Are you getting your AI/ML strategy right?

by Prashanti Ramesh on October 14, 2021 in Blogs, Uncategorized



As businesses combine cloud-based compute, memory, and networking with an explosion of new data, AI and machine learning (AI/ML) are hot topics. This potent combination assists businesses in providing superior customer experiences, better understand their business environment, and driving new levels of efficiency.

However, achieving these AI/ML-driven successes is difficult. The majority of respondents are still investigating or struggling to implement AI/ML models.

Why do AI/ML projects fail?

Businesses are struggling with their AI/ML efforts for a variety of reasons, including:

Failure to deliver the correct data to the appropriate app or point of analysis in real-time:

The data you feed into your AI/ML frameworks and intelligent applications determines the quality of your machine learning training. If the information is poor, old, or incomplete, the training will be inadequate, and the answers and results generated will be equal to the data’s quality and possibly incorrect.

Inadequate organizational collaboration

Designing the proper machine learning training and AI algorithms necessitates a cross-organizational understanding of the data and processes that are automated. This necessitates communication and buy-in from all levels of the organization. Lack of collaboration frequently results in poor implementation, poor data quality, and rejection of applications/automation projects by critical parts of the organization.

Immaturity in IT and business processes

If your IT and business processes are not well-formed, your data will likely be incomplete, and your AI/ML execution will be subpar. Furthermore, AI and ML is best served by rapid iterations and improvements in data and algorithms, which occur most effectively in a DevOps culture.

Inadequate knowledge of algorithm design, data science, and engineering

SKILLS ARE ESSENTIAL because AI and machine learning are based on high-quality, timely data and well-formed algorithms—representing the best in real-world processes and models. Finding talent in today’s market is a challenge.

However, with the right AI/ML strategy, you can overcome these obstacles. Let’s take a closer look at how you can make this happen.

Four steps to developing an effective AI and ML strategy

1. Lay the groundwork

First, you must prepare your data and applications for migration to the appropriate multi-cloud and data architecture environments. This includes learning about and comprehending your current environment and requirements, as well as developing a roadmap.

Ascertain that the data architecture appropriately supports new application deployments and that you can minimize ingress/egress fees while also maximizing performance and availability. Database transformations and data warehouse migrations are carried out at this stage.

2. Upgrade the data architecture

The transition into this phase is driven by the definition of the modern data architecture, strategy, and roadmap. In this section, you’ll concentrate on modernizing your data architecture by defining, designing, and building the data fabric. Pipelines and integration, data lakes and warehouses, and the analytics platform are all part of this.

3. Set the stage for more creativity

AI/ML prepares your organization for high-quality automation and predictive intelligence, propelling it to the next level of innovation. At this stage, you will be planning data science by designing, training, and deploying models, as well as operationalizing machine learning (MLOps). This allows you to add more value to the modern cloud and data architecture you created in Steps 1 and 2. You can begin working on this while working on Step 1, or at the very least execute the migration with an eye toward data architecture modernization.

4. Create intelligent applications

Finally, you’re ready to begin delivering strategic value and capability, where you’ll be able to fully realize the importance of this new cloud-based data fabric you’ve built. You can extract value from IoT data by using intelligent applications that incorporate chatbot services, natural language processing, machine vision, recommendation engines, predictive maintenance, and even actions. It’s all possible now, and it creates a new foundation for your company.

Professional advice for your AI and machine learning journey

When your data works harder for you, you can put your resources to better use, delivering intelligent applications, services, and outcomes. As a result, you’ll be able to make better decisions, improve collaboration, launch new revenue streams and business models, and transform customer experiences.


Do you require assistance in delivering the correct data to the proper application at the right business moment while also delivering a new level of business insights? Our experts are here to assist you. Allow our experts to assist you in harnessing the power of modern data architecture and AI.