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That said, for every unsuccessful launch, there have been resounding AI product successes that form the foundation of the age to come: the age of AI-driven solutions that will change humanity forever. 

As with all things technology, AI Models product deployments come with issues that when resolved pave the way for the wonderful things we have seen in science fiction movies (and then some).

Our team of experts have bared their minds on the related AI product launch issues and various ways of resolution. 

Star Kashman, Counsel at C.A. Goldberg PLLC

“The use of AI models is an intricate and complicated process, where precision is key. To ensure successful use, it is essential to begin with extensive testing, validation, and ethical considerations before implementing the AI model. This preliminary phase should address potential biases in AI the data and responses, the transparency of the algorithms and privacy of the model, and the cybersecurity related protections in place to protect against adversarial attacks.

The strategy for deployment should include a strong framework, ensuring there are clear policies and mechanisms for platform accountability in place. This is vital for troubleshooting issues, and maintaining trust of stakeholders and the public who may be consumers and users of the AI model. It’s imperative to involve legal and privacy experts to navigate the complex regulatory environment.

Post-deployment monitoring is equally as important, to ensure everything is running smoothly. Continuous oversight and the ability to adapt to emerging challenges can prevent the negative “boom” effect you mentioned. Many people choose to not prioritize ethics, cybersecurity, privacy, and safety until it is too late and they are dealing with an expensive issue on their hands. It is best to prepare ahead of time to prevent issues like this, and to be ready and monitoring in preparation for any unanticipated attacks.

This will help organizations to rapidly respond to any deviations from expected outcomes. This proactive approach to AI deployment protects against unintended consequences and potential dangers.”

Irina Bednova, CTO at Cordless

“The first strategy is to 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻/𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 (𝗖𝗜/𝗖𝗗) 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀. These pipelines automate the testing and deployment of your AI models, ensuring that any changes are immediately evaluated for performance and compatibility. This reduces the risk of “BOOM” scenarios, as problems can be identified and addressed before the model is deployed.

The second strategy is to 𝘂𝘀𝗲 𝗔/𝗕 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗳𝗼𝗿 𝗺𝗼𝗱𝗲𝗹 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁. These methods allow you to gradually roll out changes and compare the performance of new models against existing ones. 

This way, if a new model is underperforming, you can revert to the old model without significant impact.

The third strategy is to 𝗶𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆. This involves implementing tools and practices that provide insight into the performance of your AI models in real-time. Observability allows you to detect and diagnose issues quickly, reducing the time it takes to resolve problems and minimizing their impact.”

Todd Cochrane, CEO at Blubrry

“Blubrry Podcasting’s deployment approach has been one that we have employed for nearly six months through testing various tools regularly and formulating which tools worked best. Over about two months, we focused on a small number of tools to be used by our sales and marketing team.

This then led to the internal discussion on how we can help our customers. An advantage we have had on this is that while we provide tools and services for our podcasters, a large number of our team members are also podcasters and we started formulating the tools and functions we would integrate on the platform. This began by looking at how we used AI tools and the tasks that helped us produce better podcasts.

We polled our customer base, got an understanding of what they wanted, and started working on building our integrations. My mandate was that we would not be locked to one model. I wanted to switch to a better model within a reasonable time, not if, but when one appears.

Finally, we asked our customers to beta test and provide feedback. That is where we are today, and if all goes well, we will launch our new AI tools in about three weeks. Overall, we look at models regularly to see how they are improving and/or shifting.“

Daniel Li, CEO at Plus Docs

“The best way to successfully deploy AI models is incrementally. You don’t need to go from zero to artificial general intelligence overnight! Here are some tips on how to successfully use AI to improve productivity and speed up your workflows:

Start by making a mental note when you have done the same task 4-5 times in a row, and then ask yourself if you could try to automate the task with AI. Then use a basic AI tool like ChatGPT or a more specialized tool to automate the workflow. Once you have a basic AI flow, tweak the prompts and the workflow to continue improving it. After you have several of these AI automations, look at the bigger picture and identify other opportunities to create efficiencies with AI.”

Brian Prince, Founder & CEO at Top AI Tools

“To successfully deploy AI models in today’s fast-paced society, businesses must prioritize data quality and quantity. Ensure your data is clean, relevant, and diverse. You’ll need lots of it, and it needs to be in pristine condition. Invest in systems that capture a wide range of inputs and offer robust training data validation for the best results.

