How can you align your IT investments with your machine learning strategy?

To do this, you must first identify your business objectives before assessing the accessibility, quality, and governance of your data. Make sure you have the necessary expertise and abilities, assess whether your IT infrastructure can support ML workloads, and think about cloud solutions. Evaluate how ML will abide by rules, maintain strong security, and work with current systems. Establish monitoring systems, begin with tiny test initiatives, and perform a cost-benefit analysis. To efficiently adapt to new machine learning technologies, plan for change management and cultivate a culture of continuous improvement.

Demonstrate the business value of machine learning. Select the processes and operations that can benefit from machine learning. Prioritize the areas with high potential for improvement, such as customer service, marketing, product development, or risk management. -Examine the machine learning technologies that are available and suitable for your needs. Consider the data sources, algorithms, platforms, and tools you can use to implement machine-learning solutions. - Analyze the impact of machine learning on your current and future business models. - Communicate the benefits and impact of machine learning to your stakeholders, customers, and partners. Monitor and report your machine learning projects' KPIs and ROI.

Aligning your IT investments with your ML strategy is a critical task that demands focused attention. While there are various approaches to a comprehensive initial assessment, it's crucial to emphasize the importance of ongoing alignment as well. In my view, the best way to achieve this is by establishing a 'control tower'. This would be an enhanced version of the traditional Project Management Office (PMO). In addition to managing timelines, deliverablesand budget, this control tower would also monitor two additional elemts: the AI/ML heatmap of the organization (a list of required capabilities and the status of their acquisition) and an AI impact/risk registry.

I'm glad to see data capabilities being emphasized here. Since AI became a buzzword once again, I have seen a lot of people ignoring data literacy or data in general as a foundational need.

Think of Machine Learning as only part of a digital transformation strategy. Start with aligning process and people that will be effected. Build some models to see if they can analyze and use the data. Also, decide whether AI should drive ML strategy or vise versa. Most of these decision will fall out of analyzing how your company uses data analytics today and weighing the improvement impact ML will have.

I believe that every company or individual working with data should engage in Machine Learning. While it's possible to work with unstructured data, consider walking into an office where the books are disorganized versus arriving in a well-organized library. Similarly, when deciding to invest in ML, having a culture that values structured data makes the challenge less formidable. In my opinion, the second question revolves around KPIs. Have you defined and are you actively tracking your weekly KPIs? After addressing these questions, it becomes possible to establish a process for ML investment. This process should always be aligned with your organizational culture, and the decision should involve people who are committed to its success

As most things it starts with why, also known as strategy. What are we going to achieve? Then you need to include who, the people that are going to do it. Do you have the required expertise? Moving on to how, the processes needed. What processes will the people need to execute on the strategy? Finally, look at data, technologies and platform - also known as with what. What data and quality is needed, what platforms and technology is needed? Circling back to who, what competences do we need for pot. new tech? Last but not least, you need to establish a culture of change, evaluation and fail fast because you will fail. But it's important to reflect, change and execute fast.

Antes de saber se você (sua organização) de fato está pronta para o processo de ML, é fundamental que se tenha definido o planejamento estratégico e mapeado os processos e recursos disponíveis de forma para assegurar a correta identificação do gargalo a ser superado, ou seja primeiro o problema e depois a busca pela solução. Não adianta se preparar para o passado!

In short: it's not only about technology. A successful machine learning project is a delicate balance between technological innovation and effective change management. Recognizing and addressing the human element is as crucial as selecting the right algorithms. By fostering a culture of openness, investing in skill development, and strategically managing change, organizations can ensure that their machine-learning projects deliver technological advancements and drive positive and sustainable transformations across the enterprise. In the dynamic world of machine learning, managing change is not just a part of the process—it's the key to unlocking the full potential of AI technologies.

Sometimes it helps to bring in experts in this field to do a quick readiness assessment for you. This should include your cognitive infrastructure along with your standard IT/OT network. Any gap resolutions should be included in your implementation budget as these hidden costs usually fall on Facilities and IT, which seldom have the extra room for advanced ML projects.

