What do you do if you're a machine learning professional torn between innovation and practicality?

Balancing innovation and practicality is a common challenge for machine learning professionals. Here are some strategies to help navigate this dilemma: ?Understand the problem domain. ?Start with the basics. ?Evaluate risks and benefits. ?Iterate and experiment. ?Consider the trade-offs. ?Engage with stakeholders. ?Stay informed. ?Seek feedback and validation. ?Focus on the problem. By adopting a balanced approach that considers both innovation and practicality, you can maximize the effectiveness and impact of your machine learning efforts while mitigating risks and ensuring alignment with organizational goals.

Integration of ML with other fields is the current focus. So innovative ideas (to use ML) and practical knowledge (of the field, it could be food, security, materials) are required. To fill the gap we need to generate experts in the line of ML for Materials, ML for Food, etc. ML became a basic subject like any other basic subject "Math"

When torn between innovation and practicality in machine learning, ?? first evaluate the problem's context and urgency. ?? Aim for innovative solutions that address long-term needs, while balancing with practical approaches to deliver immediate value. Collaborate with diverse perspectives to merge creativity with feasibility, ensuring impactful results. Stay adaptable, navigating the dynamic landscape with a blend of visionary thinking and grounded execution. ??? Ultimately, seek harmony between innovation and practicality to drive sustainable progress in ML.

Analyzing and grasping the problem statement holds paramount importance for both Data Scientists/ML engineers. Frequently, a straightforward SQL query can yield remarkable results that even a complex, parameter-heavy model may struggle to achieve. The approach to addressing a problem should prioritize outcomes over techniques, ensuring that the solution aligns closely with the desired results. Simpler solutions are not only cheaper—no need for fancy GPUs or unnecessary computational power—but they also spare you those awkward "Why did we spend so much on this?" moments. However, in startup funding, sometimes being a bit sneaky and complicated can pay off. Just remember, it's all about balance, like juggling flaming torches on a unicycle.

Striking a balance between innovation and practicality in machine learning requires a nuanced approach. Professionals in this field must stay informed about the latest developments while also assessing the potential impact of new techniques. It's essential to evaluate the risks associated with innovation and start small, experimenting with new ideas on less critical projects before integrating them into larger systems. Collaboration with colleagues who offer diverse perspectives can provide valuable insights, and an iterative approach allows for continuous refinement based on real-world feedback. Flexibility is key, as the optimal balance may evolve over time with changing technologies and project requirements.

For me, the minimum phases necessary for an ML project are the following: 1. EDA (Data Extraction, Cleaning and Analysis) 2. Data Preparation. 3. ML model development 4. Retraining 5. Review 6. Deployment 7. Testing 8. Monitoring I have had to cancel more than one contract with several companies because they understood that this was not necessary. That data was purchased, an already existing ML model was taken, everything was mixed, and off it went. That is intolerable on both a personal and professional level.

Análise rigorosa: Examine as necessidades do projeto com lupa, definindo prioridades e pontos de controle com base em dados concretos e análises imparciais. Foco no objetivo: Mantenha o objetivo principal sempre em mente, utilizando-o como bússola para guiar suas decis?es e a??es. Flexibilidade: Adapte-se às mudan?as e desafios inesperados, utilizando os pontos de controle como referência para ajustar o curso do projeto quando necessário. Aprendizado contínuo: Aprimore suas habilidades e conhecimentos continuamente, buscando sempre as melhores práticas para análise e aprendizado de projetos.

To set priorities between innovation ?? and practicality: 1. Align with organizational goals ?? and stakeholder ?? needs. 2. Evaluate the potential impact and feasibility of innovative solutions??. 3. Consider resource ??????????availability, including time ??? and budget ?? constraints. 4. Prioritize projects based on ROI??, scalability, and long-term benefits.

Die Datengrundlage muss excellent und gut verstanden sein. Es muss eine Data Platform existieren mit einem Sicherheits und Verantwortungskonzept. Die Daten Aufbereitung sehe ich auch nicht als Hauptaufgabe des ML Teams, das Feature Engineering allerdings schon.

Once you've assessed your needs, it's crucial to define the priorities of your machine learning project. Identify which aspects are unavoidable and which allow for creative exploration. This may mean prioritizing accuracy and reliability over computational speed, or vice versa, depending on the project objectives. By discerning where to innovate and where to apply proven methods, you'll optimize the allocation of your efforts and resources.

Choosing battles wisely - is often overlooked. An excellent example of this is nearly all hyper-scalers (AWS, Microsoft, Oracle) did not go (crazy) headfirst into developing their own LLMs or foundation models. Their innovations (Bedrock, CoPilot, OCI-GenAI) mostly centered around making core ML innovation more accessible to people who wanted to build solutions on top of it. And where possible, they have made strategic investments in AI companies doing this core innovation. As ML folk, it's ok to use any of the available tools to achieve your practical objective. First-principles-based decision-making is not a prerequisite for all ML projects. However, recognizing high-yield potential from a possible innovation shouldn't be ignored.

To innovate smartly between innovation ?? and practicality: 1. Identify key problem areas or opportunities ?? for improvement. 2. Leverage existing technologies ?? and frameworks where possible. 3. Test and validate innovative ideas ?? through pilot projects. 4. Collaborate with cross-functional teams ?? to gather diverse insights??. 5. Monitor and measure the impact of innovations to inform future decisions??.

Em resumo, um profissional de Growth Hacker no mundo do aprendizado de máquina atua como um elo crucial entre a inova??o e a praticidade, traduzindo ideias complexas em solu??es tangíveis que geram resultados concretos. Ao combinar habilidades técnicas com uma vis?o estratégica e foco no crescimento, o Growth Hacker contribui significativamente para o sucesso de empresas e projetos que apostam no aprendizado de máquina como ferramenta de transforma??o.

