Current State of the market
In order to go in-depth on what exactly data science and machine learning (ML) tools or platforms are, why companies small and large are moving toward them, and why they matter in the Enterprise AI journey, it’s essential to take a step back and understand where we are in the larger story of AI, ML, and data science in the context of businesses:
1. Enterprise AI is at peak hype.
Of course, the media has been talking about consumer AI for years. However, since 2018, the spotlight has turned to the enterprise. The number and type of devices sending data are skyrocketing while the cost of storing data continues to decrease, which means most businesses are collecting more data in more types and formats than ever before. Moreover, to compete and stay relevant among digital startups and other competition, these companies need to be able to use this data not only to drive business decisions but drive the business itself. Now, everyone is talking about how to make it a reality.
2. AI has yet to change businesses.
Despite the hype, the reality is that most businesses are struggling to leverage data at all, much less build machine learning models or take it one step further into AI systems. For some, it’s because they find building just one model is far more expensive and time-consuming that they planned for. However, the great majority struggle with fundamental challenges, like even organizing controlled access to data or efficient data cleaning and wrangling.
3. Successful enterprises have democratized.
Those companies that have managed to make progress toward Enterprise AI have realized that it’s not one ML model that will make the difference; it’s thousands or hundreds. Also, that means scaling up data efforts in a big way that will require everyone at the company to be involved. Enter democratization. In August 2018, Gartner identified Democratized AI as one of the five emerging trends in their Hype Cycle for Emerging Technologies. Since then, we have seen the word “democratization” creep into the lexicon of AI-hopefuls everywhere, from the media to the board room. Also, to be sure, it’s an essential piece of the puzzle when it comes to an understanding of data science and machine learning (ML) platforms.
Is hiring Data Scientist enough to fulfil your AI and Machine learning goals?
Employing for data functions is at an all-time high. Currently in 2019, according to career listing data, a data scientist is the hottest career out there. Moreover, though statistics on Chief Data Offers (CDOs) vary, some put the figures as high as 100-fold growth in the function over the past 10 years.
Hiring data experts is a crucial element to a robust Enterprise AI strategy; however, hiring alone does not guarantee the expected outcomes, and it isn’t a factor not to invest in data science and ML platforms. For one thing, working with data scientists is costly – often excessively so – and they’re only getting more so as their need grows.
The truth is that when the intention is going from producing one ML model a year to tens, hundreds, or even thousands, a data team isn’t enough because it still leaves a big swath of employees doing day-to-day work without the capability to take advantage of data. Without democratization, the result of a Data team – even the very best one comprised of the leading data scientists – would be restricted.
As a response to this, some companies have decided to leverage their data team as sort of an internal contractor, working for lines of business or internal groups to complete projects as needed. Even with this model, the data team will need tools that allow them to scale up, working faster, reusing parts of projects where they can, and (of course) ensuring that all work is properly documented and traceable. A central data team that is contracted out can be a good short-term solution, but it tends to be a first step or stage; the longer-term model of reference is to train masses of non-data people to be data people.
Choosing the right tools for Machine Learning and AI
Opens Source – critical, but not always giving what you need
In order to be on the bleeding edge of technological developments, using open source makes it easier to onboard a team and hire. Not only are data scientists interested in growing their skills with the technologies that will be the most used in the future, but also there is less of a learning curve if they can continue to work with tools they know and love instead of being forced to learn an entirely different system. It’s important to remember, that keeping up with that rapid pace of change is difficult for big-sized corporations.
The latest innovations are usually highly technical, so without some packaging or abstraction layers that make the innovations more accessible, it’s challenging to keep everybody in the organization on board and working together.
A business might technically adopt the open source tool, but only a small number of people will be able to work with it. Not to mention that governance can be a considerable challenge if everyone is working with open source tools on their local machines without a way to have work centrally accessible and auditable.
Data science and ML platforms have the advantage of being usable right out of the box so that teams can start analyzing data from the first day. Sometimes, with open source tools (mostly R and Python), you need to assemble a lot of the parts by hand, and as anyone who’s ever done a DIY project can attest to, it’s often much more comfortable in theory than in practice. Choosing a data science and ML platform wisely (meaning one that is flexible and allows for the incorporation and continued use of open source) can allow the best of both worlds in the enterprise: cutting-edge open source technology and accessible, governable, control over data projects.
What should Machine Learning and AI platforms provide?
Data science and ML platforms allow for the scalability, flexibility,
and control required to thrive in the era of Machine Learning and AI because they provide a framework for:
- Data governance: Clear workflows and a method for group
leaders to monitor those workflows and data jobs. - Efficiency: Finding little methods to save time throughout the data-to-insights process gets business to organization value much faster.
- Automation: A specific type of performance is the growing field
of AutoML, which is broadening to automation throughout the data pipeline to ease inefficiencies and maximize personal time. - Operationalization: Effective ways to release data jobs into production quickly and safely.
- Collaboration: A method for additional personnel working with data,
much of whom will be non-coders, to add to data tasks in addition to data scientists (or IT and data engineers). - Self-Service Analytics: A system by which non-data expert from various industries can access and deal with data in a regulated environment.
