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Your machine learning team is struggling to meet deadlines. How can you get them back on track?

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Machine learning projects can be complex and challenging, requiring a lot of coordination and collaboration among team members. However, sometimes things don't go as planned and deadlines start to slip. How can you get your machine learning team back on track and deliver quality results on time? Here are some tips to help you manage your team effectively and overcome common obstacles.

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Constantine Shulyak

Author of $100M+ social project | Featured on Forbes | CEO at BLCKMGC
Facundo Sislian

Sr Associate @ JPMorgan Chase | Machine Learning & Intelligent Automation

1 Identify the root cause

The first step to solving any problem is to understand what is causing it. Is your team facing technical difficulties, unclear requirements, communication gaps, or skill gaps? Are there any external factors, such as changing customer expectations, competing priorities, or resource constraints, that are affecting your team's performance? By diagnosing the root cause of the delays, you can address it more efficiently and prevent it from recurring.

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Constantine Shulyak

Author of $100M+ social project | Featured on Forbes | CEO at BLCKMGC
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In my view, identifying the root cause of missed deadlines is crucial for devising effective solutions and preventing future setbacks. It requires a thorough examination of various factors, including technical challenges, communication breakdowns, or external constraints. Once the underlying issues are pinpointed, tailored strategies can be implemented to address them. By proactively addressing the root cause, teams can regain momentum and enhance their productivity, ultimately ensuring the timely delivery of projects.

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2 Prioritize and scope

Once you know the cause of the problem, you can prioritize and scope your tasks accordingly. What are the most important and urgent features or goals that your team needs to deliver? What are the nice-to-have or optional ones that can be postponed or eliminated? How can you break down your tasks into smaller and manageable chunks that can be assigned and tracked easily? By prioritizing and scoping your work, you can focus on the essential and avoid scope creep or unnecessary complexity.

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Fernando Guedes

Analista de Marketplace | Business intelligence | Business analytics | Product Owner | Analista de negócios | Gest?o de Projetos | IA | NLP
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Realizar uma avalia??o honesta das demandas e recursos disponíveis para definir expectativas realistas. Em seguida, priorizar tarefas e definir metas alcan?áveis ajuda a concentrar esfor?os nos pontos mais críticos, além de promover uma comunica??o aberta e transparente dentro da equipe e como uma estratégia mais global: considerar a possibilidade de redefinir prazos ou realocar recursos conforme necessário

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3 Communicate and collaborate

Communication and collaboration are key to any successful machine learning project. You need to keep your team members informed and aligned on the project objectives, expectations, roles, and responsibilities. You also need to facilitate regular feedback and knowledge sharing among your team members, as well as with other stakeholders, such as customers, managers, or domain experts. By communicating and collaborating effectively, you can foster a culture of trust, transparency, and learning within your team.

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4 Automate and standardize

Machine learning projects often involve repetitive and tedious tasks, such as data collection, preprocessing, validation, testing, or deployment. These tasks can consume a lot of time and resources, and introduce human errors or inconsistencies. To save time and improve quality, you can automate and standardize these tasks using tools and frameworks, such as pipelines, scripts, APIs, or platforms. By automating and standardizing your workflows, you can increase your team's productivity and efficiency.

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Vishnu Krishna

Sr. Program Manager at Amazon| Product Manager | Cloud | AI/ML| Data Analytics
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One way to be more productive in ML projects is to utilize robust MLOps practices.To get your machine learning team back on track, automate and streamline your ML workflows. Implement CI/CD pipelines for machine learning models to enable faster iterations and deployments. Use version control for models and data to enhance collaboration and reproducibility. Monitor models in production to quickly identify and address performance issues. Adopt containerization to ensure consistency across environments. Finally, foster a culture of continuous learning and improvement within the team to stay updated with MLOps best practices.

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5 Learn and improve

Machine learning is a dynamic and evolving field, where new techniques, technologies, and best practices emerge constantly. To keep up with the latest developments and innovations, you need to encourage your team to learn and improve continuously. You can provide your team with opportunities and resources to acquire new skills, explore new ideas, or experiment with new approaches. You can also conduct regular retrospectives and reviews to identify what worked well and what can be improved in your team's processes and outcomes. By learning and improving constantly, you can enhance your team's capabilities and performance.

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Facundo Sislian

Sr Associate @ JPMorgan Chase | Machine Learning & Intelligent Automation
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One thing I’ve found helpful is to integrate learning into daily workflows rather than viewing it as a separate activity. For instance, incorporating short, focused training modules into regular meetings can make learning more immediate and relevant. Additionally, setting aside time for team members to share insights from recent projects can foster a culture of collective improvement and knowledge sharing.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Your machine learning team is struggling to meet deadlines. How can you get them back on track?

1

2

3

4

5

6

1 Identify the root cause

2 Prioritize and scope

3 Communicate and collaborate

4 Automate and standardize

5 Learn and improve

6 Here’s what else to consider

Machine Learning

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Your machine learning team is struggling to meet deadlines. How can you get them back on track?

1

2

3

4

5

6

1 Identify the root cause

2 Prioritize and scope

3 Communicate and collaborate

4 Automate and standardize

5 Learn and improve

6 Here’s what else to consider

Machine Learning

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