Dive deeper, develop an effective strategy, and start leveraging the full power of machine learning technologies.
The machine learning journey
Businesses have the opportunity to unlock significant value across the organization with the help of machine learning and AI. Follow the proven path to machine learning success. Read the e-book to discover:
- The full machine learning journey in 6 steps
- How to transform investments into business-differentiating solutions
- Inspiring stories of industry leaders who’ve successfully implemented machine learning
- Insights from AWS machine learning experts
Think of it as an instruction manual to guide you on your journey at every stage and help you accelerate machine learning initiatives to achieve measurable results.
7 leading machine learning use cases
Move beyond the hype and discover the tangible benefits of machine learning. In this e-book, we have outlined seven leading use cases where businesses have successfully applied machine learning to achieve fast, efficient, and measurable results. Read the e-book to learn more about these use cases and the requirements you should look for when identifying a suitable application for machine learning, such as:
- Solves real business problems
- Leverages sources of untapped data
- Can be completed in a matter of months
Download the e-book now to start or expand your machine learning journey.
Accelerating machine learning innovation through security
To build successful machine learning models, you often need datasets unique to your business. These datasets are extremely valuable assets and need to be secured throughout every step of machine learning—including data preparation, training, validation, and inference.
Amazon SageMaker, a fully managed machine learning service, provides comprehensive security features that can help your organization:
- Meet the strict security requirements of machine learning workloads
- Secure datasets through every step of the process
- Go from idea to production faster, more securely, and with a higher rate of success
Read the e-book to learn how security features from Amazon SageMaker and the AWS Cloud can help you go from idea to production faster.
A strategic playbook for data, analytics, and machine learning
If your organization is like most others, the pressure is on to use data, analytics, and machine learning to drive critical business decisions, including:
- When to expand product offerings
- How to introduce new revenue streams
- Where to automate manual processes
- How to optimize interactions with customers and business partners
Download the IDG CIO Playbook for help refining your data-driven strategy so you can effectively scale analytics and machine learning across your enterprise. Get the guidance you need to accelerate innovation and drive your business forward with data.
Modernize machine learning development at scale
Machine learning has entered the mainstream and organizations are leveraging its power to drive real-world innovation. Amazon SageMaker, the most comprehensive ML cloud service, provides scalable infrastructure and integrated tools to enable organizations modernize their ML development and reap the benefits of ML faster. In this e-book, discover the business outcomes of modernizing ML development using Amazon SageMaker including:
- Acceleration of ML development
- Responsible and secure use of ML
- ML accessibility to builders of all ML skill levels
- Reduction in the total cost of ownership
Q&A: Choosing the right compute infrastructure for machine learning
Now that the cloud has brought machine learning within reach for organizations big and small, many executives are asking, “What factors should I consider when choosing the right machine learning infrastructure and services for my objectives?” For the answers to that question and more, we turned to Dr. Saha, Vice President of machine learning services in Amazon AI. Access the Q&A for a closer look at:
- The biggest challenges facing developers and organizations as they adopt machine learning
- Guidance and best practices for evaluating the infrastructure requirements of your ML workloads
- Amazon EC2 compute instances for ML model training and inference