Outside the FAANG corporations like Facebook, Amazon, Netflix, artificial intelligence (AI) is now being broadly embraced. Recently, At Sartify LLC have helped Niajiri Platform Ltd to create, deploy and scale machine learning system thatextract data from resumes and quickly fill up those information in their system. The solution has saved 100+ hrs, increased 10x efficiency at 96% success rate, saved money, and better talents obtained as it maintains fairness to all candidates.. And niajiri isn't alone..
A recent index from Deloitte shows how companies across sectors are operationalizing AI to drive business value. It comes as no surprise that even Gartner predicts that by the end of 2024, more than 75% of organizations will have moved from AI technology piloting to operationalizing it, This shift will present new many challenges thus this article discusses how to avoid them .
AI is most valuable when it is operationalized at scale. For business leaders who wish to maximize business value using AI, The term "scale AI"refers to the extent to which AI is integrated into an organization's core products or services, as well as its business processes. In other words, it measures the depth and breadth of AI integration within an organization.
Unfortunately, scaling AI in this sense isn’t easy. Getting one or two AI models into production is very different from running an entire enterprise or product on AI. And by experience is that as AI is scaled, problems can (and often do) scale too.
As an example, a specific mid-sized financial organization lost more than $20,000 in less than 10 minutes as a result of a problem with one of its machine learning models.
The same thing recently happened to tech giant google, Google's AI chatbot, Bard, sparks a $100 billion loss in Alphabet shares Google's after delivering a factual error in a search demo that the company shared widely.
You see! AI has a lot of risks and a lot of projects fail in the AI field, With no visibility into the root issue — and no way to even identify which of its models was malfunctioning — most companies left with no choice but to pull the plug such as take down the model or roll back to previous iterations which degrades the performance too.
As Warren Buffet says :
Risk comes from not knowing what you are doing
Therefore , At Sartify LLC we have helped organizations that are serious about AI to gain value from scaling it and increase ROI by professionally using a new discipline, defined loosely as “MLOps” or Machine Learning Operations to solve their business challenges with scalable AI solutions.
MLOps seeks to establish best practices and tools to facilitate rapid, safe, and efficient development and operationalization of AI. When implemented right, MLOps can significantly accelerate the speed to market and reduce unnecessary risks. Implementing MLOps requires investing time and resources in three key areas: people, processes and tools.
- People: Let teams focus on what they’re best at:
- Processes: Standardize how you build and operationalize models.:
- Tools: Pick tools that support creativity, speed, and safety:
AI development used to be the responsibility of an AI “data science” team, but building AI at scale can’t be produced by a single team — it requires a variety of unique skill sets, and very few individuals possess all of them. For example, a data scientist creates algorithmic models that can accurately and consistently predict behavior, while an ML engineer optimizes, packages, and integrates research models into products and monitors their quality on an ongoing basis. One individual will seldom fulfill both roles well. Compliance, governance, and risk require an even more distinct set of skills. As AI is scaled, more and more expertise is required.
To successfully scale AI, business leaders should build and empower specialized, dedicated teams that can focus on high-value strategic priorities that only their team can accomplish. Let data scientists do data science; let engineers do the engineering; let IT focus on infrastructure.
Building the models and algorithms that power AI is a creative process that requires constant iteration and refinement. Data scientists prepare the data, create features, train the model, tune its parameters, and validate that it works.
When the model is ready to be deployed, software engineers and IT operationalize it, monitoring the output and performance continually to ensure the model works robustly in production. Finally, a governance team needs to oversee the entire process to ensure that the AI model being built is sound from an ethics and compliance standpoint.
Given the complexity involved here, the first step to making AI scale is standardization: a way to build models in a repeatable fashion and a well-defined process to operationalize them.
In this way, creating AI is closely akin to manufacturing: The first widget a company makes is always bespoke; scaling the manufacturing to produce lots of widgets and then optimizing their design continuously is where a repeatable development and manufacturing process becomes essential. But with AI, many companies struggle with this process.
The process standardization piece of MLOps helps streamline the development, implementation, and refinement of models, enabling teams to build AI capabilities in a rapid but responsible manner.
