AI and cloud computing are two of the most in-demand and fast-growing skills in the digital economy. But how can you acquire and update these skills to stay competitive and relevant in the job market? Here are some tips to help you close the AI and cloud computing skills gap.
Before you dive into the advanced topics of AI and cloud computing, you need to have a solid foundation in the core concepts and tools. This includes programming languages, data structures, algorithms, databases, networks, and security. You can find many online courses, books, and tutorials that cover these topics and help you gain practical experience. You can also join online communities and forums where you can ask questions, share ideas, and learn from others.
Master the basics, then ignite passion around AI and cloud computing. Build a solid foundation in core concepts and tools. Embrace online courses, tutorials, and practical experience. Engage with like-minded communities, sharing ideas and learning from others. With a strong base, explore exciting projects, and connect with enthusiasts for an exciting journey in the digital economy. Embrace continuous learning, with confidence and relevance in AI and cloud computing space.
I must say I do like the linkedin learning courses as you can learn many of the topics associated with cloud and AI both top down and bottom up. For example I did some of the GenAI courses and am considering doing some of the Azure ones too. There is something for every level. For me it's about getting a good grounding first and then planning if I want to go deeper on one specific subject.
Basics of the AI are really very important before going into deep knowledge. Some important tools knowledge and programming language can boost you quickly to get on the track!
It's also key to know that you need to know the basics to get started, but at some point, you need to start applying. There is a large market creating and suggesting you take up courses, my advice is to pick a select few courses and then focus on projects. You learn the most by building. fast.ai courses are the best for learning how to apply Deep learning in the real world and Kaggle competitons are incredible as well
Establishing a solid technical foundation and developing transferable skills for evolving technologies are crucial. Beyond technical knowledge, hone skills like problem-solving, communication, and collaboration. Approach tech with a growth mindset, focusing on expertise and real-world application. Balance your technological know-how with 'soft' skills such as emotional intelligence and creativity, ensuring technology serves human needs and effectively influences stakeholders.
AI and cloud computing are broad fields that encompass many subdomains and applications. You can't master them all, so you need to choose a specialization that matches your interests, goals, and strengths. For example, you can focus on machine learning, natural language processing, computer vision, or robotics for AI, or on AWS, Azure, Google Cloud, or DevOps for cloud computing. You can then deepen your knowledge and skills in your chosen area by taking more advanced courses, working on projects, and earning certifications.
Having a T shaped knowledge set is helpful. Be an okay generalist and a great specialist, most of us don't work in silos and have to collaborate with many other teams, it's really useful to have working knowledge of other sub-domains as well. How do you pick your specialisation? Passion driven learning is the fastest road to mastery, pick a passion project or problem and figure out how to solve it. You learn the most by applying.
The importance of specialization and domain/field is very important. Let's take the example of AI in Healthcare, as we know that all the algorithms and models may not fit into clinical data so if we know our specialty and then focus on a specific skill it will lead to a master in it. Nowadays these upcoming technologies AI, ML, and Cloud are key to success.
Try to look at the cloud computing skill that your dream job require or look at the current trend. Of course, stick with what you already comfortable with.
Specializing can indeed provide a clearer path in the vast landscapes of AI and cloud computing. By honing in on a specific domain, individuals can become experts, making them more valuable in niche areas. However, it's essential to remember that the tech world is interconnected. While specializing, maintaining a holistic understanding of adjacent fields can foster innovation and adaptability. For instance, a specialist in natural language processing with a foundational understanding of cloud deployment could create more scalable and efficient applications. The balance between depth and breadth can pave the way for well-rounded expertise.
This portion of the question misses the point of systems thinking. Better stated, while AI Cloud systems could encompass a broad scope you need to master the topics required by your system. These topics are defined in the system baseline which is continuously updated as your system matures.
AI and cloud computing are constantly evolving and innovating, so you need to keep up with the latest trends, developments, and best practices. You can do this by following industry experts, blogs, podcasts, newsletters, and social media accounts that provide insights, analysis, and updates on AI and cloud computing. You can also attend webinars, workshops, conferences, and hackathons where you can network, learn, and showcase your skills.
Living close to the source code is a great idea here, as builders you keeping an eye out on latest papers or weekly trending GitHub repositories are my personal favourites.
Almost for everyone but more important for young are the benefits the networking provides. It is the first and second degree networks that provide direct benefits in terms of career and recognition.
In the fast-paced realm of AI, a developer I know set aside "Trend Tuesdays" where she dedicates a few hours to explore new articles, tools, or podcasts. This not only kept her updated but also sparked innovative ideas for her projects. Over time, this habit became a catalyst for her professional growth, as she often was ahead of her peers in implementing cutting-edge techniques. Regular, dedicated time for exploration can make a significant difference in staying current.
