How can you monitor your team's progress on AI project deadlines?

There's little difference in defining clear and realistic goals, whether this is an AI-driven or not an AI-driven project. Over the last two decades, product and project managers have been following the SDLC (Software Delivery Life Cycle) and have aligned the goals to that lifecycle within the golden triangle of time, scope, and resources. In the era of AI, there is now a new model, MDLC (Model Delivery Life Cycle), which runs through four key phases: Planning, Data, Development, and Deployment. When you are well acquainted with the MDLC, you are aware of the longer deployment time that models require, and you are accurate on the resources you have, which will also allow you to set realistic and more precise goals.

?? In the dynamic world of AI project management, success hinges on precise progress tracking. I'd emphasize leveraging AI tools like predictive analytics and automated progress tracking for real-time insights. As Andrew Ng, a leading voice in AI, aptly puts it, "AI is the new electricity." It's crucial to intertwine AI with traditional methodologies to foresee potential delays and optimize workflows—regular, AI-informed team check-ins and embracing agile principles further bolster efficient deadline management. Remember, using AI to monitor AI projects isn’t just efficient; it's a strategic imperative in today's tech-driven landscape. ????

Let's consider implementing an AI knowledge base to enhance customer support. The primary goal is to reduce resolution time by 20% within six months. Break it down into milestones: first, establish a comprehensive knowledge base structure in two weeks; second, create and upload 100 FAQs within a month. Set a deadline for each milestone and define success criteria, such as a functional search algorithm and positive feedback from support agents and customers. Align these goals with stakeholders, ensuring their expectations are met. Regularly communicate progress to the team, fostering transparency and addressing any challenges promptly. This approach ensures a clear path, measurable outcomes, and proactive issue identification.

As an academic overseeing AI projects, effective monitoring entails setting clear goals and milestones aligned with project objectives. Regular check-ins assess team contributions, address challenges, and ensure alignment with timelines. Utilizing collaborative tools and project management software aids in tracking tasks and overall progress. This approach provides a roadmap for the team, enabling proactive monitoring, timely adjustments, and efficient execution of AI projects.

AI projects are unique. Particularly Gen AI projects. Technology proliferates at a pace that’s difficult for the stakeholder or project team to keep up with. Hence building incrementally and iteratively in short sprints with well defined scope and goals is key. Define and measure success as you go because you can’t improve what you can’t measure. Build feedback mechanisms and analytics to gather subjective and objective insights from end user for the team.

Best practices from engineering can be adapted to AI project management. Depending on the team's size, methodologies like Scrum, Kanban, or a hybrid like Scrumban can be particularly effective. What's crucial is establishing routines such as daily check-ins and sprint plannings. These routines not only keep track of progress but also identify potential blockers early on. This proactive approach enables the team to spot problems promptly, allowing for joint decision-making rather than facing delays at the project's end. It's also worth mentioning the plethora of tools available to facilitate this process, with Jira being one of the most popular.

Agile methods such as Scrum, Kanban or Sprints may not be optimal for AI projects, as breaking them down into tickets or tasks can impede engineers' creativity. Allowing the team to delve deeply into problems and develop creative solutions, rather than rigid time boxing, works much better in my experience. That being said, it's equally important to set clear deadlines and expectations so that there's a shared goal ahead for everyone, and having regular meetings to foster collaboration and help remove blockers that may arise.

Why Agile? The Agile approach is not a one-size-fits-all solution but is adaptive. Agile is an iterative project management approach emphasizing: 1. Iterative Development: Breaking projects into manageable parts for continuous improvement. 2. Adaptive Planning: Adjusting plans based on changing requirements. 3. Collaborative Approach: Encouraging frequent communication and teamwork. 4. Feedback: Prioritizing user input for a product that meets expectations. 5. Flexible Response to Change: Adapting to changes in requirements, technology, and market conditions. 6. Continuous Delivery: Regularly delivering functional components for ongoing feedback. 7. Transparency: Maintaining open communication for understanding project goals and progress.

Embracing agile and iterative methods is a strategic approach, particularly beneficial in the dynamic landscape of AI projects. Agile methodologies like Scrum and Kanban provide a framework that allows teams to adapt to evolving requirements seamlessly. This flexibility is crucial in AI, where datasets, algorithms, and objectives may change throughout the project lifecycle. In summary, adopting agile and iterative methodologies in AI projects fosters adaptability, continuous improvement, and a focus on delivering incremental value. These approaches are well suited to the iterative nature of AI development, contributing to the overall success of the project.