Foster a culture of collaboration between data scientists, domain experts, and decision-makers. Encourage cross-functional teams to work together closely. It’s true what they say: Teamwork makes the (AI) dream work. You need to ensure that your AI solutions solve actual problems and that you’re not just deploying technology for the sake of having the latest and greatest. Any addition to your tech stack, AI included, should align with your company’s goals and fit seamlessly into workflows.

Along the same lines, be transparent and realistic about what the AI can and cannot do. Make sure stakeholders understand how the AI works, the limitations, and even the ethical considerations and potential biases. Test, fail, repeat should be your mantra, as your AI will adapt and continue to learn.

The world of AI is always changing, so be agile in how you approach it. Stay up to date with the latest tech and trends, and be willing to tweak your strategies as you go to get the most out of your AI efforts.”

Sorcha Gilroy, Head of Professional Services (EMEA) at Peak

“We can talk about getting your data AI-ready, getting budget sign-off and stakeholder buy-in all day long, but the secret to successful deployment of AI models comes from an organization’s overarching approach to AI adoption.

All too often, we see a monolithic approach. Organizations that go down this path often embark on expensive and lengthy data transformation programs, investing anywhere from thousands to millions on AI infrastructure. This approach may come with an appealing vision, but easily fails to deliver on vision of AI transformation in practice, meaning adoption can take years and go massively over budget before delivering any results.

The modular approach, on the other hand, looks at AI adoption as a project, managing AI deployment on a case-by-case basis. From almost a decade operating in this space, we know the modular approach should start by delivering the easiest or most important AI use case. 

Once that AI use case is delivered, an organization can move onto the next use case, and then the next — and so on — until they’ve implemented all the available use cases, reaching that coveted end state of AI maturity.”

Bob Rogers, CEO at Oii.ai

“My most successful, robust, and agile GenAI deployment has been a question-answering system that can call analytics functions that I have written.

The approach was to deploy a self-tuning AI pipeline with DSPy so that as new cloud-based models come online, I can immediately include them in my pipeline, automatically re-optimize the pipeline code and prompts with just a small number of examples, and then adopt these new models if they perform better on my benchmarks. This can be done with both cloud-native and on-premise AI models.

The advantages of not needing brittle hand-tuned prompts, and of being able to call my own custom analytical tools for calculations on sensitive data cannot be stressed enough. The resulting pipeline is easier to create and maintain, more robust, and more secure from a data privacy point of view.

Another strategy is to use an all-in-one AI model development and deployment platform such as H2O.ai. These platforms have tooling to deploy, monitor, retrain, and update AI and machine learning algorithms that can be controlled from a single user experience. One nice advantage of this approach is that you can build your own AI model and then deploy and monitor it through the platform. 

This is the approach we took at a large academic medical center where we collaborated with H2O.ai to develop a custom model and then leveraged H2O.ai tooling to deploy it, resulting in an efficient AI automation pipeline to process millions of incoming fax requests for a variety of services.“

Ryan Doser, VP of Client Services at Empathy First Media

“While the term “AI models” can encompass a variety of meanings, I am going to assume you are referring to large language models (LLMs). Ever since the launch of ChatGPT in late 2022, dozens of LLMs have publicly launched. Some are useful but most AI models are overhyped. The best strategies for the successful deployment of AI models includes the following:

1. Major Investments in Resources

The last thing you want to do is “cheap out” on the deployment of an AI model. OpenAI, Anthropic, and other notable LLM companies have received billions of dollars in investments from tech behemoths. Google Gemini of course has Alphabet Inc to fund their venture. To ensure a successful deployment, investments should be allocated towards infrastructure, data collection, marketing, and talent (engineers, data scientists, etc.).

2. Focus on Real Value

A successful deployment means an AI model must address specific needs and deliver tangible benefits. There are dozens of LLMs available on Hugging Face and the Internet in general, but most of them are shiny objects. Clear value propositions should be defined and compared to what AI models currently exist to the public. Some high-impact use cases to focus on would be task automation, improvement on ideation, text-to-video technology, and the creation of personalized experiences.

3. Prioritize User-Experience and Interpretability

AI is extremely overwhelming and confusing to the average person. Making AI models understandable and easy to use will differentiate an AI model from others. ChatGPT is a perfect example of this. Every successful AI model that has been deployed so far has a user-friendly interface, clear explanations, visualizations, help guides, and a process where user feedback is taken seriously.”

