The media and entertainment (M&E) industry is undergoing a revolutionary shift toward cloud-based workflows. Leading this transformation, Amazon Web Services (AWS) is offering a wide range of tools and services that help support, streamline, and enhance this transition. Like all major developments in technology, there is often confusion and misinformation surrounding the adoption.
Contents
- Goal of this Series of Articles
- Why Migrate M&E to the Cloud?
- Recent Challenges in the M&E Sector (As of Mar 2024)
- Addressing Other Common Challenges
- Global Trends
- Artificial Intelligence & Machine Learning (AI/ML)
- AWS Services for AI/ML Workloads
- Popular AI/ML Use Cases in M&E
- Best Practices for Building and Deploying AI/ML Applications on AWS
- Solution Example: Media2Cloud Implementation for Jukin Media
- Data Analytics
- Data Analytics – M&E Use Cases
- Data Lake vs. Data Warehouse for Media Content
- Content/Data Lake Solution Architecture
- Data Warehouse Solution Architecture
- Solution: TrackIt Data Insights
- Data Collaboration: AWS Clean Rooms
- Other Data Solutions
- Working with an AWS M&E Partner Integrator
Goal of this Series of Articles
This series of articles seeks to educate the reader on the benefits of AWS, specifically for M&E workflows. It will provide insights into how the comprehensive suite of services offered by AWS can empower content creators, broadcasters, and streaming platforms to optimize their operations, enhance scalability, and ensure cost-efficiency. In a nutshell, these articles aim to serve as a solid knowledge base that explores the possibilities that AWS provides for the media industry.
Why Migrate M&E to the Cloud?
The adoption of M&E cloud-based workflows brings numerous benefits:
Agility: Easy-to-deploy cloud resources provide rapid response to market opportunities.
Cost Savings: Reduces commitment to expensive and quickly obsolete on-premises infrastructure, resulting in significant cost savings.
Flexibility: Resources can be auto-scaled up or down based on fluctuations in demand.
Security and Backup: Robust application security and backup capabilities ensure that data and content are protected in the event of a disaster.
Collaboration: Simultaneous multipoint global accessibility, enabling collaboration from anywhere in the world.
Recent Challenges in the M&E Sector (As of Mar 2024)
From Kevin Savina’s speech ‘Nouveautés d’AWS pour les médias et nouvelles tendances du marché’: Link
Challenge #1 – Money, Supply, and Energy
The media sector, like many others, has grappled with the adverse effects of current geopolitical and macroeconomic conditions. These conditions have exacerbated the difficulties in accessing essential resources, namely capital, media supply chain resources, and energy. Elevated interest rates and inflation have created significant barriers to investment, dissuading potential investors and hindering growth prospects.
Challenge #2 – Low or Negative Revenue Growth
Media companies, particularly in the Europe, Middle East, and Africa (EMEA) region, have been contending with a concerning trend of either stagnant or negative revenue growth. This predicament has been observed across diverse revenue models including advertising and subscription-based approaches.
Challenge #3 – Cost of Production
The costs associated with content production, which encompass internal production expenses, rights acquisition, and related expenditures, have been steadily escalating. Media enterprises are grappling with an ecosystem where these mounting costs pose a formidable challenge to their profitability and operational sustainability.
Three Key Solutions
In response to these challenges, organizations in the media sector are strategically innovating to maintain competitiveness in an ever-shifting macroeconomic climate. Three key strategies have emerged to address these issues:
Solution #1 – Emphasis on Agility
Strategic investments are being made in technology to enhance agility and swiftly scale infrastructure in response to market fluctuations. Noteworthy investments include virtualization, transitioning from hardware to software-based solutions, and migrating from on-premise infrastructure to cloud-based platforms.
Solution #2 – Data
The pervasive motto of the past year has been “Data, data, and more data.” Media companies have come to recognize the paramount importance of data in optimizing their operations, gaining deeper visibility into their audience, and deriving actionable insights. This data-driven approach is proving instrumental in adapting to evolving market dynamics.
