Deep AI Integration in IT Project Management at SKILup Days 2024

Welcome everyone. In today's rapidly evolving landscape, artificial intelligence based projects. Machine learning based projects are not a one-time implementation anymore.

They require a lot of ongoing refinements, continuity and how they can sustain their value in the compar transformation is important. This is me spe your guide for the session and I'm founder for Citrix Technologies, uh, our metaverse firm and also mentor for Startup India, which is on government India's initiative. Today we will deep dive into practical strategies for ensuring the continuous improvement in artificial intelligence and machine learning projects.

We'll explore the challenges that project managers face and look at the best for all the practices for achieving the long term success. And we'll look at some of the real world case studies to make this concepts more tangible. So let's deep dive into the AI projects.

So quickly we'll go with the agenda. What are we covering today? So we'll start by discussing why continuous improvement is, why do for artificial intelligence projects.

Then we'll move to artificial intelligence and machine learning, project management strategies and we'll deep dive into model retraining and how we can ensure the long term value from the projects, how we can make them more sustainable. And then we'll finish with few real world examples that deploy examples, how we can see a ized version of if you want to deploy an EIM and project, what would be the best way a case study should bring this all ideas to life when we think of adding value from the session. Okay, so let's start.

So why continuous improvement is critical for architectural intelligence success. That's always for thought PI models, much like uh, data that they rely on are dynamic over time. They decrease due to challenges in the data patterns and technological advancement.

That's why continuous improvement is essential, not just keeping AI systems efficient, but also for driving ongoing innovation and staying competitive. We think of taking example, think of Netflix recommendation engine. They have been continuously improving their algorithms based on evolving user behaviors, which are customer trends, their usage patterns and their age profile, their demographic profile and different customer profiling which are impaired into real time and gets updated real pay.

Now they managed to deliver this highly personalized content and keep users engaged. This is a clear example of ai, which is at its best way when we are continuously improving the system according to the market needs and AI ML project management. If we happen to discuss the best practices and approach managing AI and ML projects require a strategic approach, we need to keep on adopting agile methodologies so that we ensure that we continue with frequent iterations, which will allow teams to adapt as they receive the feedback they'll continuously improve.

The feedback can be from the market, from the users, from internal teams, from the project team, the supply team, or different team members. All channels of feedback in today's world are most important, where we learn, listen and inspect and adapt the changes. Collaboration across the team is the key behind driving AI projects.

AI projects are really confined to data scientists alone. The states scaleability is crucial and your AI model should evolve as your business task. When we think of managing AI ML projects, which includes the careful planning and this planning is needed across various stages of development, right from the data collection till the deployment.

Let's try to deep dive into an example of, uh, uh, AI ml. Now when we think of taking this real world examples, the objective to use artificial intelligence is to predict the machine failures, uh, and how it can also increase or optimize the maintenance schedules. That's the example that we will see in any project when we think of taking such examples.

Typically it starts from uh, a sequential format. So on my slide I'm not sure how far you will, will be able to see, but remember about the four aspects. One, data collection, it's very important how you treat the historical data.

Maintenance of historical data and cleaning up and labeling the data is most important at the start of the project. Then comes model development. When we think of a model development for any project, machine learning, uh, algorithms are built to detect the failures.

For this, you need to have a subject matter expertise pool. You need to connect with the data scientists, the ground experts, the field experts, the backend experts, and the entire off process owner group. You need to involve the product group as well.

When we think of creating such model development, it needs a rigorous time investment and understanding and then comes the pre testing and retraining the data. So in testing and retraining data, we need to continuously retrain the model species that year time data. A project managers or project owner should really think of investing, uh, ample amount of time, which should suffice a mature data model even before you think of making it live through the proof of concepts.

And once you have the tested and retrained model which is scalable for your proof of concept, then you should move forward for the deploy deployment. Not at an large scale. Deployment should also be for a finite segment to test the solution if build is making sense for the business or not deployment, uh, against a sense where artificial intelligence if in this example was deployed for across the factory equipments, it was intending to reduce the downtime and it really increased the downtime by 20%.

Uh, sorry, reduced the downtime by 20% and cutting the maintenance cost 2050 8%. Now how was this achieved by the need of clear planning, understanding the regular updates and making it scalable to ensure that the project is ongoing success and it'll yield the product, which is uh, a good for practice, a good for improvement for project managers in AI ML space. This example highlights how structured planning mostly the cross-functional collaboration and iterative improvement are critical for delivering the values.

You may see the time gaps which are uh, divided and they are clearly aggressive. This best practices will always ensure that the AI project aligns with business goals while managing the risk more effective. Let's deep dive into strategies for continuous learning and artificial intelligence space in AI systems.