1. Efficient Tools: Utilize cutting-edge project management tools for seamless deadline tracking. 2. Frequent Sync-ups: Conduct dynamic team meetings to ensure ongoing progress. 3. Milestone Mastery: Set and conquer key performance indicators and project milestones. 4. Task Tech: Employ advanced software to effortlessly monitor task completion. 5.Agile Advantage: Embrace agile methodologies with nimble sprints and regular reviews. 6. Feedback Flow: Foster open communication channels for swift issue resolution.

One of the biggest strategic choices for AI projects today is around what tools to use. These decisions can make or break projects long term. With how quickly the AI space is moving today, it can be really difficult to decide if you should use an off the shelf tool or build something from scratch. Often time, it is easy for engineering teams to want to build from scratch so they don’t need to take on external dependencies. This is a useful mindset but the main consideration to keep in mind is how much time you want to focus on building tooling vs the actual product / project.

Monitor data features coverage and timeliness closely. When choosing a dataset for model training and evaluation, ensure selected features are and will remain available in production for real users. Implement quality testing aligned with project requirements, goals, and guardrails to ensure the model operates as intended. For instance, in a recommendation system, include tests that make sure the model recommends diverse content and adheres to integrity rules. Finally, prioritize version control and data versioning for develop systems that are resilient, interpretable and easy to debug.

Let me share 5 points in making a better development practice for AI projects: Data and Code Quality Checks: Implement data and code quality checks to enhance the reliability and accuracy of your AI solutions. Tools for Data Quality: Utilize data validation, cleaning, labeling tools to ensure adherence to best practices in data handling. Tools for Code Quality: Employ code review, testing, and documentation tools to maintain high standards in code development. Data and Code Protection: Utilize version control, data backup, and data security tools to safeguard against loss or corruption. Monitoring and Error Prevention: Implementing these checks helps monitor team progress, preventing errors and bugs during the AI project lifecycle.

Implementing rigorous data and code quality checks is paramount in AI projects. Quality assurance starts with robust data practices, including validation, cleaning, and labeling, ensuring the integrity and reliability of the models. Code quality, upheld through reviews, testing, and documentation, is equally crucial for maintainability and collaboration. By integrating version control, teams can track changes systematically, minimizing the risk of errors and enabling effective collaboration. Data backup and security tools safeguard against data loss or corruption, essential for maintaining project continuity. These measures not only contribute to the accuracy and reliability of AI solutions but also play a pivotal role in preventing errors.

The planning part consists of business requirements and an analytics approach. The data phase includes data requirements, collection, and preparation. The development part includes model selection and model evaluation. And finally, the deployment is not a disc or an install suite that you send your clients to install like back in the day. Many tuning and evaluation are going on with AI model deployments.

The most important question is why to track? Let me explain each: Performance Metrics (Accuracy, Precision, Recall, F1-Score): Why: Measures the effectiveness of AI models by assessing overall correctness, accuracy of positive predictions, ability to find relevant instances, and a balanced performance indicator. Efficiency Metrics (Error Rate, Latency, Throughput): Why: Evaluate the efficiency of AI solutions based on error frequency, prediction time, and system capacity, impacting overall performance and user experience. Return on Investment (ROI): Why: Assess the project's impact by measuring the value generated compared to the resources invested, aiding in decision-making, resource allocation, and demonstrating project success.

Tracking and visualizing key metrics is pivotal for gauging the effectiveness of AI projects. Utilizing tools like dashboards and charts offers real time insights into crucial performance indicators like accuracy, precision, recall, and more. These metrics provide a comprehensive view of the AI solution's quality and impact. Incorporating user centric metrics such as feedback loops, A/B testing, and user testing is essential for understanding user satisfaction and refining the model based on real world usage. In summary, a robust system of tracking and visualizing key metrics provides transparency and accountability, enabling teams to iteratively enhance their AI solutions and meet both technical and user oriented objectives.

One thing I've found helpful is integrating real-time dashboards into the monitoring process. This not only provides a dynamic snapshot of key metrics but also fosters quick decision-making. The ability to see accuracy, precision, and user feedback in real time enhances our responsiveness and ensures that our AI project stays aligned with goals and expectations.

I want to emphasize the importance of retrospectives in this context. These meetings, integral to Agile, are crucial for fostering a culture of continuous improvement. They allow teams to reflect on their journey, discussing what worked and what didn't. This reflection helps identify areas needing improvement, whether it's process adjustments, new technologies, or communication strategies. Retrospectives also promote open communication and inclusivity, giving each member a voice. Retrospectives deliver actionable outcomes, directly enhancing future work cycles in terms of efficiency, effectiveness, and team dynamics. Incorporating regular retrospectives can significantly contribute to a team's ongoing development and success.