Solution #3 – A Deeper Focus on Customer Experience
Media companies are proactively introducing new services or modernizing existing ones with a pronounced focus on enhancing the customer experience. A particular emphasis has been placed on catering to the preferences and expectations of younger audiences, who represent a crucial demographic in the ever-evolving media landscape.
Addressing Other Common Challenges
Data Security and Privacy
Data security and privacy measures need to be established to safeguard sensitive media content and user information from unauthorized access. A robust combination of encryption, access controls, and authentication protocols can help mitigate these risks.
Performance Issues
Media companies may encounter latency or network disruptions that affect performance. Implementing more robust content delivery networks (CDNs), optimizing network architecture, and utilizing edge computing technologies can minimize these issues, and deliver a smooth viewer experience.
Data Loss
The potential for data loss or corruption is a critical concern that underscores the need for effective backup and disaster recovery strategies. Automated backup solutions, versioning controls, and geo-redundant storage options can minimize the impact of data loss events, ensuring content integrity and availability.
Cloud Costs
The potential cost of operating in the cloud needs to be considered, especially when dealing with large media files and high demand. Adopting resource-efficient architectures, leveraging serverless computing, and optimizing usage through flexible pricing models can help manage costs without compromising quality.
Cloud Solution Management
The scarcity of skilled personnel proficient in managing and maintaining cloud-based solutions can pose challenges during deployment. This can be mitigated with comprehensive training programs, partnering with managed service providers, and utilizing user-friendly cloud management tools to simplify solution management and alleviate skill constraints.
Compliance and Legal
Adhering to industry regulations and standards is often a critical requirement for media cloud deployments. Robust auditing, continuous monitoring, and compliance-specific features can help address regulatory requirements and ensure adherence to legal requirements.
Global Trends
Data and AI/ML: As highlighted in the Recent Challenges section, data, GenAI, and the personalization of customer experiences are at the forefront of industry trends within the M&E sector. Harnessing the power of data-driven insights and GenAI technology, media organizations are optimizing post-production editing and object detection workflows, enhancing both efficiency and creative output. Additionally, customization and personalization of customer experiences are being elevated through AI/ML, enabling tailored content delivery and resonating with audiences on a deeper level, further driving the industry’s growth and evolution.
Hybrid (Cloud + On-Prem): The Hybrid approach is gaining momentum as media companies and creative studios seek to enhance agility and scalability while maintaining robust infrastructure. This strategy combines on-premises resources with cloud-based solutions, allowing organizations to seamlessly adapt to evolving demands and tap into a global talent pool. In the wake of the COVID-19 pandemic, this hybrid model not only facilitates remote work but also provides the flexibility needed to optimize resource allocation and efficiently scale operations.
Sustainability: Sustainability is emerging as a paramount concern within the M&E industry, and cloud-based solutions are at the forefront of this transformative trend. By transitioning to cloud infrastructure, media organizations can significantly reduce their carbon footprints through optimized energy usage and efficient resource allocation. The inherent flexibility of cloud solutions minimizes wastage, contributing to a more sustainable future while maintaining high standards of performance and innovation.
Artificial Intelligence & Machine Learning (AI/ML)
AI/ML technologies are being leveraged at an increasing rate to enhance content creation, recommendation systems, and personalized user experiences. Media enterprises can now analyze extensive datasets to gain valuable insights into audience preferences and behavior, facilitating precise customization of content and recommendations. Furthermore, the predictive capabilities offered by AI/ML are transforming audience engagement, resulting in increased satisfaction and retention in the constantly evolving digital media landscape.
