Lyndsay Noble’s Post

Are you Building your AI team? Check out the roles your enterprise needs (as suggested by the co-founders (S. Koushik Debroy & Ashish Singh) at TheCodeWork See, to be honest, Artificial intelligence (AI) isn’t just about frameworks — it’s also about people. As a business, you can have a successful AI journey only when you have executive leadership support and the right talent in key AI roles. Allow us to walk you through the key roles here: > Executive sponsor Enterprises that have successfully implemented AI have strong executive leadership support for the new technology. A C-suite sponsor can take an active role in ensuring that AI projects are aligned with the strategy of your company. > Systems architect Sometimes referred to as a cognitive solutions architect, the person in this role is responsible for operationalizing the entire suite of machine learning and deep learning models within the IT framework and systems at hand. > Data engineer Ensuring data quality means the data engineer role is vital to your AI success. They are responsible for defining and implementing the integration of data into the overall AI architecture. Skills include experience with data platforms including SQL and NoSQL databases. > Data scientist At its heart, data science is focused on exploring data to extract actionable information for making business decisions. The job calls for math aptitude and coding skills as well as critical thinking and problem-solving abilities. > DevOps engineer DevOps engineers work with architects, developers, data engineers, and data scientists to ensure solutions are rolled out and managed. They are professionals with experience in both application development and deployment. > Business analyst Business analysts use results from the data science models and act as “translators” between the business users and the machine learning team. 💡 Overview Bringing in experienced experts to work with employees on delivering the first use cases of AI is recommended. Reach out to us for a free consultation call on this - [https://lnkd.in/gutNhqyz] #ai #artificialintelligence #softwaredevelopment #devops

In order to scale Data & AI Product initiatives throughout the company, hiring more Data Product Managers won't be enough (or needed). You need a shift towards a Data & AI Product Mindset. Everyone is concerned, from the CEO to the Data, Product and Engineering teams. 👉 C-Level and Business people need to understand what can (and what cannot) be done with Data & AI, and align the Data Strategy with the Business Strategy. 👉 Product Teams need to understand how to work with Data Teams, and the specificities that are typically not encountered when working with Engineering Teams. They also need to upskill their technical knowledge : fundamentals of BI / dashboarding, SQL and Data Analysis become increasingly necessary. Using AI and GenAI in their day to day job is not the same thing as incorporating AI in their products. 👉 Engineering Teams, with the decentralization of Data, will have to incorporate more and more tasks and skills that used to be on the Data side : creating and orchestrating pipelines, managing data as a product, engaging with Data Contracts, and much more. 👉 Data Teams need to transition from a Project mindset to a Product Mindset. They need to stop considering that the algorithm is the only value in an AI Product. They need to be incorporated in Product Discovery phases, and develop more Software Engineering best practices. Tomorrow, every Product will leverage Data & AI in some form. It's spreading throughout the whole company, in every team. But it's only by adopting the right Mindset and adopting a common language that you will really benefit from it and address the right issues and create real value. What do you think ? Anne-Claire BASCHET Adrien Vercoutere Claire Lebarz

🚀 How To Build An AI Team From Scratch 🚀 Today, we're diving into the key technical roles that are essential for a successful AI team: Machine Learning Engineers, Data Scientists, and Data Engineers. First up, let's talk about Machine Learning Engineers. These experts are the architects of AI models. They design, build, and deploy machine learning models that power your AI applications. Their work involves a deep understanding of algorithms, programming, and software engineering principles. They are critical because they turn theoretical models into practical solutions that can be integrated into your products or services. Next, we have Data Scientists. Data Scientists are the detectives of your data. They analyze and interpret complex data to help your organization make data-driven decisions. Their skill set includes statistical analysis, data visualization, and domain expertise. Data Scientists work closely with Machine Learning Engineers to refine models and extract actionable insights from data, ensuring your AI initiatives are both innovative and effective. Finally, let's discuss Data Engineers. These professionals are the backbone of your data infrastructure. They design and manage the systems that collect, store, and process data at scale. Data Engineers ensure that data is accessible, reliable, and ready for analysis by Data Scientists and Machine Learning Engineers. Their role is crucial because a robust data pipeline is foundational for any AI project to succeed. By recruiting these three key roles early on, you establish a strong technical foundation for your AI team. Machine Learning Engineers, Data Scientists, and Data Engineers each bring unique skills and perspectives that are essential for the development and implementation of successful AI projects. So, what are your thoughts on these crucial roles? Have you started building your AI team yet? Let me know in the comments! Stay tuned for more tips on assembling an AI team, and don't forget to follow for more insights on creating a powerful AI-driven organization! #AI #ArtificialIntelligence #MachineLearning #DataScience #DataEngineering #TechLeadership #AITeam #AIstrategy #Innovation #TechCareers

My most unpopular hot take? Data teams that don’t create revenue will be gone in 2 years. The future of data and AI teams is customer-facing products. Platforms are taking over reporting and operational efficiency initiatives. I’ve seen two enterprise AI platforms supporting operational workflows in the last month. They crowd-sourced the data and built AI-supported features to address the most common use cases. The reporting and models work out of the box. No data scientists or data engineers are required. Enterprise AI platform companies have the same sales pitch, ‘CIOs. If you want to be seen as a strategic partner, help the business transform with technology platforms like ours.’ They’re telling line of business leaders, ‘You’ll be able to do more yourself without expensive data science talent.’ Enterprise AI companies justify the investment by reducing spending on data teams. We need to find new places to add value. The good news is that less time working on reporting and basic internal automation frees us up to deliver customer-facing, revenue-generating products. Data teams that thrive will establish themselves as the business’s primary growth driver by taking ownership of customer-facing products and focusing on innovation initiatives. AI strategists and product managers will be more critical than ever. They lay the foundation so data teams get buy-in for revenue-generating initiatives. Advocate for those roles if they don’t exist today so the data team avoids commoditization and reductions. Think about upskilling into one of these roles when you’re ready to advance. Follow me here or click the link under my name to learn more. #ProductManagement #AI #DataScience