Tailoring feedback for an AI project involves specific technical and collaborative considerations: 1. Technical Focus: - Specify technical aspects like model accuracy. - Quantify results with relevant metrics. 2. Collaboration Dynamics: - Acknowledge and appreciate team contributions. and knowledge sharing. 3. Project Alignment: - Relate feedback to overarching project objectives. 4. Learning and Growth: - Identify learning opportunities and suggest skill development. 5. Constructive and Open Dialogue: - Be constructive and positive in feedback. - Encourage open and two-way communication. This tailored approach promotes continuous improvement, motivates team members, and ensures alignment with AI project goals.

Regular and constructive feedback is a linchpin in fostering a productive and motivated team in AI projects. Open channels such as meetings, emails, chats, and surveys create a communicative environment where insights can be shared. Constructive feedback not only highlights areas for improvement but also reinforces positive aspects of the team's work. In the dynamic field of AI, where learning is constant, feedback serves as a mechanism for continuous improvement. It aligns the team with project objectives, enhances problem solving, and ensures that everyone remains engaged and invested in delivering high quality results. Regular feedback is a cornerstone for steering the team toward success in the ever evolving landscape of AI development.

Using AI to monitor AI involves implementing advanced technology to oversee and manage the performance, behavior, and outcomes of AI systems. This approach uses MLalgorithms and analytics to track key metrics, detect anomalies, and optimize the efficiency of AI models. By employing AI for monitoring, one can enhance accuracy, identify potential issues, and ensure that AI systems align with desired objectives. One use case for using AI to monitor AI is in anomaly detection for cybersecurity. AI algorithms can continuously analyze patterns in network traffic, user behavior, and system activities. This proactive monitoring helps to detect and mitigate cybersecurity incidents in real-time.

Si la organización ya tiene experiencia con la implementación de proyectos haciendo uso de la IA, en efecto se podrá utilizar la IA para monitorear nuevos proyectos con IA, pero, si es la primera vez que se estan implementando, definitivamente, no será recomendable su uso. El implementar e integrar la IA en los proyectos requiere inicialmente una evaluación previa y cumplimiento de ciertos requerimientos mínimos como son: recursos, capacitación, gobernabilidad, gestión del cambio y otros. En resumen, la IA ayudará en el monitoreo de proyectos de IA si es un proceso ya desarrollado y maduro en la organización.

Using AI as a monitoring tool is a skill many consider cutting edge today, but is likely to be common place in the future. A simple AI workflow can ingest project requirements, send daily messages to contributors, and deliver an up to date weekly summary of a projects status, all without any human intervention. When searching for tools, it’s best to find one that can support the end to end processes rather than solve just part of this monitoring problem.

One tool I find very interesting for this is called Team GPT. Not only can you have a collaborative ChatGPT workspace where all your prompts and GPTs are organized in a shared environment. But you can also monitor adoption rate by member. And we all know, AI adoption starts with practical advanced use of prompt-engineering

One of the most difficult parts of AI projects right now is how quickly the space is moving. You might start a project and halfway through realize the ecosystem or your customers have moved on to something else. In order to avoid this, focus on shipping quickly and often. Also don’t be afraid to stop halfway through if you have clarity that what you are working towards is no longer feasible. It’s better to shift directions than end up having to maintain something long term that no one is using.

Adopt an agile approach, dividing the project into sprints with achievable tasks. Regularly reassess priorities and adapt to changes quickly. This method promotes continuous improvement and flexibility in response to evolving project requirements. Along with continuous integration and deployment with the help of testing folks, this will help side by side fixing bugs in particular module devloped by particular team members working on agile approach. Last but not least project awareness among all forms a major part.

Fundamental principles of engineering and management don't change with AI. Sometimes it feels like people get carried away and lost in the jargon.

Implement Regular AI Challenges: Set up periodic AI challenges or competitions within your team. This not only encourages friendly competition but also provides a measurable way to assess the team's progress. It fosters a dynamic learning environment, stimulates innovation, and helps identify areas for improvement. Additionally, recognizing and rewarding achievements from these challenges can boost morale and motivation within the team.

AI projects require a lot of training, in my experience, we can track the progress by tracking the data labelling and the accuracy achieved as an outcome. For IDP, training with different data set and running pipeline is one criterion, however, the data pre and post-validation and extraction stage levels can be tracked via Agile methodology.