AWS Services for AI/ML Workloads
Amazon Web Services (AWS) provides a range of services to support AI/ML workflows in the M&E industry.
| Use case | AWS Service(s) | Description |
| Generative AI | Amazon Bedrock | Helps build and scale generative AI applications with foundation models. |
Metadata generation | Media2Cloud on AWS | Comprehensive media processing solution that helps automate various aspects of media workflows, including metadata extraction, transcoding, and content delivery. |
| Amazon Rekognition | Image recognition service that leverages deep learning-based models to identify objects, people, and text in images and videos. | |
| Image Labeling | Amazon Rekognition | The Rekognition Custom Labels console offers a fully-managed solution for customized data labeling. |
| Object Detection | Amazon Rekognition | Detects objects in images and videos for applications such as content moderation and facial recognition. |
| Amazon SageMaker Ground Truth | Helps create high-quality labeled datasets for object detection models. Ground Truth also provides a WebUI and API to assist in reviewing false positives and model retraining. | |
| Speech-to-Text Conversion | Amazon Transcribe | Converts audio speech into accurate and time-stamped text, useful for creating transcripts from recordings. |
| Personalized Recommendations | Amazon Personalize | Employs machine learning algorithms to create real-time personalized recommendations, enhancing user engagement. |
| Amazon SageMaker | Provides tools for building, training, and deploying personalized recommendation ML models. | |
| Language Translation | Amazon Translate | Translates text between languages with high accuracy and speed. |
| Sentiment Analysis | Amazon Comprehend | Analyzes text for sentiment and emotion, helping to understand customer opinions and feedback. |
| Amazon SageMaker | Helps train custom sentiment analysis models. | |
| Fraud Detection | Amazon Fraud Detector | Identifies potentially fraudulent activities using machine learning and real-time analytics. |
| Amazon SageMaker | Helps build custom fraud detection models tailored to specific business needs. | |
| Time Series Forecasting | Amazon Forecast | Uses machine learning to generate accurate forecasts for time series data, such as sales and demand patterns. |
| Amazon SageMaker | Helps build and deploy custom time series forecasting models. |
Popular AI/ML Use Cases in M&E
Generative AI
Generative AI, often referred to as GenAI, represents a transformative paradigm shift within the media and entertainment industry. This innovative technology revolutionizes both efficiency and creativity by enabling the rapid generation of diverse types of content including artwork, music, and entire narratives. Leveraging advanced deep learning techniques, GenAI streamlines content creation processes, granting artists and creators newfound creative latitude. This technology has the potential to reshape content generation entirely, ushering in an era of personalized and captivating experiences that deeply resonate with audiences.
Live Streaming
Generative AI can now offer real-time audio content moderation, facilitating seamless adjustments such as replacing profanity with suitable alternatives. While the real-time generation of video to match audio (akin to deep fake technology) remains a complex endeavor, advancements are being announced regularly, and functionality may be available surprisingly soon.
VOD Content
Video on Demand (VOD) Content Enrichment: For VOD content enriched with metadata extracted through Media2cloud on AWS or Amazon Rekognition pipelines, there lies an opportunity to further enhance workflow efficiencies. Utilizing metadata as the input for Language Models (LLM) can yield valuable insights such as sentiment analysis and automatic caption generation. This approach adds substantial value to content curation and accessibility.
Thumbnail Generation and Enhanced Metadata Extraction
The utilization of advanced models like Stable Diffusion or similar techniques may enable the extraction of additional metadata from VOD content, especially in situations where an object detection service such as Amazon Rekognition may face limitations. These cutting-edge models hold the potential to uncover nuanced information that can enhance content discoverability and engagement.
Content Tagging and Metadata Generation
AI/ML algorithms can be employed to automate the process of content tagging and metadata generation. This automation facilitates efficient search, categorization, and management of media assets. By intelligently assessing content characteristics, themes, and context, these algorithms generate rich metadata that helps locate and organize media files with ease.
Text Generation
Text generation and transformation algorithms serve as valuable tools for various applications including reading and writing assistance, script summarization, and narrative generation from diverse data sources such as sports statistics or sketches. The algorithms can also be leveraged to simplify complex text, create engaging stories, or summarize vast amounts of information.