The AI team should partner with existing data management and IT infrastructure teams to fully leverage and extend data platforms and cloud services already in place. This can save significant time and money versus building new greenfield infrastructure for your AI services. Time to value is critical, so don’t be tempted to reinvent/rebuild core capabilities that already exist :)

Existen soluciones integradas en aplicaciones sectoriales como Industria 4.0 que tienen una capacidad de usar sus propios algoritmos o integrar soluciones de terceros. Simplifica la adopcion de ML dentro de ciertos sectores sin necesidad de desarrollar soluciones desde muy abajo.

Die Plattform hängt natürlich vom Einsatz ab. Eine Verbesserung der Marketingunterlagen kann gut in der Cloud passieren. Ein KI System, dass das Kmow How der Spezialisten „lernen“ soll, muss on premise und sehr gut geschützt sein.

Interesting point of View. Besides what was shared, it's very important thst every ML initiative be aligned with the business outcome, a robust communication strategy and a executive sponsorship. It's s better strategy to set that is a business journey and IT is only one piece of that.

-Consider cloud-based ML services for ease of use and scalability. -Use ML frameworks and libraries for greater control and customization. -Explore hybrid approaches if needed.

Select an ML platform that aligns with your organization's needs and capabilities. Consider factors such as scalability, ease of use, integration with existing systems, and support for various ML algorithms and models.

Selecting the appropriate ML platform is a pivotal decision in shaping the success of your data-driven initiatives. A comprehensive evaluation of platforms should involve assessing scalability, flexibility, and compatibility with your existing IT infrastructure. Consider the platform's support for diverse ML algorithms, ease of integration, and the level of community and vendor support available. Robust security features and compliance capabilities should also be paramount. Ultimately, aligning the ML platform with your organization's specific needs and long-term goals ensures a solid foundation for successful implementation and sustainable growth.

Tailoring your approach to machine learning requires a thoughtful consideration of your readiness, objectives, and requirements. Opting for ML platforms offers a spectrum of choices, each varying in automation, flexibility, and scalability. Cloud-based ML services furnish pre-built models, APIs, and task-specific tools, streamlining tasks like image recognition and natural language processing. On the other hand, leveraging ML frameworks such as TensorFlow, PyTorch, or Scikit-learn empowers you to construct and personalize your models. A hybrid strategy, blending cloud and on-premise solutions, adds another layer of adaptability to suit your unique needs in the ever-evolving landscape of machine learning.

Bevor Sie sich für eine Machine-Learning Platform entscheiden ist abzuklären, welche Ziele, welche Anforderungen und welche Skills in Zukunft für diese Plattform innerhalb Ihrer Organisation massgeblich sind. Zudem ist es sicher von Nutzen, wenn die "vorgestaffelten" Basiskenntnisse der Domänen Cloud-Servicemanagement, Enterprise Big Data und AI bei den später organisationsrelevanten Rollenträgern schon etabliert sind. Stellen Sie also zuerst sicher, dass die notwendigen Skills innerhalb der Organisation etabliert sind, bevor Sie sich zu Plattformen entscheiden, die dann später von den relevanten und ausgebildeten Stakeholdern genutzt werden.

IT infrastructure and investments play a crucial role in enabling and supporting machine learning projects. Machine learning models often require substantial computational resources, including powerful processors, fast storage, and sufficient memory. Ignoring IT investments could result in inadequate infrastructure, leading to poor performance and scalability issues. Recommandation: Working closely with your IT department to ensure that your infrastructure can support the computational demands of your machine learning workloads. Consider investing in cloud-based resources or dedicated on-premises hardware to meet your specific requirements.

Ensure that your IT infrastructure and architecture can support ML initiatives. This involves evaluating data storage, processing capabilities, network infrastructure, and security measures required for handling ML workloads efficiently.

MLOPs methodologies are vital for ensuring that ML systems are developed and deployed effectively. So, it’s crucial to set up a strong ML infrastructure capable of supporting continuous delivery and integration. Therefore, to identify which components are necessary for a particular workload, ML architects must also consider goals related to ML workload outcomes and business priorities such as Performance, scalability, ML life cycle duration, cost, security and compliance.

This step cannot be over looked. Without proper preparation of your data, networks, end user devices etc. Oh and let’s not forget security. Without proper preparation any dive into ML against your data will be meat with frustration. This can be especially tough for legacy systems and their stakeholders. At some point the pain of not moving must be overcome by the benefits. Taking the time or properly align resources can mitigate the pain of moving. Lastly let’s not forget we also have to align our most valuable asset the people who do the work, think the ideas, and execute the plans. IT is a people just as much as it’s a 1 and 0 business.