 Eric Siegel, Ph.D., Author, The AI Playbook: Mastering the Rare Art of Machine Learning Deployment (MIT Press)

  1. “Value: Establish the deployment goalThis step defines the business value proposition: how ML will affect operations in order to improve them by way of the final step, model deployment. 
  2. Target: Establish the prediction goal. This step defines exactly what the model will predict for each individual case. Each detail of this matters from a business perspective.
  3. Performance: Establish the evaluation metricsThis step defines which measures matter the most and what performance level must be achieved—how well the model must predict—for project success.
  4. Fuel: Prepare the data. This step defines what the data must look like and gets it into that form.
  5. Algorithm: Train the modelThis step generates a predictive model from the data. The model is the thing that’s “learned.”
  6. Launch: Deploy the modelThis step uses the model to render predictions (probabilities)—thereby applying what’s been learned to new cases— and then acts on those predictions to improve business operations.”

Randy Lariar, Director of AI and Analytics atOptiv Security


What steps to take before introducing generative AI tools?

“It is important to recognize the transformative potential of AI and the heightening regulatory and operational risks mean organizations need to have a plan. Some are already drafting AI policies, governance processes, staffing plans and technology infrastructure to be ready for the surge in demand for AI capabilities and associated risk. Important steps include:

  1. Understand AI: Begin by gaining a comprehensive understanding of AI, specifically generative models and their implications for your business. This includes grasping potential benefits, risks and the ways these models are beginning to be incorporated into technology.
  2. Assess Current Capabilities: Review your existing technological infrastructure and skills base. Identify gaps that could hinder AI implementation or lead to enhanced risks to develop a strategy to address them.
  3. Develop AI Policies: Establish clear enterprise AI policies that define guidelines for its usage and protection within your organization. These guidelines should cover topics like approved use cases, ethics, data handling, privacy, legality and regulatory impacts of AI-generated content.
  4. Establish Governance Processes: Create governance processes to oversee AI deployment and ensure compliance with internal policies and external regulations.
  5. Plan Resource Allocation: Consider staffing and resourcing plans to support AI integration. This may include hiring AI specialists, engaging with consulting firms, developing staff training, or investing in new technology.
  6. Prepare for Risks: Generative AI can present many unique risks, such as IP leakage, reputational damage and operational issues. Risk management strategies should be included in all phases of your AI plan.
  7. Manage Data Effectively: Ensure that your data management systems can support AI demands, including data quality, privacy, and security. “


How important is it to have monitoring and control procedures in place?

“Monitoring is critical for AI because the inner workings of the model are very hard to trace. This makes it very hard to explain precisely what inputs drive AI content creation or decision making. Monitoring and logging of AI inputs and outputs is critical to understand what people are doing with your AI and how it is responding. Monitoring and logging additionally allow you to “threat hunt” your usage to detect patterns of misuse or risk that can be mitigated through enterprise controls. Monitoring also plays a critical role in model performance optimization and ensuring AI ethics and fairness.

As with any risk, AI introduces a need for controls that can reliably reduce the likelihood of certain bad things happening. This can include traditional cyber and risk controls that harden the entire AI infrastructure and protect it from accidental or malicious data loss. It also will need to consider new forms of risk such as “prompt injection” and AI agents performing autonomous tasks outside of the scope of their design. Strong guardrails are a necessity to enable teams to seize many of the opportunities of AI without exposing the organization to significant new risk.”

Gaurav (GP) Pal, CEO and Founder ofstackArmor

“The successful deployment of AI models lies almost entirely around its security and safety – from the security and safety of the training data and the model to production operations all must be secured. Since AI is an emerging technology, it can be difficult to govern efficiently, and this is especially a problem for sectors that are heavily regulated, such as government, financial services, and healthcare.  

Business leaders should look to existing standards-based security frameworks especially from NIST to best protect their reputation, customers’ data and contain legal risks for their AI systems. For example, authority to operate (ATOs)  can help accelerate the adoption of AI in compliance with governance models within industry requirements. Cybersecurity risk management frameworks can be augmented to secure AI in the same way they are applied to other technologies, such as cloud computing. 

Organizations must find systematic and consistent ways to enable actions to manage AI risks and responsibly deploy trustworthy AI systems. As leaders dive into AI adoption, understanding these complex risks in a highly repeatable way is essential.”

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