Content Moderation
AI-powered content moderation employs advanced algorithms to swiftly identify and flag harmful or policy-violating content across text, images, audio, and video. The integration of AI significantly enhances the efficiency of content moderation by allowing human reviewers to focus their attention solely on the flagged scenes as opposed to reviewing the entire content.
Personalization
Personalized experiences can be crafted by analyzing user data and behavior. By understanding individual preferences, consumption patterns, and contextual information, content and ads can be dynamically tailored to each user, increasing user engagement and revenues.
Broadcast Monitoring
AI/ML models can assist in detecting brands, logos, and other elements in live content, aiding in proper brand attribution for advertising purposes, while also facilitating the redaction of logos to address copyright and privacy concerns.
Subtitle/Closed Caption Generation
Subtitle and closed caption generation with AI/ML helps improve accessibility in media and entertainment. Advanced algorithms automatically transcribe spoken dialogue and ambient sounds, generating accurate and synchronized subtitles or closed captions. This not only enhances the viewing experience for individuals with hearing impairments but also improves content discoverability and engagement for a broader audience.
Closed Captions generation workflow with Amazon IVS and AWS AI/ML
Live Stats Generation
AI/ML-powered live stats generation can provide real-time insights to both viewers and broadcasters. Utilizing data from various sensors and sources, AI/ML algorithms can deliver up-to-the-minute statistics such as player performance metrics, scores, and game analyses. This enriches the audience’s engagement, offering a deeper understanding of the action while enabling broadcasters to deliver dynamic and informative coverage.
Large Language Model (LLM) Creation
AI models in the Media and Entertainment sector can be customized by training them with specific customer data, improving context-aware language processing. Additionally, “Gen AI” technology enables the creation of entirely new LLMs from legacy archives and content libraries, enhancing content understanding and efficiency. This customization of language models offers a strategic advantage, promoting greater productivity and creativity in content management.
Best Practices for Building and Deploying AI/ML Applications on AWS
Data Preparation and Preprocessing: Clean and well-prepared data is crucial for training accurate ML models. Data should be properly labeled, structured, and representative of the target population to ensure reliable results.
Model Training and Evaluation: ML models should be trained using appropriate algorithms and techniques. It is important to regularly evaluate and validate the performance of the models against relevant metrics to ensure accuracy and effectiveness.
Deployment and Monitoring: ML models should be deployed in a scalable and reliable infrastructure. Continuous monitoring of the model performance and regular updates or retraining are essential to ensure ongoing accuracy and effectiveness.
Machine Learning Ops: Amazon SageMaker MLOps capabilities streamline the end-to-end machine learning lifecycle, allowing for automated and standardized processes, thereby enhancing productivity while ensuring model performance in production.
Solution Example: Media2Cloud Implementation for Jukin Media
Jukin Media utilized Media2Cloud to automatically extract metadata from their vast library, unlocking advanced search and enhanced content discovery capabilities. By integrating Media2Cloud into their content workflows, Jukin significantly reduced manual effort and improved efficiency in managing metadata for their media assets. The benefits realized by Jukin include the following:
- Time and cost savings: Automation reduced manual effort and helped Jukin save time and resources required for metadata generation.
- Enhanced accuracy and quality: The AI/ML capabilities offered by AWS services improved metadata accuracy and ensured high-quality information extraction.
- Scalability and efficiency: The intrinsic scalability offered by AWS services enabled Jukin to efficiently process large volumes of media files.
- Improved searchability of media assets: Automated metadata generation enhanced search and discovery capabilities, enabling rapid discovery of relevant content.
Data Analytics
The rapid transformations observed in the M&E industry are intrinsically linked to the influence of data. With the influx of information from diverse sources, M&E companies are now able to leverage advanced data analytics solutions to gain deeper insights into audience behaviors, content performance, and industry trends. These insights have helped optimize workflows, enhance content relevance, and provide personalized experiences.
AWS provides an ideal ecosystem that fosters the pursuit of data-driven excellence by facilitating the creation of scalable data analytics solutions such as Data Lakes and Data Warehouses for content.