Some things to consider before choosing the AI and MAchine Learning platform
Governance is becoming more challenging
With the quantity of information being accumulated today, data safety and security (particularly in specific sectors like financing) are crucial. Without a central area to access and collaborate with information that has correct user controls, data might be saved across different individuals’ laptop computers. And also if an employee or specialist leaves the company, the threats raise not just because they could still have accessibility to sensitive data, however since they might take their collaboration with them as well as leave the group to go back to square one, uncertain of what the individual was servicing. On top of these concerns, today’s enterprise is afflicted by shadow IT; that is, the suggestion that for years, different divisions have invested in all kinds of various innovations and are accessing as well as utilizing information in their ways. A lot to make sure that also IT groups today do not have a central sight of that is using what, just how. It’s a problem that becomes dangerously amplified as AI efforts scale and points to the requirement for governance at a more significant as well as much more fundamental scale throughout all industries in the business.
AI Needs to Be Responsible
We learn from a young age that topics like science and mathematics are all goal, which implies that naturally, individuals think that data science is as well – that it’s black and white, a specific discipline with just one method to reach a “proper” service, independent of who constructs it. We’ve understood for a long time that this is not the case and that it is possible to utilize data science strategies (and, hence, produce AI systems) that do things, well … incorrect. Even as just recently as last year, we are witnessing with problems that giants like Google, Tesla and Facebook face with their AI systems. These problems can cause domino effect very fast. It can be private information leakage, photo mislabelling, or video recognition not recognizing a pedestrian on crossing the road and hitting it.
This is where AI needs to be very responsible. And for that you need to be able to discover in early stages where you AI might fail, before deploying it in the real world.
The fact that these companies might not have fixed all of the problems, showing quickly how challenging it is to get AI.
Reproducibility of Machine Learning projects as well as scaling the same projects
Absolutely nothing is extra ineffective than needlessly repeating the same processes over as well as over. This relates to both duplicating procedures within a project (like data prep work) over and over as well as repeating the same process throughout projects or – even worse – unintentionally duplicating entire jobs if the team gets large yet does not have insight right into each other’s role. As well as no service is insusceptible to this danger – as a matter of fact, this issue can become exponentially worse in huge ventures with bigger teams and also even more separate in between them. To range efficiently, data groups require a tool that helps in reducing duplicated work and makes sure that work between members of the group hasn’t currently been done before.
Utilize Data Experts to Augment Data Scientists’ Job
Today, information researcher is one of the most in-demand settings. This means that data scientists can be both (1) difficult to locate and bring in and also (2) expensive to work with as well as retain. This combination implies that to range data initiatives to pursue Venture AI, it will unavoidably need to be submitted with service or information analysts. For the two sorts of a team to collaborate appropriately, they require a central atmosphere from which to work. Experts also often tend to work in a different way than data scientists, experienced in spreadsheets as well as possibly SQL yet generally not coding. Having a tool that allows each account to leverage the tools with which (s)he is most comfortable enables the performance to range data efforts to any size.
Ad-Hoc Methodology is Unsustainable for Large Teams
Small teams can sustain themselves to a specific point
by dealing with data, ML, or larger AI tasks in an ad-hoc fashion,
indicating staff member save their work in your area and not centrally and don’t have any reproducible procedures or workflows, figuring
things out along the method.
However, with more than just a couple of employee and more than one
job, this becomes rowdy rapidly. Any business with any hope of
doing Enterprise AI requires a central location where everybody involved
with data can do all of their work, from accessing data to deploying
a design into a production environment. Permitting workers -whether directly on the data team or not – to work ad hoc without a central tool from which to work is like a construction group attempting to build a high-rise building without a primary set of blueprints.
Machine Learning models Need to be Monitored and Managed
The most significant distinction between developing traditional software application and developing machine learning models is upkeep. For the most part, the software is composed when and does not need to be continually kept – it will typically continue to work over time. Machine learning models are established, put in production, and then must be kept an eye on and fine-tuned up until performance is ideal. Even when the efficiency is optimal, model performance can still move gradually as data (and the individuals producing it) changes. This is quite a different approach, especially for companies that are used to putting software application in production.
Moreover, it’s easy to see how issues with sustainability might eventually trigger – or intensify – problems with ML design bias. In reality, the two are deeply linked, and disregarding both can be devastating to a business’s data science efforts, particularly when magnified by the scaling up of efforts. All of these factors point to having a platform that can help manage design tracking and management.
Required to Create Models that Work in Production
Investing in predictive analytics and data science means guaranteeing that data teams are productive and see projects through to completion (i.e., production) – otherwise called operationalization. Without an API-based tool that allows for a single release, data teams likely will need to hand off designs to an IT team who then will have to re-code it. This step can take lots of time and resources and be a substantial barrier to executing data tasks that genuinely affect the business in essential methods. With a tool that makes it smooth, data groups can easily have an impact, screen, fine-tune, and continue to make improvements that positively impact the bottom line.
Having all said, choosing the right platform is not always straightforward. You need to carefully measure what you really need now and what will you need in the future.
You need to do so taking in account your budget, employees skills and their willingness to learn new methodologies and technologies.
Please bare in mind that developing a challenging AI project takes time, sometimes couple of years. that means your team can start building your prototipe in easy to use open source machine learning platform. Once you have proven your hypothesis you can migrate to more complex and more expensive platform.
Good luck on your new machine learning AI project!
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