Aligning IT architecture and operations for ML success involves integrating data flows seamlessly. Ensure scalable infrastructure to handle ML workloads and prioritize interoperability between systems. Implement efficient data pipelines for timely access. Foster collaboration between data scientists and IT teams, ensuring a shared understanding of goals. Leverage cloud services for flexibility and scalability. Establish robust governance to manage data security and compliance. Regularly assess and update the IT architecture to accommodate evolving ML requirements. This alignment ensures a cohesive approach to support ML initiatives within the broader IT framework.

Aligning IT investments with a machine learning (ML) strategy is imperative for effective integration. Define business objectives aligned with ML applications and invest in a resilient IT infrastructure capable of handling computational demands. Seamless integration with existing systems, prioritizing data governance, and ensuring data quality lay the groundwork for successful ML implementation. Develop internal expertise through training, fortify cybersecurity measures, and embrace scalable solutions. Continuous monitoring tools facilitate ongoing optimization, while regular assessments of ROI and impact guide informed adjustments. This strategic alignment ensures that IT investments harmonize with evolving ML strategies.

Make sure your computer stuff works well with machine learning. Keep data safe and organized. Use smart tools to make ML development and deployment faster and smoother. It's like making everything fit together nicely for better results!

In the ML era, aligning IT is crucial. Data Integration, Security, Reliability: Integrate data sources, secure data with encryption, and maintain reliability through regular quality checks. Resource Optimization: Employ scalability, specialized hardware, and cost-effective storage. DevOps, Containers, Microservices: Adopt DevOps for streamlined development, use containers for efficient deployment, and implement microservices for flexibility. Aligning IT with ML requirements is essential for success, unlocking the full potential of data-driven initiatives.

With the aforementioned already executed, one must consider the information storage platforms to be available, the backups and capacity to be available, and the network resources necessary to be able to interact with solvency and online connectivity.

Asegurar que los siguientes puntos sean considerados en la definición del lineamiento de la operación y su arquitectura - Definición y levantamiento de las distintas fuentes de datos, almacenamiento y flujos de procesos presentes. - Optimizar los recursos de red disponibles pensando en el flujo de datos y cargas de trabajo futuros - Planificar las tareas que se puedan ejecutar con el uso de microservicios y contenedores - Establecer políticas de industria para DevOps, sobretodo automatizaciones en integraciones CI/CD - Definir en el acceso a los datos las distintas políticas de privacidad y cumplimiento

It is crucial not to expect miraculous results from AI/ML at the outset. Avoid squandering time by entrusting your data to AI companies in the hope of something "magical." Instead, define your business goals and expectations, and commence with small steps. You don't necessarily need a large team of AI experts to begin, but rather the right individual. Furthermore, it is essential for your AI expert to have a comprehensive understanding of the business, its objectives, and expectations. Unless you intend to utilize a substantial amount of data for training, consider exploring existing tools as the most cost-effective approach to learning how AI can benefit your organization.

AI roles are very different from traditional IT. Cross functional is a good way to go if you have the resources, expertise in data science, business, process improvement and leadership help here. Effective risk management is also a key competency that is essential for ethical AI.

We are at a stage where two contradictions must be resolved before we position AI as a decision-making machine. - We need to define the responsibility for the wrong decisions of the AI system, which is very unclear at the moment. - Educating and informing users about how to use AI solutions, advantages, possibilities, and limitations. Civilizations developed over centuries by handing down knowledge between generations, learning from mistakes, and practical experience. AI shortens this process of knowledge transfer, which is useful. Still, this speed is a double-edged sword if we do not solve the problem of testing new knowledge with a larger population of people and not just with individual groups.