Data Analytics – M&E Use Cases
| OTT Insights | ||
| User Behavior Analysis | Personalized Recommendations | Churn Prediction |
| User interaction tracking Session analysis Viewership patterns A/B testing capabilities | User profiling Content-based filtering Contextual recommendations Real-time recommendation updates | User engagement analysis Subscription cycle tracking Predictive modeling |
| Ad Performance | Content Performance | Quality of Experience (QoE) Metrics |
| Ad Viewability Tracking Click-Through Rate Analysis Ad Placement Optimization Ad Impact on User Experience Conversion Tracking | Viewership Metrics Engagement Metrics User Retention Analysis Trend Analysis Content ROI Analysis | Streaming Quality Analysis Load Time Monitoring Device Compatibility Testing Real-Time Performance Monitoring |
| Social Media Sentiment Analysis | Fraud Detection | Demographics and Geographic Insights |
| Sentiment Score Calculation Trend Analysis Influencer Engagement Analysis Topic Modeling | Account Sharing Detection Unusual Behavior Analysis Payment Anomaly Detection IP Address Monitoring | User Demographic Profiling Geographic Distribution Analysis Content Preference Analysis Marketing Effectiveness Measurement |
| Post Production Insights | |
| Project Completion Time | Quality Control Metrics |
| Project Duration Tracking Milestone Analysis Trend Analysis Client Satisfaction Metrics Cost Management | Error Rate Tracking Rework Frequency Client Feedback Analysis Quality Score Quality Trend Analysis |
| Workflow Efficiency Metrics | Cost Metrics |
| Process Time Tracking Bottleneck Identification Resource Allocation Optimization Process Improvement Tracking Comparative Analysis | Project Cost Tracking Phase-wise Cost Breakdown Cost Trend Analysis Resource Cost Analysis Profitability Analysis |
| Resource Utilization Metrics | Asset Management Metrics |
| Personnel Utilization Tracking Equipment Usage Monitoring Resource Allocation Efficiency Project-wise Resource Utilization Utilization Trend Analysis | Asset Utilization Tracking Asset Lifespan Monitoring Maintenance Schedule Compliance Asset Value Assessment Asset-related Cost Tracking |
| Broadcaster Insights | |
| Audience Ratings | Advertising Revenue |
| Total Viewership Share Rating Points (GRP/TRP) Average Audience Duration Peak Viewership Time | Revenue by Advertiser Revenue by Program or Time Slot Revenue by Ad Type Trends in Advertising Revenue Advertising Fill Rate Cost per Thousand (CPM) Rates |
| Audience Demographics | Program Performance |
| Age and Gender Metrics Geographic Location Income Level Education Level Interests and Preferences Viewing Habits | Audience Ratings per Program Demographics per Program Advertising Revenue per program Social Media Engagement per program Cost per Program Content Consumption Metrics per program |
| Content Consumption Metrics | |
| Average Viewing Time Completion Rate Rewatch Rate | Peak Viewing Times Device Usage Viewing Frequency |
Data Lake vs. Data Warehouse for Media Content
AWS currently offers two options for effectively storing, managing, and analyzing vast amounts of data. The two centralized data repository solutions are Data Lake and Data Warehouse. These platforms provide distinct advantages that cater to different analytical needs, allowing for the seamless management of data ecosystems and the generation of insights.