There is only one AI Cloud trend you need to stay in front of, that is your system’s evolution and demand for innovation. How you do this may be sourced from any and all listed in this question. This is a journey of doing, failing and learning.
The best way to close the AI and cloud computing skills gap is to apply your skills to real-world problems and scenarios. You can do this by joining or creating projects that use AI and cloud computing to solve challenges, improve processes, or create value. You can also look for internships, freelancing, or volunteering opportunities that allow you to use your skills in a professional setting. This will help you build your portfolio, demonstrate your abilities, and gain feedback.
We gain these skills from online courses offered by various vendors/MOOC platforms or internally offered to employees through their firms. In my opinion, it goes beyond certifications, and closing the AI and cloud skills gap requires applying hands-on skills. I often stress to university students and the colleagues that I mentor, that the true value of learning emerges only when the knowledge is put into action. Practical application matters immensely - whether it be through small projects, volunteering for building a non-profit website leveraging cloud/AI technology, or shadowing industry experts. Applying these learned skills, combined with nurturing soft skills, is pivotal for navigating today’s tech landscape.
The application of skills to real-world scenarios is arguably the most effective way to bridge any skills gap. It enables learners to transition from more theoretical, foundational knowledge to the application of practical, essential skills. None has ever learned how to ride a bike or drive a car by watching someone else do it. You have to get on the bike or behind the while to master the skill.
The bigger question here is how to select your first project or where to apply this knowledge? Most of us as engineers struggle to come up with a creative problem or project. The best approach is to take up the first few projects without overthinking it, most likely the first projects are inclined to be stepping stones, so it's good to optimise for speed rather than planning when starting out
Absolutely, application is where theory meets practice. While theoretical knowledge provides a foundation, applying skills to tangible problems solidifies learning and reveals gaps. It's akin to learning a musical instrument: reading about music is informative, but playing tunes refines the skill. In the tech world, real-world projects not only enhance proficiency but also boost confidence, making individuals more marketable and effective in their roles. Practical experience is invaluable in truly mastering AI and cloud computing.
The most important skill we need to apply is that of being human. We are much more than our sum of knowledge; we build machines (AI Cloud in this context) to free us from the mundane that can be reduced to one-zeros. The skill we need to apply is one of value creation through systems: - problem solving - process enhancement - stability and resilience
AI and cloud computing are dynamic and complex fields that require continuous learning and improvement. You can't afford to stop learning or become complacent with your skills. You need to constantly challenge yourself, seek feedback, and learn from your mistakes. You also need to explore new topics, tools, and techniques that can enhance your skills and expand your horizons. You can use online platforms, courses, books, podcasts, and mentors to help you keep learning and growing.
Writing about your projects is a great way to both get exposure and also have fun. It's great to build, sharing your knowledge with the world is an accelerator in learning.
Anyone who is not a continuous learner will soon find themselves out of context. The speed with which the technologies and their applications develop is faster (and getting faster) that most people think. One caveat is to have at least one domain to have deeper learning and some peripheral learning for adjacent domains.
Setting aside a dedicated "learning hour" each week can be a great way to ensure continuous growth. During this time, dive into a new topic, revisit foundational concepts, or explore an emerging tool in AI or cloud computing. Over time, this habit compounds, ensuring you remain at the forefront of industry advancements and continually refine your skills. Pair this with monthly goals or challenges to apply what you've learned, and you'll find yourself not only keeping up with the field but also leading in it.
The best system learning is based on testable outcomes. While intuition is a core of what makes us human, intuition informed by a testable outcome assures our successful. Along our AI Cloud journey we need to establish milestones as a measure of our progress. It is the measurable deviation from plan that drives learning and innovation.
Whatever you choose to learn, make sure to understand the broader context it relates to. No doubt, individual tools, techniques, and programming languages are key. But understanding what they can be used for and what other tools, etc. are required to deliver AI-driven cloud applications will help you grow in your career — and gain more flexibility to do so.
Closing the AI and cloud computing skills gap is essential, with cloud computing being a crucial enabler of AI innovation. The key to bridging this gap lies in a holistic understanding of available tools and ecosystems and applying suitable solutions to meet business needs, rather than creating in-house tools. Success in this domain requires the right team, robust data strategies, solid infrastructure, and strategic partnerships with cloud providers, all aligned with the operational and strategic goals of the business. This approach ensures the relevance, sustainability, and contribution of innovations to the long-term success of the organization.
The field of AI and cloud computing is vast. So whatever you choose, make sure you choose a specific area or topic to dive into. Also, recognise recognise that having a technical background is not enough, you need also to understand the business context.