment.
Use Case: Useful for validating changes, minimizing risks, and catching potential issues early before a broader release.
A/B Deployment
This is a deployment strategy in which updates or new versions are gradually rolled out to different user segments, similar to A/B testing. In this context, this means deploying the updated version to a subset of users (Group B) while the majority continues to use the current version (Group A). This allows for real-time comparison of both versions in a live environment.
Use Case: This deployment strategy can be effective in scenarios where a rolling release is preferred, allowing you to monitor and evaluate the performance of a new release before a full-scale rollout.
Multi-Armed Bandits (MAB) deployment
This is a dynamic decision-making strategy used to optimize resource allocation in scenarios with multiple options. In applications such as A/B testing, content personalization, or online advertising, MAB algorithms intelligently balance exploration and exploitation, making real-time adjustments based on ongoing feedback. By continuously learning from outcomes and adapting, MAB deployment maximizes a specified reward metric, such as click-through rates or revenue. It offers an efficient and adaptive solution for scenarios where decision-makers must navigate the exploration-exploitation dilemma to achieve optimal outcomes.
Let’s check your knowledge! Check what You found out about practical strategies for implementing AI
QUIZ – Practical Deployment Strategies
https://www.datarobot.com/wiki/machine-learning-life-cycle/
https://www.databricks.com/glossary/machine-learning-models
For those looking for an engaging conversation on this topic, we recommend listening to this podcast:
Spotify – AI for Good – Strategies for Ethical and Sustainable AI Development
Emerging Trends in AI
Artificial intelligence is an incredibly dynamic field, but below are 10 emerging trends:
Explainable AI
Explainable AI focuses on making AI models more interpretable and understandable. This trend addresses the need for transparency and accountability in AI systems, especially in critical applications where decision-making impact is significant.
AI in Edge Computing
Edge computing involves processing data closer to the source rather than relying solely on centralized cloud servers. AI in edge computing enables real-time decision-making, reducing latency and enhancing privacy, making it well-suited for applications like IoT devices.
AI Ethics and Responsible AI
As AI systems play a more significant role in decision-making, there’s a growing emphasis on ethical considerations. Responsible AI practices aim to address issues such as bias, fairness, transparency, and the societal impact of AI technologies.
Federated Learning
Federated learning allows models to be trained across decentralized edge devices without exchanging raw data. This approach enhances privacy and security, making it suitable for applications where data privacy is a top priority.
AI in Healthcare
The healthcare industry is witnessing increased adoption of AI for tasks like medical imaging analysis, drug discovery, personalized medicine, and predictive analytics. AI contributes to improved diagnostics, treatment planning, and patient outcomes.
Natural Language Processing (NLP) Advancements
Advances in natural language processing have led to more sophisticated language models, enabling applications like language translation, sentiment analysis, and chatbots. Models like GPT-3 demonstrate the potential for understanding and generating human-like text.
AI-driven DevOps and MLOps
Integrating AI into DevOps and MLOps processes streamlines the end-to-end machine learning lifecycle. This includes automating model training, deployment, monitoring, and continuous improvement for efficient and scalable AI operations.
Generative Adversarial Networks (GANs)
GANs have become a prominent technology for generating realistic data and images. Beyond image synthesis, GANs find applications in art, fashion, and content creation, showcasing their versatility in creative domains.
AI-driven Cybersecurity
The integration of AI in cybersecurity involves using machine learning algorithms for threat detection, anomaly identification, and automated responses to security incidents. AI enhances the ability to analyze vast amounts of data to identify and mitigate potential cyber threats.
AI-powered Robotics
AI is playing a crucial role in advancing robotics capabilities. AI-powered robots are becoming more adept at handling complex tasks, adapting to dynamic environments, and collaborating with humans in areas such as manufacturing, healthcare, and autonomous vehicles.
Let’s check your knowledge! Check what You found out emerging trends in AI:
For those looking for interesting materials on this topic, we recommend watching this engaging video:
Andrew Ng: Opportunities in AI – 2023
https://www.techtarget.com/searchenterpriseai/tip/9-top-AI-and-machine-learning-trends
https://www.technologyreview.com/2023/12/19/1085696/four-trends-that-changed-ai-in-2023/
https://influencermarketinghub.com/
Learning outcomes:
In this lesson we have learnt about AI Deployment and Future Trends.
By completing this lesson you are able to:
- understand and describe practical examples of AI implementation strategies,
- talk about emerging trends in artificial intelligence.
Main take-aways:
- there are many different strategies to help implement AI in an organization,
- the key to success is to adjust the appropriate strategy to the situation and characteristics of the organization,
- AI is such a dynamic topic that to stay up to date you need to constantly follow the news appearing on the market
- AI will improve our lives on many different levels in the coming years.
Brief introduction of the next block:
This was the last lesson of the “AI revolution: trends and tools” course. We hope that the materials we have collected have allowed you to learn more about this topic. In the next course, you will gain extensive knowledge about AI in various industries.