“When everyone is looking for gold, it's a good time to be in the pick and shovel business.” (or selling jeans, like Levi-Strauss) Solid data infrastructure is table stakes for any company looking to innovate with AI. But most of our current data infrastructure wasn't built for these new AI demands. We've seen this play out before. In the early 2010s companies were scrambling to hire data scientists without realizing they lacked the data engineering foundation to support them. Now, history is repeating itself. Most companies’ current data infrastructure won't support their AI needs. In the same way that data warehouses, ETL pipelines, analytics tools and notebooks emerged to support BI and data science, we now need a new generation of tools tailored for AI workloads. Infrastructure that can handle: → Properly labeled data for training & fine-tuning → Data infra that can handle structured AND unstructured data → Real-time processing at massive scale → Robust testing, monitoring and compliance of AI systems This creates a meaningful opportunity for companies building the next generation of data infrastructure tailored for AI. Not only an opportunity—it's a necessity. And it's why I've doubled down on investing in engineer-founders tackling these challenges. For a deeper dive, check out my latest VentureBeat article and Bessemer's recent piece on AI infrastructure (👀 HoneyHive, Continual, Dagster Labs, Lightning AI, StarTree, Voltron Data, Decodable, Watchful, MotherDuck, Hightouch, Hex, Great Expectations, Anomalo, Acryl Data, Modal, Soda), both linked in comments. But don’t worry, it will probably be outdated by the time you finish reading this. That's how fast AI is moving.

As many organizations are still looking to take the step of cloud migration, it is best for leadership to be thinking many steps ahead. Your two most valuable assets are your customers' trust, and your data. AI is here to stay, be smart, be safe, and be strategic, not reactionary.

The Role of Data Engineers in AI Data engineers are responsible for designing, building, and maintaining the infrastructure and tools that allow data to flow seamlessly and be used effectively. Here's a deeper look into their responsibilities: 1. Building Data Pipelines Data engineers create the pipelines that allow raw data to be transformed, stored, and processed into a format that AI systems can work with. This involves a deep understanding of databases, cloud storage, and ETL (Extract, Transform, Load) processes. Example: If you're using a music recommendation system, data engineers ensure that user activity, song preferences, and other relevant data are efficiently processed and passed on to AI systems. 2. Data Ingestion and Integration Data is coming from various sources—web logs, customer interactions, IoT devices, and more—and needs to be integrated into a unified system. Data engineers ingest data from these sources, making sure it’s available in the right format and is easy to analyze. Example: Data engineers collect data from mobile apps, websites, and external APIs to ensure that the recommendation system has a comprehensive dataset to work from. 3. Data Cleaning and Preprocessing Data isn't always in a pristine state—there can be missing values, outliers, duplicates, or irrelevant information. Data engineers clean and preprocess this data, making it ready for AI engineers to use in building models. Example: If you're building an AI system for predicting customer behavior, the data needs to be cleaned to ensure that incomplete or erroneous data doesn’t skew predictions. 4. Scalability and Performance Optimization AI models often need to process large datasets quickly and efficiently. Data engineers ensure the infrastructure can handle the volume, velocity, and variety of data that modern AI systems require. The Takeaway: Acknowledging the Backbone of AI While AI may seem to be the flashy, attention-grabbing side of technology, it’s the data engineers who truly make it all possible. The next time you marvel at how an AI system anticipates your needs or adapts to your behavior, remember the hard work of the data engineers who laid the groundwork for those intelligent systems to thrive. Collaboration is key! Let’s continue to celebrate and support the essential roles that both data engineers and AI engineers play in shaping the future of technology. 🤝

I offer an engagement to help you with your AI strategy on the following topics based on my cross industry experience as a data and AI professional in services and sales. My goal is to help you bring substance to the AI hype for your company. Please reach out if you have 6-12 weeks time to develop a custom AI strategy and pilot/proof of concept. Or I can do a 2-4 hour workshop to teach your team about these key components for an AI Strategy - AI Roadmap - AI Business Models - AI Project Implementation Processes - Technologies and Data Science/Machine Learning Algorithms - AI Governance - Ethical AI - People - Hiring Data Scientists - AI Platform Architecture and Design - ML Pipeline Automation - AI Infrastructure - Deployment Strategies

🚀 Boost Your Business with Machine Learning! Looking to harness the power of machine learning? Discover the key to success in our latest blog post: https://lnkd.in/eK7yJwtm Learn: - Benefits of machine learning for your business. - What to look for in top developers. - Tips for successful hiring. Don't miss out on these insights! #MachineLearning #TechTalent #Innovation #BusinessGrowth #WeblineIndia

Struggling to budget for a Machine Learning Engineer? The true cost may surprise you. 💰 The financial realities of hiring top ML talent are more complex than they appear. With the global Machine Learning market projected to hit $225.91 billion by 2030, competition for skilled MLEs is fierce. But it's not just about the salary – there’s much more to consider. Here’s a glimpse of what you should be thinking about: ✔️ Assessing your organization’s MLE readiness. ✔️ Current market demand and salary trends. ✔️ A comprehensive cost breakdown (spoiler: it’s more than you think). ✔️ Different hiring models and their financial implications. ✔️ Strategies for maximizing ROI on ML talent. Don’t let unexpected costs derail your AI initiatives. Get the full picture and set your ML projects up for success. Read our latest for more details: https://lnkd.in/gMVZjNzX #MachineLearning #AIHiring #TechBudgeting