Data lakes provide a scalable and flexible solution for storing vast and varied content datasets in their raw form. On the other hand, data warehouses offer a structured and organized repository that facilitates quick querying and analysis of structured data. A thorough comparison of both solutions can be found in the table below:
| Data Lake | Data Warehouse | |
| Data | Stores data that may or may not be curated. | Stores highly-curated data that serves as the central version of the truth. |
| Schema | Schema-on-read, i.e. the schema is written at the time of analysis | Schema-on-write, i.e. the schema is defined prior to the implementation |
| Scalability | Highly scalable. Can handle large volumes of structured and unstructured data with ease. | Scalable. However, may require more resources and planning for handling large amounts of data. |
| Insights | Suitable for raw data exploration and big data analytics. May require additional data processing to gain insights. | Designed for generating business insights through structured, processed data. |
| Costs | Lower-cost storage in Amazon S3. However, the cost per query can be higher for larger volumes of data. | Higher-cost storage in Amazon Redshift, AWS’s data warehouse. However, the cost per query is lower. |
| Adaptability | Highly adaptable and versatile, able to store and process new data types without changes to the schema. Cost-effective when adding data science processes. | Less adaptable to unstructured or rapidly changing data types. |
| Performance | Can easily handle large volumes of data. Could be slower for structured data queries when compared to a Data Warehouse. | Optimized for high performance in querying structured data. Is less efficient when handling unstructured data. |
| Maintenance | Lower maintenance as it does not require a predefined schema. However, may require more effort in data organization and management. | Higher maintenance due to schema requirements. However, structured data makes it easier to manage. |
Content/Data Lake Solution Architecture
Data Lake Architecture Diagram
Content Lake Pipeline
Data Ingestion: Data ingestion into the data lake is orchestrated using AWS services tailored to the data type. Amazon Kinesis efficiently handles real-time streams or IoT events. AWS AppFlow can be used to seamlessly integrate SaaS applications. For application databases, AWS Database Migration Service (DMS) ensures robust data capture, and AWS Lambda enables customized integration channels.
Data Cleaning: An AWS Glue job is utilized to clean and reformat the raw data, improving its quality and making it easier for subsequent analysis. The cleaned data is stored in a secondary S3 bucket (curated zone). AWS Glue Crawlers catalog the data in the curated zone as well.
Data Transformation: An additional Glue job is employed to further process the data for business insights by aggregating and transforming it. The transformed data is then stored in a third S3 bucket (production zone). AWS Glue Crawlers catalog the data in the production zone, ensuring efficient organization and management.
Data Visualization and Querying: Processed data can be queried using Amazon Athena and visualized using BI tools such as Amazon QuickSight, Tableau, and Looker for further analysis and decision-making.
Additional Features
Data Archiving: The data in the first S3 bucket (landing zone) can be archived in Amazon S3 Glacier for long-term, cost-effective storage
Access Control and Management: AWS Lake Formation is responsible for managing data transformations and permissions, ensuring a secure and organized data lake.
Data Catalog: AWS Glue Crawlers are used to catalog the data in each bucket (landing, curated, and production), which is important for efficiently organizing and managing the data.
Artificial Intelligence and Machine Learning: AWS offers a range of powerful machine learning tools designed to uncover valuable insights from data. These tools can be leveraged to perform specific tasks such as pulling text from images or PDFs, extracting captions from videos or audio, and detecting objects in videos and images. AI/ML services can also be harnessed to create content such as forecasts and personalized user recommendations.
Data Warehouse Solution Architecture
Data Warehouse Architecture Diagram
Data Warehouse Pipeline
- Data Ingestion: Data ingestion into the data warehouse is orchestrated using AWS services tailored to the data type. Amazon Kinesis efficiently handles real-time streams or IoT events. AWS AppFlow can be used to seamlessly integrate SaaS applications. For application databases, AWS Database Migration Service (DMS) ensures robust data capture, and AWS Lambda enables customized integration channels.
- Data Storage: The retrieved data is stored in an Amazon Redshift database.
- Data Visualization: Amazon QuickSight queries the Redshift database and creates visualizations based on the data.