As a project team, we must learn continuously to stay relevant with the market, relevant with the business and relevant with the ask of the products. And this relevancy starts with establishing the robust feedback loops. This feedback loops, which are the feeds from the real world data, the data that is packed into the model, regular data sets should be updated and there should be a frequency validations, checks, quantity, validations and check accuracy, validations and check as well frequently by the project team, by the product team, by the experts, by the market, and by the industry experts.

When we say that this checks will help us to make the necessary updates to ensure that the model remains accurate over time, by the time we may have started the project till the time we have deployed and tested, remember there would have been lot of changes. I've seen 80% of the project after deployment. They have to initiate a change management process because they have not been able to think of the gap from the planning till deployment speech.

The changes not being taken care if we think of automating the errors. Automating error analysis should help identify where your AI solution may be falling shallow, which will allow you to make the corrections at the right time and real time even before it's deployed. If I happen to take a wonderful example, Google's search algorithms are a prime example.

Each interaction each click each search, each scroll feeds back into the algorithm, which enables the solution to deliver more accurate search result that continuous learning in action, um, demonstrate how clearly we can take and improve the use of its to make solution clearer better. Now, uh, if we happen to talk about, uh, retraining, if we happen to talk about making the models better, more efficient, retraining is the aspect that is really should be taught. Retraining is critical for keeping the AI models performing optimally.

One should always set the regulat interval for retraining. This models based on your business needs, your models need the accuracy levels that you have been checking, uh, and the quality checks you have been performing. Once we understand the needs, we should keep on monitoring for the drifts.

When a model's prediction become less accurate, we should take that as a trigger for analyzing analysis and taking this into the improvement aspect. AB testing can help you all compare how you can retrain the models with current versions so that we ensure that they are truly improving without disrupting operations. I remember uh, the model for Spotify.

Spotify, a music company retrains its recommendation engine regularly so that they can reflect the user preferences and user behaviors. Now they keep the platforms recommendation fresh and highly engaging. This is something that is possible because of the continuous feedback, continuous analysis, continuous inspection and continuous improvement.

Each user action is taken as a improvement aspect on the project system or on AI and engine engine. When we try to think of pre-training, when we try to think of improving, why do we think of optimizing the solution? Because we will want to ensure that we have a long term value derived out of any AI project.

Long-term success in AI projects or AI products would require aligning your AI project objectives with the broader business codes. Scalability and sustainability are essential parameters for your model. You must grow as your business evolves, as the customers have their expectations changing, the needs changing.

Your model should change, your model should evolve. A real time monitoring is the key for AI projects. You need to detect and adjust to the performance changes as they happen and should not wait for the negative feedback or less accurate data.

If we think of, uh, a process, AI process or AI project process, think of it as a continuous loop. That's why I've kept this loop on the slide. Now, ai, inci, AI insights or the artificial intelligence based insights will feed directly into the business business decisions.

When this business decisions are guided, they will future, uh, they'll be the future AI enhancement, they'll be the solution enhancement. And once you adopt this, you will try to use this model in the business. We try to again connect the feed and this is the cycle that it should repeat.

This constant alignment will drive the long term business value, customer value, and also business model value. I'd say, and let's try to deep dive into one more case study. When we think of continuous improvement, um, I think of Amazon's uh, initiatives.

They are the pioneers in the space. Amazon is a great example of continuous AI improvement. Their AI system constantly analyze the sales trend, the weather data, and even external factors like supplier performance.

This is to optimize their supply chain by continuously retraining their models. Amazon keeps its operations efficient, highly responsive, and they are able to accommodate any changing conditions. One, Amazon uses uh, AI to optimize its supply chain and they do it by continuously retraining its model.

It can predict the sales train, it can adjust the inventory, it can adjust the pricing. It can also estimate the delivery in real time. For AI project managers, this is the best example which demonstrates the importance of both adaptability and proactive adjustments.

Taking into consideration the internal and external factors, not just from the market but from the organization, from the ecosystem of the business, absolutely understanding the goals of the business and then deploying the solution which is best fit for the Now let's try to recap what we have taken through. To wrap things up, continuous improvement isn't just an option for ai. It's a necessary implementing the feedback loops, regular retraining, aligning the AI goals with business strategy will ensure that your project delivers the long, long-term scalable value.

The success of AI systems lies in their ability to evolve alongside your business. Yeah. With that we'll conclude our presentation.

I would like to hear your thoughts and questions. Feel free to ask about how the strategies can be applied to your own AI ML projects or challenges that you may be facing in the current role. And I would love to absolutely share any project template or details or guidance that you need forward forward.

Once again, this is Professor. I am the CEO and P for Citrix Technologies Private Limited. We are a metaverse well and uh, I'm also the mentor for Startup India, which is an initiative of government of India or almost 47 plus style at the moment.

Thank you for any questions. Please do use the chat to put them up. I definitely answer for sure.