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Today, we continue to explore AI/ML and recommend the AWS AI use cases exploration website. The website provides many used cases where you can filter based on your interests.
For example, we explored and summarized the cases of AI improving efficiency and productivity.
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 According to PMI's talent triangle, project managers will embrace AI in terms of skills, business accruement, and ways of working to prepare for the era of AI.
PMO｜Get Ready to Manage AI/ML Projects10/15/2024  As ML/AI and Generative AI projects become top priority initiatives in many organizations, PMOs and project managers are facing increasing demands to manage them effectively. This article will analyze the nature of such projects, what PMs must know, and what distinguishes them from non-AI/ML projects.
Use OKR to Plan.When planning ML/AI projects, it's important to acknowledge that there is often less certainty due to the need for technology research and experimentation. Unlike traditional projects, ML/AI projects have less predictable outcomes and require constant trial and error. Therefore, it's essential to allow flexibility in the timeline and scope of the model training process, data quality, and algorithm performance tuning. In such cases, using the OKR (Objectives and Key Results) approach can help focus on the results rather than the process. Managing stakeholder expectations is crucial, as AI/ML projects may take a long or iterative approach. Clearly communicating and engaging stakeholders when defining OKRs can help gain their buy-in, convey the project's values, and help them understand that it may take time to realize the benefits fully. Put Data First. ML/AI projects are heavily data-driven, meaning that the quality and quantity of data directly impact the project's success. Managing these projects requires a deep understanding of data sources, data collection, preprocessing, and data quality. As a project manager, you'll need to coordinate with data engineers and scientists to ensure the correct data is available at the right time. Bridge the Communication Gap. ML/AI projects often require collaboration across multiple disciplines, including data science, machine learning, software development, and domain experts. As a project manager, you will need to bridge the gap between these technical and non-technical teams, ensuring clear communication and that all parties understand the project goals and requirements. Tracke AI Metrics. In ML/AI projects, success is often measured by model performance metrics such as accuracy, precision, recall, F1 score, and others, which can be complex to understand. Project managers must familiarize themselves with these evaluation criteria and know how they align with business objectives. You will need to track these metrics throughout the project and adjust timelines or resources as needed. Create Cycles with Continous Iterations. ML/AI projects typically follow an iterative development process involving cycles of model training, testing, and refining. This contrasts with the more linear nature of traditional projects. As a project manager, you'll need to manage this continuous iteration while ensuring that deadlines and milestones are met, even if outcomes need to be clarified. Control the Risks. AI ethics, including the risk of bias in models, is a significant concern in ML/AI projects. As a project manager, you must ensure that the team addresses ethical considerations, such as preventing bias in the data and models and considering the societal impacts of the AI solution. Regulatory compliance and fairness should be part of your project's objectives. Pay Attention to Resources and Costs. Unlike traditional software projects, ML/AI projects require significant computational resources, especially for model training and large datasets. As a project manager, you'll need to manage cloud infrastructure or specialized hardware resources like GPUs to ensure that computational requirements are met without exceeding budget constraints. Plan after Deployment. Deploying ML/AI models into production presents unique challenges compared to traditional software deployment. Models need to be monitored post-deployment for performance decay over time due to changing data patterns. You'll need to work closely with engineers to plan for continuous monitoring, updates, and maintenance to ensure the model remains effective. Closely track Risks. Due to uncertainties in data, algorithms, and experiment outcomes, ML/AI projects involve more risk and variability. Managing these risks requires an adaptive and proactive risk management approach, including contingency plans and continuous reassessment of project viability. Stay Compliant with Regulations. AI projects must often comply with regulations regarding data privacy, especially when using sensitive or personal data (e.g., GDPR). As a project manager, ensuring the project complies with these legal requirements and that the AI models are transparent and explainable is important. In summary, in ML/AI project management, the project manager must be adaptable, comfortable with uncertainty, and knowledgeable about data science and machine learning processes. The role involves managing timelines and resources, ensuring data quality, fostering cross-disciplinary collaboration, and addressing ethical concerns. It's a more complex, iterative process than traditional project management, requiring a strategic balance between experimentation, resource management, and business alignment.  According to the U.S. Bureau of Labor Statistics, the project management position is predicted to grow 7% until 2033, with an average yearly salary of $98,580 in 2023 (around $47/hour). However, the actual pay could vary across industries and companies. This month, our analysis will focus on AI/ML-related project management, a field that is currently in high demand. We will analyze the job requirements, skills, and qualifications, highlighting the value of your expertise. Let's look at an example job opening: Netflix: Product Manager, ML Platform: Training (10/14/2024) Netflix offers entertainment services with 278 million paid memberships in over 190 countries. It is one of the big five tech companies called "FAANG." These companies are known for high pay and extensive company benefits. The position is part of a team building the Machine Learning Platform (MLP) to improve researchers' and engineers' productivity.
Netflix: Principal Product Manager, Data Platfor -Analytics Platform (10/18/2024) Netflix seeks a Principal Technical Product Manager to lead its Data Platform, focusing on optimizing data utility across the company by managing analytics platforms and driving technological innovations. This role demands a seasoned professional to develop strategic visions customer use cases to engineering features, foster cross-functional collaborations, and spearhead product roadmaps, all aimed at enhancing both the technical and non-technical user experience. Key Skills and Experience Required:
Meta: GenAI Project Manager, Product Data Operations (10/21/2024) Meta, formerly known as Facebook, is a global technology powerhouse renowned for connecting billions worldwide through platforms like Facebook, Instagram, WhatsApp, and Oculus. The company's culture emphasizes rapid innovation, open communication, and initiative, encouraging employees to lead projects and innovate. The Project Manager position recently posted is their Product Data Operations team to advance GenAI programs. The job requires strong leadership and the ability to manage complex, technology-driven projects.
Editor's Notes: You may use the following link to find the AI/ML jobs on Linkedin:  Artificial Intelligence (AI) has impacted many areas of work and daily life, and project management is no exception. AI will change how we manage projects. Let's delve into this topic today.
What does it mean to apply AI to project management? AI can take over the administrative tasks in project management, such as planning, tracking, and reporting, allowing project managers to focus more on communication, influence, and leadership. This includes selecting projects for mobilization, generating project plans, prioritizing tasks, allocating resources, resolving dependency issues, tracking project status, and forecasting potential risks. AI is attractive because it is faster, can assess more data points, and stays more rational than humans to prevent biases and cognitive limitations. With AI, project managers have more time to build relationships with stakeholders, manage more projects, and perform strategic tasks! Can you be specific on the areas in which AI should be applied? Project managers can use AI to augment skills and boost efficiency and strategic decision-making. These areas include:
Are we there yet? How far? AI in project management is still in its early stages. While ChatGPT can generate a project plan with a comprehensive list of tasks and assign them to the right team, it needs more intelligence analysis to ensure project operation success. This includes dependency analysis, risk analysis, and proper task assessment. However, using ChatGPT to generate project communications, plan meeting agendas, and summarize meeting minutes in real time is ready for prime time. For example, Grammarly can be used to check grammar and spelling errors and make word choices. The service is reliable and produces consistent quality results. How can we adopt AI in our organization? This is the most critical question to answer right now. We must adopt AI immediately to stay ahead of the curve and remain competitive. The challenge is that there needs to be ready-to-use ML mode and data to start training AI. The most critical missing piece is data. High-quality data would enable AI to learn in the right direction. To adopt AI in project management, we must take the following actions:
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