Intelligent innovation in machine learning often involves incremental improvements rather than radical inventions. Applying advanced research in an optimal way without overloading the project is crucial. This approach ensures that new discoveries are integrated in a practical way, enriching the field while respecting deadlines and the quality of deliverables. It balances technical progress with operational needs, the key to long-term success.

Testing is absolutely crucial for any AI model. For AI scientists, ensuring that a model remains unbiased towards any particular society is paramount. Take Google, for instance. Back in 2015, their model, Google len, made quite the blunder. It tagged images of black people as "gorillas," landing them in hot water. A little more testing could have saved them from that embarrassment and backlash. Bias can creep in from various sources—maybe the developer was unknowingly biased, or perhaps they were just optimizing for the majority without considering the consequences. It's like trying to fix a leaky faucet and accidentally flooding the whole house. So, let's remember to test rigorously and avoid those unexpected surprises.

Testing approach followed by software applications isn’t nearly the same as the ML applications. In the software applications one might expect to test for latency, performance and overall usage of the application. In ML application, one has to think more creatively. There’s a bunch of variables to consider- 1. Real world data ( varying inputs) 2. Data related issues ( Skewed, Biased, etc) 3. Model performance (does the model still perform consistent for the data seen before? Are there new prediction biases?) 4. Application performance (does the model take long for one type of data vs the other?) 5. Integration test ( how does the overall pipeline work with all the components?)

Deadlines come fast and nobody is impressed by a novel algorithm, that wasn't finished. With automated tests and a CI/CD pipeline, you can make sure, that you produce a viable product, that always satisfies the customer. In the remaining time, you can innovate your product, knowing that there is always the working model ready to deploy.

Ohne MLOps geht nichts. Dazugeh?ren natürlich auch Monitoring, Testing, Deployment, Documentation und Finance Control. Gestet werden sollte viel und h?ufig.

Integrating innovations requires rigorous testing to ensure their effectiveness. This includes verifying accuracy, performance, robustness and scalability. It's essential to use a variety of datasets and scenarios to test your solutions and be ready to fine-tune them. Testing is a crucial stage where practicality comes first, ensuring that innovations stand up to real-world applications. This strikes a balance between technical advancement and operational reliability.

It is necessary to have clear communication with your team and stakeholders: how this idea is connected to the purpose and what the benefits are. Additionally, it is important to be explicit about how you test and apply the experiment and will not affect machine learning models that are already working.

It's essential to explain both the potentials and constraints of your approach to stakeholders who might not be well-versed in ML. By fostering clear communication, you create a space where innovative ideas are valued alongside their practical applications.

In my opinion, innovative or traditional, communication is key in either case. Innovation doesn’t always mean a longer and complex approach. It could also mean arriving at the solution in a much faster and out-of-the-box way when compared to traditional methods. With the existence of Gen AI at everyone’s disposal it’s easier than difficult for brainstorming ideas to be innovative where traditional ways fail Keeping that in mind, providing regular updates on comparing and contrasting between traditional and innovative methods provides better clarity and context to stakeholders. In my opinion, the most essential value is to be able to translate technical verbiage to “How does this implementation technique help business?”

Clearly communicate the rationale behind your decisions to stakeholders, highlighting the trade-offs between innovation and practicality. By fostering open dialogue and managing expectations, you can ensure that everyone is aligned on the approach being pursued. Ultimately, finding the right balance between innovation and practicality requires a combination of critical thinking, creativity, and adaptability. By approaching each situation with an open mind and considering the specific context and constraints involved, you can make informed decisions that drive both progress and impact.

Clear communication is essential in managing the trade-off between innovation and practicality. Articulating the benefits and limitations of your approach to stakeholders, who may lack in-depth knowledge of machine learning, is crucial. Effective communication helps manage expectations and fosters an environment where innovative ideas are valued in a practical context, ensuring better understanding and adoption of innovations.

*Innovate wisely, combine between operational excellence and customer experience to gain a perfect balance. *Accept the fact that not all new trends will provide benefits to your organization, so evaluate innovations in a smart way. *Keep an eye on new trends especially for organizations within same industrial field.

Beyond the listed points, it's crucial to foster a culture of adaptability, where innovation and practicality harmonize. Embrace diversity of thought and encourage interdisciplinary collaboration. Stay abreast of emerging technologies while understanding the value of tried-and-tested methods. Prioritize user feedback and iterate accordingly. Foster transparent communication channels to align innovation with business goals effectively. Lastly, maintain a growth mindset, continuously seeking opportunities for improvement and learning from both successes and failures.

Try Out Small Ideas First, begin by testing your new ideas on a small scale before using a lot of resources. This can show if an idea might work well without taking big risks right away.

Sometimes, some things are just not feasible. But it could also just be the case that you are in the wrong environment. I've personally found that academica was not right for me and my interests. This sentiment was unfortunately shared by some researchers I've spoken to. Unfortunately, money is also a consideration. Meaningful research takes a long time, and that also means a lot of money. PhDs are a great way to really dig into something you are passionate about, but for me I've found that private research and startups were the better environment.

AI is like a double-edged sword, both helpful and perilous. Nowadays, many models are picking up biases faster than a kid learns to mimic their parents. They soak up all sorts of human biases when trained on anonymous or society-skewed datasets. Take Reddit, for instance. Train your model there, and you're basically feeding it a diet of American perspectives with a side of toxicity, thanks to all that anonymous venting. It's like teaching your pet parrot to swear—once it learns, good luck undoing that. So, when it comes to AI, let's remember to be responsible parents and not pass on our bad habits. Training a Model with Bias Mitigation is Important if there are any protected classes.