As a starting point, like every innovation initiative, start from a business goal, and as you start experimenting you should define the other pillars of implementation, e.g R&R, process and more. It's important to understand that AI/ML solutions often impact operational processes in the systems, and understanding the eco-sytem of the process, implementation, controls and feedback loops is key to successful implementation

Indeed, investi g un human capital is vital. Though, machine lesrning models often need to scale as the volume of data and the complexity of models grow. Ignoring IT investments and human resources can hinder your ability to scale and optimize the performance of your machine learning solutions. Recommendation: Invest in scalable IT infrastructure and technologies, as well as continuous and life long education and learning that can accommodate the growing demands of your machine learning workloads. This may involve leveraging distributed AI, computing frameworks, high-performance storage solutions, and parallel processing capabilities.

Die Rollen die sich später mit dem beschäftigen, was man durch maschinelles Lernen unterstützen will, sollten sich vor allem mit folgenden Inhalten auseinandergesetzt haben: - Enterprise Big Data Architektur - Enterprise Big Data und AI Use Cases - Lösungsarchitekturen für Machine Learning - Grundlagen der Datenanalyse, Auswertung und Datenpräsentation - Data Science, Data Analytics - Mustererkennung Bauen Sie sich zuerst ein Rollenkonzept, damit Sie wissen, für welche Rollen welche Qualifikationen aber auch welche grundlegenden Talente benötigt werden.

Desde luego los nuevos perfiles tienen que tener una forma de pensar diferente, no es hacer lo mismo con una tecnologia diferente, sino transformar lo que hacemos y extraer todo el potencial de este tipo de tecnologias

Invest in developing a skilled team capable of working with ML technologies. Offer training programs, hire experts, encourage a culture of innovation and experimentation, and create cross-functional teams to bridge the gap between IT and data science.

Teach your computer team about cool machine learning stuff. Make a friendly environment for learning and sharing. Team up with different experts for better ML projects. It's about learning and working together for success!

The selection and training of resources for professional, expert, collaborative, united work is essential, under an awareness of clear, simple, pragmatic, efficient, and safe procedures, with sufficient quality tests (fast but bad, it is twice bad ).

Machine Learning and AI are not cheap initiatives. As with all investments, a firm grasp of the costs, both in personnel and compute costs is critical to understanding whether the investment in Machine Learning and AI are right for you. In a world of flashy new technologies, the advice to start small and build capacity and understanding of cost impacts is essential.

Developing indicators for correct constant, online, automatic monitoring, and its constant evaluation, is the key that will allow tuning processes, adequate infrastructure, costs and compliance with business cases, learning, results and adjustments of evolutionary and corrective developments. . A virtuous loop of lessons learned, self-correction, value and total quality.

La mise en place des KPI est nécessaire pour prendre les bonnes décisions et agir selon des normes bien établies. Cela est nécessaire pour ajuster, agir en se basant sur des métriques.

In my experience monitor and evaluate your ML outcomes and adjust your IT investments accordingly. You need to track and measure your ML performance, impact, and value using relevant indicators and metrics, such as accuracy, precision, recall, ROI, or customer satisfaction….

Implement robust monitoring and evaluation mechanisms to track the performance of ML models and initiatives. Continuously assess the impact of ML on business outcomes and adjust strategies accordingly.

Once an ML model is deployed, its performance must be monitored continuously. That's because model performance can degrade over time, which needs to be detected and addressed promptly. Models can degrade due to a number of issues, including changes in customer behavior or problems with the data also known as concept drift and data. In fact , any performance degradation needs to be caught early on and addressed. The corrective actions can include retraining models, fixing data quality issues, revamping models, and auditing upstream systems. You may need special tooling to store the model predictions so that they can later be used for diagnosis and quality analysis. 

In the pharmaceutical sector, it's important to monitor and evaluate ML outcomes, focusing on metrics like accuracy and ROI. This involves gathering feedback from industry-specific stakeholders to refine ML models and align IT resources with pharma's unique demands and compliance standards.

Como todo processo, tem-se de estabelecer procedimentos e metas (KPI), de forma a possibilitar o aprendizado organizacional (que vai impactar a cultura) e, através da análise crítica do desempenho da ML (planejado x realizado) iremos estabelecer meios de evolução.

Monitoring ML outcomes is difficult. The results of ML use is often not seen directly but on the mid- to longterm scale. Though IT investments and especially ML are quite expensive it is imperative to transform a company and keep it competetive for the future.

Al delegar muchas de las decisiones de negocio a soluciones de ML es clave que se verifiquen que las opciones y resultados tienen sentido y se alinean con las expectativas y objetivos de negocio que se han fijado previamente.

