Four steps to AI and machine learning success
AI and machine learning (AI/ML) are hot topics, as businesses bring together cloud-based compute, memory and networking with an explosion of new data. This powerful combination is helping businesses deliver superior customer-centric experiences, understand their business environment like never before and drive new levels of efficiency.
But achieving these AI/ML-driven successes is tough. In a recent Rackspace Technology-sponsored study, only 17% of respondents report mature AI/ML capabilities. The majority of respondents (82%) are still exploring or are struggling to operationalize AI/ML models.
Why AI and machine learning efforts fail
According to the study, businesses are struggling with their AI/ML efforts for several reasons:
- Failure to get the right data to the right app or point-of-analysis in real time
Your machine learning training is only as good as the data you feed into your AI/ML frameworks and intelligent applications. If the data is bad, old or incomplete, the training will be poor and the answers and results generated will be (at best) equal to the quality of the data — and perhaps flat out wrong.
- Lack of organizational collaboration
Designing the right machine learning training and AI algorithms requires a holistic understanding of the data and processes that you’re automating, across organizational boundaries. This requires communication and buy-in across the business. Lack of collaboration often yields a poor implementation, lower-quality data and rejection of the applications/automation projects by key parts of the organization.
- IT and business process immaturity
If your IT and business processes are not well formed, then it’s likely your data is not complete, and the AI/ML execution will be sub-par. Also, AI/ML is best served with rapid iterations and improvements in the data and algorithms — something that happens most effectively in a DevOps culture.
- Lack of expertise in mathematics, algorithm design or data science and engineering
Since AI and machine learning are built on high-quality, timely data and well-formed algorithms—representing the best in processes and models of the real world — skills are critical. And finding the talent is tough in today’s market.
But with the right AI/ML strategy, you can overcome these challenges. Let’s dive deeper into how you can make this happen.
Four steps to AI and machine learning success
Step 1: Build the foundation
You must start by preparing your data and applications to migrate to the right multicloud and data architecture environments. This includes getting to know and understand your current environment and requirements and defining a roadmap.
Be sure that the data architecture supports the new application deployments appropriately, and that you can minimize ingress/egress fees while also maximizing performance and availability. This is also the stage when database transformations and data warehouse migrations are implemented.
Step 2: Modernize the data architecture
Defining the modern data architecture, strategy and roadmap drives the transition into this phase. Here, you’ll focus on modernizing your data architecture — defining, designing and building the data fabric. This includes pipelines and integration, data lakes and warehouses, and the analytics platform.
You can start on this while you’re working on Step 1, or at least execute the migration with an eye toward data architecture modernization.
Step 3: Set the stage for more innovation
AI/ML prepares your organization for high-quality automation and predictive intelligence — driving innovation to the next level. At this stage, you’ll be planning the data science by designing, training and deploying the models, and operationalizing machine learning (MLOps). This enables you to deliver greater value to the modern cloud and data architecture you built in Steps 1 and 2.
Step 4: Build intelligent applications
Finally, you’re ready to start delivering strategic value and capability, where you can realize the full value of this new cloud-based data fabric you’ve created. You can employ intelligent applications that incorporate chatbot services, natural language processing, machine vision, recommendation engines, predictive maintenance and even actions — and get value from IoT data. It’s all possible now and forms a new foundation for your business.
Expert guidance for your AI and machine learning journey
When your data works harder for you, you can take your resources further, delivering intelligent applications, services and results. This, in turn, enables you to make smarter decisions, improve collaboration, deliver new revenue streams and business models, and transform customer experiences.
Do you need help getting the right data to the right application at the right business moment, while delivering a new level of business insights? Our experts are here to help. Let our specialists help you harness the power of modern data architecture and AI.
Are You Realizing the Cloud Optimization Benefits of Kubernetes and Containers?
September 22nd, 2023
Google Cloud Next ’23 Highlights— AI and Beyond
September 14th, 2023
Why You Need an MLOps Framework for Standardizing AI and Machine Learning Operations
September 12th, 2023
Mastering Multicloud: The Power of Cloud-Neutral Data Storage
August 31st, 2023