MLOps tools such as Model Catalogs and Feature Stores can support this standardization.
Finally, we come to tools. Given that trying to standardize the production of AI and ML is a relatively new project, the ecosystem of data science and machine learning tools is highly fragmented — to build a single model, a data scientist works with roughly a dozen different, highly specialized tools and stitches them together.
On the other side, IT or governance uses a completely different set of tools, and these distinct toolchains don’t easily talk to each other. As a result, it’s easy to do one-off work, but building a robust, repeatable workflow is difficult.
Ultimately, this limits the speed at which AI can be scaled across an organization. A scattershot collection of tools can lead to long times to market and AI products being built without adequate oversight.
Faster iteration demands ongoing contributions from stakeholders across the model lifecycle, and finding the correct tool or platform is an essential step. Tools and platforms that support AI at scale must support creativity, speed, and safety. Without the right tools in place, a business will struggle to uphold all of them concurrently.
When picking MLOps tools for your organization, a leader should consider ):
Interoperability : There will almost always be some kind of AI infrastructure in place already. Choose a tool that will work with the current ecosystem to lessen resistance to adoption. Model services must function with DevOps technologies that IT has previously validated on the production side (e.g., tools for logging, monitoring, and governance).
Make that new tools can be quickly adapted to give this assistance or that they will function with the current IT environment. Finding solutions that will function in a hybrid environment is important for firms going from on-premise infrastructure to the cloud, as cloud migration frequently takes several years.
Whether it’s friendly for data science as well as IT : Tools to scale AI have three primary user groups: the data scientists who build models, the IT teams who maintain the AI Infrastructure and run AI models in production, and the governance teams who oversee the use of models in regulated scenarios.
Of these, data science and IT tend to have opposing needs. To enable data scientists to do their best work, a platform must get out of the way — offering them the flexibility to use libraries of their choice and work independently without requiring constant IT or engineering support.
On the other hand, IT needs a platform that imposes constraints and ensures that production deployments follow predefined and IT-approved paths. An ideal MLOps Platform can do both. Frequently, this challenge is solved by picking one platform for the building of models and another platform for operationalizing them.
Collaboration : As described
above, AI is a multi-stakeholder initiative. As a result, an MLOps tool
must make it easy for data scientists to work with engineers and vice
versa, and for both of these personas to work with governance and
compliance. In the year of the Great Resignation, knowledge sharing and
ensuring business continuity in the face of employee churn are crucial.
In AI product development, while the speed of collaboration between data science and IT determines speed to market, governance collaboration ensures that the product being built is one that should be built at all.
Governance : Governance in AI and ML applications is significantly more important than it is in other applications. AI governance extends beyond an application's security or access management. It is in charge of guaranteeing that an application complies with an organization's ethical standards, that it does not discriminate against members of protected groups, and that the judgments made by the AI application can be trusted.
As a result, it becomes essential for any MLOps tool to bake in practices for responsible and ethical AI including capabilities like “pre-launch” checklists for responsible AI usage, model documentation, and governance workflows.
VOILÀ! ) :
Apart from MLOps, the use of other well-defined processes and methodologies, such as Research & Development (R&D) , and Data-Driven Scrum (DDS) , is crucial for AI scale within an organization. By using these methodologies, companies can ensure that AI is deeply integrated into their core products, services, and business processes.
At Sartify LLC, we prioritize the use of these methodologies to deliver AI-driven business value to our clients, while maintaining a well-defined and repeatable process for building and operationalizing AI models. .
In the race to scale AI and realize more business value, leaders are always looking for ways to get ahead of the pack. AI shortcuts like pre-trained models and licensed APIs can be valuable in their own right, but scaling AI for maximum ROI demands that organizations focus on how they operationalize AI.
The businesses with the best models or smartest data scientists aren’t necessarily the ones who are going to come out on top; success will go to the companies that can implement and scale smartly to unlock the full potential of AI.
At sartify LLC we positioned to help our clients attain success with AI not just it's implementation, reach us today through info@sartify.com to start experience exponential growth with AI.