Solution: TrackIt Data Insights
Data Insights is a comprehensive data analytics offering that incorporates the implementation of a data lake or warehouse for advanced business insights. Interactive dashboards, visualizations, and reports derived from the data lake or warehouse provide insights to assist in enhanced decision making.
| Data Lake | Data Warehouse |
 Configuration of Amazon S3 for data storage, TTL, and versioning Configuration of AWS Glue of data cataloging and ETL Integrating AWS Athena for serverless querying Adding AWS EMR for big data processing (optional) Ingesting and configuring the data lake/warehouse Extracting, transforming, and loading data with AWS Glue Managing the data catalog and metadata Setting up data governance and access control per best practices Analyzing and visualizing data |  Configuration of Amazon Redshift for data storage and querying Configuration of data structure, tables, and schema Ingesting the data in the data warehouse Setting up data governance and access control per best practices Analyzing and visualizing data |
Dashboard Examples
Data Collaboration: AWS Clean Rooms
AWS Clean Rooms is a service designed to streamline collaborative data analysis while preserving data security and privacy within the AWS environment. Clean rooms can effortlessly be created to analyze collective datasets without the need to share or duplicate underlying data. Through the AWS Management Console or API, customers can invite collaborating AWS partners, select specific datasets, and establish custom restrictions for participants. AWS Clean Rooms offers a suite of privacy-enhancing controls, including query management, output restrictions, and query logging, along with advanced cryptographic tools to ensure data remains encrypted even during query processing, ensuring compliance with stringent data-handling policies.
Media companies can leverage this service to securely collaborate with partners and industry peers, facilitating joint analysis of vast datasets while maintaining data confidentiality.
Source: AWS
Other Data Solutions
ActionIQ CX Hub: Technology that helps brands explore and act on customer data, while enabling technical teams to extend and enhance existing tech investments to manage data governance, costs, and performance.
Datazoom: Platform that addresses the crucial need for observability and optimization in streaming video deployment. Datazoom collects and normalizes data from various endpoints, including video players and CDNs, providing standardized data definitions and routing capabilities through Connectors, facilitating the measurement of end-user Quality of Experience (QoE) and enabling organizations to understand and improve their video streaming workflows.
Working with an AWS M&E Partner Integrator
AWS M&E partner integrators can assist in navigating the various challenges associated with the implementation and maintenance of cloud-based media workflows on AWS.
With deep expertise in AWS, M&E partners not only offer guidance on best practices and migration strategies but also provide comprehensive managed services including monitoring, support, and optimization, allowing media companies to focus on their core business while outsourcing the technicalities of cloud management to experts.
M&E partners also play a crucial role in facilitating the relationship between AWS and end-users. Partners have extensive knowledge of AWS-based media workflows and can help clients identify funding opportunities provided by AWS. These funding programs help offset the costs associated with migrating to the cloud, making it more accessible for companies operating with stringent budgets.
Process of Engagement
The basic process of engagement typically involves an initial consultation to understand client needs and objectives, followed by a design phase to develop a customized solution. Implementation and testing are then carried out, followed by ongoing support and maintenance to ensure that solutions remain up-to-date and effective.
CloudWise – AWS Managed Services
CloudWise, TrackIt’s AWS Managed Services offering includes a suite of services such as monitoring, optimization, and support, enabling companies to manage their AWS infrastructure with ease and efficiency. CloudWise allows customers to stay focused on their core business while TrackIt experts handle all the technicalities of cloud infrastructure management.
Built on in-house custom monitoring software, the offering includes real-time monitoring, customized dashboards, monthly cost analysis and coverage reports, annual architecture reviews, and quarterly security assessments. Customers also benefit from 24/7/365 global support from AWS-certified TrackIt engineers working to ensure that their cloud investments are optimized to their full potential.
Managed Services Offering Provided by AWS M&E Partner TrackIt
About TrackIt
TrackIt is an Amazon Web Services Advanced Tier Services Partner specializing in cloud management, consulting, and software development solutions based in Marina del Rey, CA.
TrackIt specializes in Modern Software Development, DevOps, Infrastructure-As-Code, Serverless, CI/CD, and Containerization with specialized expertise in Media & Entertainment workflows including AWS Studio in the Cloud (SIC), Retail workflows, High-Performance Computing environments, and data storage.
In addition to providing cloud management, consulting, and modern software development services, TrackIt also provides an open-source AWS cost management tool that allows users to optimize their costs and resources on AWS.