To align IT investments with a machine learning strategy: 1. Define Goals: Identify specific objectives for machine learning. 2. Infrastructure Assessment: Ensure your current IT supports ML (processing power, storage). 3. Scalable Tech: Invest in scalable solutions, like cloud services. 4. Data Management: Prioritize efficient data collection and processing systems. 5. Talent Development:Train staff in ML or hire experts. 6. Security:Implement robust data security. 7. Tool Selection: Choose ML tools that match your IT environment and goals. 8. Start Small, Scale Up: Begin with pilot projects, then expand. 9. Continuous Monitoring: Regularly review performance and adapt as needed.

A lot are talking about technology and operation this is very important for the CIO to be a business enabler. I suggest to map the business needs and strategy and see which one of them can be better answered with a ML / AI etc. If you are not sure try looking for trends and use case in your industry. What was successful and who.

6-Iterative Approach: Adopt an iterative approach to machine learning projects. Learn from each iteration, gather feedback, and adjust IT investments accordingly to better align with evolving business needs.

Beyond these steps, consider factors like data governance and compliance, building partnerships with vendors or experts, establishing clear communication channels between IT and business stakeholders, and fostering a culture of continuous improvement and learning.

Aligning IT investments with your Machine Learning (ML) strategy involves strategically connecting technology decisions with overall business goals. It's crucial to ensure that your IT infrastructure is scalable, capable of handling diverse data requirements, and aligned with the skills needed for ML implementation. Security and compliance should be at the forefront, and fostering collaboration between IT and data science teams is essential. Choosing interoperable technologies and establishing feedback loops for continuous improvement are key considerations. Ultimately, a careful cost-benefit analysis should guide IT investments, ensuring they contribute directly to the success of your ML initiatives.

Absolutely, those steps are a great breakdown of how to align IT investments with a machine learning (ML) strategy. Here are some additional considerations to ensure a comprehensive alignment. - Data Accessibility and Integration. - Ethical and Responsible AI Practices. - Scalability and Flexibility. - Collaboration and Interdepartmental Communication. - Feedback Loops and Iterative Improvement. - Risk Management and Contingency Plans. - Regulatory Compliance and Legal Considerations. By considering these additional aspects, you can further strengthen the alignment between IT investments and your machine learning strategy, ensuring a more comprehensive and effective implementation of ML initiatives within your organization.

ML readiness is not about technology alone. Some of the reasons many ML project struggle to succedd are due to lake of an holistic view and planning: 1. If you don't strat from a business case, but building an AI/ML model for the sake of building it 2. E2E view: A CIO's perspective should always take into account the end-to-end business process. Machine learning models are not standalone solutions; they often require a rule engine, operational system/site effort, and leadership and maturity from business owners. 3. AI/ML model results can be confusing, leading users to lose trust and abandon the model. Project effort must consider & plan that too 4. Data quality issues

Security and Compliance: Machine learning systems handle sensitive data, making security and compliance critical. Neglecting IT investments in security measures can expose your machine learning solutions to potential vulnerabilities and regulatory risks. Recommendation: Prioritize IT investments in cybersecurity measures, such as access controls, encryption, and monitoring tools, to safeguard your machine learning infrastructure and data. Ensure compliance with relevant regulations, such as GDPR, HIPAA, or industry-specific standards.

What else to consider is stop asking bloody consultant questions. If ML is not helping you naturally ( with some normal experts ) just consider it might not be the best yet for you.

Sie sollten natürlich das "Hammer-Nagel" Problem vermeiden sonst landen sie in der gleichen Falle wie damals bei Einführung der "Fuzzy-Logik", wo es selbst Reissiebe gab, auf denen das Buzzword stand. ML- und KI-Begeisterung ist gut, um schnell Ideen im Unternehmen zu generieren. Hackathons sind dafür ein bewährtes Mittel. Danach fängt die anstrengende Arbeit des Aussiebens an, damit aus vielversprechenden Ideen auch Innovation wird. Dazu können sie auch ML-Projektideen einfach mit Hilfe ihrer Investitionskriterien prüfen, denn was nützt die eleganteste Idee, wenn sich mit ihr kein Geld verdienen lässt?