AI (Artificial Intelligence) is no longer an alien on the planet of IT capabilities. It is going mainstream with every new tool and application that the industry embraces as the relevance of AI and ML (Machine Learning) gathers more steam. After all, we are talking about 2.3 million jobs by 2020 (~ Gartner).
From industry biggies like Google and Amazon Web Services (AWS) to newbies, everyone is trying to put a duck in the AI and ML rows.
The current state of AI tools and programs, hence, presents an unusual opportunity for developers, database engineers, IT professionals and practitioners.
The space is still nascent, so it is the right time to start equipping oneself with the resources and arsenals that are emerging in the realm of AI and ML. That said, picking an AI expertise program should not be a short-sighted strategy. Choose your path after careful thought, as it is going to join a super-fast highway someday soon.
But how do you ascertain if the program you are pursuing is a well-rounded and relevant one for your career ambitions? Simple, see where the market is moving. Do not follow the herd, but you can always follow the Sun.
1. Look who is conducting
Certain Institutions and people have built sharp expertise in AI and Machine Learning because of access to deep tech research infrastructure. Faculty from such credible-research backed Institutes can help you guide towards deep aspects of AI and Machine Learning. Are you on the radar of such an Institution?
2. Depth of the Curriculum
Is the curriculum deep enough for you to step into an AI project confidently? Or is it giving just a surface-level familiarity? Remember, companies are deploying many methods to find out how deep your knowledge is. Just a mention of AI in your resume will no longer cut the ice. In fact, the claim that you have learned AI will make you go through additional questions and assessments at the time of recruitment or project team formation. Choose a program that has a practitioner’s curriculum and provides both foundational and experiential learning opportunities.
3. Check the top tools
It has been noticed that certain tools are flagship choices of the players and users of AI. Their dominance has remained unshaken, and they are also investing in fixing gaps and addressing new trends. So if a program does not include tools like Python, TensorFlow, R, SageMaker etc. then you should try to assimilate these areas through the right programs or complementary learning.
4. Take a step ahead, say the ‘A’ word
Yes, no matter how cool a tool or a capability sounds, everything is going to boil down to its applicability and scalability. So any program that ignores the business aspect of technology is bound to be reduced to isolation and redundancy. Go for capabilities that build-up the execution and usage side of AI– to the last mile. Ask for application knowledge.
5. Watch out for the wreckage
It has been widely apprehended by now that the influx of new-age ML engineers, data scientists and automation is going to replace a lot of jobs. Now would be a good time to ask if any of these areas would actually be decimated or would they evolve into new roles. You may need to spruce up your existing repertoire to match the new-world needs. Why throw away your hard-earned experience and insight just because the grapevine says so? Why not adapt? Why not find interfaces that make you all the more relevant?
6. Complement AI
A lot of AI success would hinge upon the availability and compatibility of other areas of technology and skills. So investing in Spark, Apache, Hadoop, DevOps, frameworks like Keras, Containers, RPA, Reinforcement learning, Neural Networks etc. would also be a good idea. Everything that helps AI accelerate its impact and reach would be hot next and will stay hot for a long time.
7. Life-long learning
Do not assume that a program is a three-week stop-watch exercise. Any institute or team that simply slaps on a time-bound box as a program is failing to see the wider dimensions and implications of AI. A great AI program should enable you to open your mind and reach. It should expand your horizons to continuous learning, new networks, practice areas, and meaningful re-learning.
If there is one thing you can be sure about AI is that it is going to get bigger and better. So maybe investing in something that needs manual coding now would become obsolete with the very next library or tool kit that an AWS or Microsoft or a Google tosses out. But if you know how to make sense of AI, and how to translate it into a solution – those are the capabilities that will never go out of date.
So go ahead and arm yourself well. But remember, it is not a battle. It is Darwinism at work.